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SAS Risk Modeling and Decisioning The New Age of Risk Analytics


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Last Update: March, 2019


Risk Management is a Core Strength and Top Focus Area

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Introduction to Risk Management


What is Risk?


What is Risk?

• Risk is the potential for uncontrolled loss of something of value. Values (such as physical health, social status, emotional well-being, or financial wealth) can be gained or lost when taking risk resulting from a given action or inaction, foreseen or unforeseen (planned or not planned). Risk can also be defined as the intentional interaction with uncertainty. Uncertainty is a potential, unpredictable, and uncontrollable outcome; risk is an aspect of action taken in spite of uncertainty. Wikipedia

https://en.wikipedia.org/wiki/Physical_health

https://en.wikipedia.org/wiki/Social_status

https://en.wikipedia.org/wiki/Uncertainty


Risk Types


Financial risks


Not all risks are equally quantifiable

Reputation

(Franchise) Existing

Business

Operationa

l Risk

Funding

LiquidityALM

Non

TradingTrading

Credit /

Country

Risk

Business Risk Market Risk

New

Business

Nature of Risk Inherent

Governance

structure and

internal

control

Complimentary

Controls

Quantifiable

risk?

Due diligence

process

Business

Management

Process

Governance

structure and

internal

control

Governance

structure and

internal

control

Governance

structure and

internal

control

Governance

structure and

internal

control

Governance

structure and

internal

control

Governance

structure and

internal

control

Indirect losses

arising from

other risk types,

or opportunity

costs rather

actual losses

With

strong

assumptions

With

strong

assumptions

Data

completeness /

adequacy issue

Market

Standard

exists

Market

Standard

exists

Market

Standard

exists

Market

Standard

exists

Market

Standard

exists

Risk Taking Inherent InherentRisk

minimizationRisk Taking Risk Taking Risk Taking Risk Taking

Not Quantifiable Quantifiable


Credit Risk – Overview

• Credit risk refers to the risk that a borrower will fail to meet their contractual

obligations and hence, will not be able to repay their loan

• Credit risk arises whenever a bank is expecting to use future cash flow to

pay a current debt

• In order to not jeopardise the financial situation of the bank, bank needs to

have risk management function and whenever a borrower defaults, the bank

is required to set aside capital to handle unexpected credit losses

• Institutions use permitted by supervisors approaches to quantify their capital

requirement

What is Credit Risk?


Credit risk in TelecommunicationsTelco’s offer banking products


Credit risk in TelecommunicationsTelco – banking partnerships


Risk Management

Risk management is the identification, evaluation, and prioritization of risks (defined in ISO 31000 as the

effect of uncertainty on objectives) followed by coordinated and economical application of resources to

minimize, monitor, and control the probability or impact of unfortunate events or to maximize the realization of opportunities. Wikipedia

https://en.wikipedia.org/wiki/Risk

https://en.wikipedia.org/wiki/ISO_31000


Corporate governance principles for banks


Corporate governance principles for banks


Risk Culture

Top Management

Front Office / Sales

Risk Management

Risk Control

Personal Values and

Goals

Know-how and

Expertise

MindsetAnd

Attitude

Rulesand

Approach

Risk Culture


Three lines of defense

Chartis Research – Model Risk Management solutions 2014


Independence of the control functionsEBA guidelines on internal

governance (GL44)

Definition of an independent control function (GL44: Section D, §24-6)

In order for the control function to be regarded as independent, the following conditions should be met:

a. its staff does not perform any tasks that fall within the scope of the activities the control function is

intended to monitor and control

b. the control function is organisationally separate from the activities it is assigned to monitor and

control;

c. the head of the control function is subordinate to a person who has no responsibility for managing

the activities the control function monitors and controls. The head of the control function generally

should report directly to the management body and any relevant committees and should regularly

attend their meetings; and

d. the remuneration of the control function’s staff should not be linked to the performance of the

activities the control function monitors and controls, and not otherwise likely to compromise their

objectivity

Independence of the control functions are the backbone of the 3LOD structure


3LoD model Risk management related roles and responsibilities

Business

RB, WB, WM, TresuaryRisk management (CRO) GC

Board of Directors & BRC

CEO, EM, Senior management

Compliance

(CCO)

Internal Audit

(IA Head)

Pro

ce

ss

es

Data

& IT

Sys

tem

s

Ind

ep

ende

nt a

ud

it b

y a

pp

lyin

g IR

B a

ud

it s

tra

tegy

Va

lida

tion

of risk m

od

els

ind

ep

end

en

t fr

om

CR

CU

s

Ind

ep

ende

nt co

mp

liance m

on

itoring

• Develop risk models and

frameworks, incl. IRB

• Use risk models for business and

capital purposes

• Responsible for internal control

• Owns and manages risks

• Adhere to frameworks & models

developed by RM

• External reporting on capital &

risk exposures

• Monitor execution of risk

management by 1st LOD

• Monitor performance

• Risk control of all material risks &

capital management processes

• Set / approve risk policies

• Review and test 1 LOD reporting

• Independent risk reporting

• Responsible for business data

and IT Systems

• Responsible for data quality

controls

• Set IRB related data requirements to

business

• Risk control of data quality and IT

systems

1st LoD 2nd LoD 3rd LoD


Introduction to Credit Risk Management


Credit Risk Modeling and Decisioning Few words about basics

300 $

Principal 200 $

Principal 150-180 $

305 $

Capital

Provisions

What can be used

Models are used for calculation

Models are used to minimize


Economic Capital Stress

Frequency of Loss

Amountof Loss

Expected Loss Unexpected Loss

Expected Loss / Unexpected Loss / Stress

Provisions


Loan Pricing


Credit Risk Modeling and Decisioning

APPLICATION SCORE

APPLICANTS %

20

15

10

5

0

0 100 200 300 400

Bads Goods

Trade Off

Poor Quality ,

High VolumeHigh Quality ,

Low Volume

1 : 1

10 :1

25 :1

50 :1

100 :1

Score

600 750 850 900 1000

Decline Refer AcceptDecline Refer Accept

High Interest Guarantor Increase LoanHigh Interest Guarantor Increase Loan

Deposit/

Autopay

Limited

Terms

Cross

Selling

Deposit/

Autopay

Limited

Terms

Cross

Selling


After the financial markets crisis: The flood of regulations overruns all areas of banking


Regulations goes cross function and cross category, but can be categorized in a number of key areas


…and it is all linked together

Capital

Adequacy

Own Funds

Capital

Planning

ICAAP/

ILAAP

Capital

and

Liquidity

Reporting

Credit

Risk

Operati

onal

Risk

Market

Risk

Liquidity

RiskCounter

party

Credit

Risk

Large

Exposu

res

Leverage

Ratio

GSIB

Reporting

Minimum

requirements

Risk

Reporting

Financial

Reporting

Disclosure

Tax

Reporting

Statistical

Reporting

Capital

Joint

Decision/

SREP

Data

Management/

RDARRInternal

Governance

Accounting

Taxes

Anti-money

Laundering

Customer

Protection

Supervisory

reporting

Securities

and Asset

Management

External

Credit

Assessment

Capital

Planning

ICAAP/

ILAAP

Securities

and covered

bonds

Market

Infrastructure

Remuneration

Passporting

and

supervision

of branches

Recovery

and

Resolution

Banking

Union and

SSM


Regulators

Global regulation

European regulation

National Regulation

• Financial Stability Board - FSB

• Basel Committee on Banking Supervision - BCBS

• Bank for International Settlement – BIS

• Group of Central Bank Governors an Heads of Supervision- GHOS

• European Commission

• European Parliament

• European System of Financial Supervision

• European Central Bank

• Finansinspektionen

• Monetary Authority of Singapore (MAS)

• HKMA

• National Central Bank


Regulatory requirementsIAS 39 provisioning


A New Standard for Financial Accounting - provisionsIFRS 9 Overview

• Global standard

• Effective 2018in

over 116 countries

• Replaces IAS 39 (incurred loss approach)

Classification and

Measurement

Defines valuation approaches to be applied across balance sheet

Hedge Accounting

Replaces the rules-based approaches with a greater focus on risk management

Impairment Calculations

Moves from incurred loss to expected credit loss approach

Anticipated to raise overall reserve levels by ~ 35%


A New Standard for Financial Accounting - provisionsContext


Basel Committee• The Basel Committee on Banking Supervision provides a forum for regular cooperation on banking

supervisory matters. Its objective is to enhance understanding of key supervisory issues and improve the quality of banking supervision worldwide.

• The Basel Committee is the primary global standard-setter for the prudential regulation of banksand provides a forum for cooperation on banking supervisory matters. Its mandate is to strengthen the regulation, supervision and practices of banks worldwide with the purpose of enhancing financial stability.

• The Committee's members come from Argentina, Australia, Belgium, Brazil, Canada, China, France, Germany, Hong Kong SAR, India, Indonesia, Italy, Japan, Korea, Luxembourg, Mexico, the Netherlands, Russia, Saudi Arabia, Singapore, South Africa, Spain, Sweden, Switzerland, Turkey, the United Kingdom and the United States.

Basel Timeline

International

Convergence of

Capital

Measurement and

Capital Standards

(Basel I)

1988

Basel II

enters into

force

2007

A revised

framework is

published (Basel

II)

2004

Market risk

amendment

to the Capital

Accord

1996

International

framework for

liquidity risk

measurement,

standards and

monitoring (Basel

III)

2010

Extended

accord for

Basel III

2011

Process to

monitor members

implementation of

Basel III

2012

Fundemental

review of trading

book,

2013

Leverage ratio

framework &

disclosure

equirements,

Review of Risk

models under

way

2014…


The Basel Accord – Evolution and Risks covered

Basel I

Liquidity & Leverage

Effective

Credit Risk Loan Book

Credit RiskTrading

Market Risk

Operational Risk

NA

1988

yes, not risk sensitive

no

no

no

Capital Ratio 8%

Pillar 2 and 3 NA

Basel 1.5

1998

NA

unchanged

no

Internal model

no

unchanged

NA

“Basel II.5”

NA

2011

unchanged

Significant changes

changes

unchanged

unchanged

unchanged

“Basel III”

Significant changes

2013-18

changes

as Basel II.5

as Basel II.5

unchanged

Significant changes

Significant changes

Basel II

NA

2005-2008

Risk sensitive, internal rating

yes

unchanged

3 options, internal model

unchanged

Significant changes

“Basel IV”

changes

2018-2022

changes

changes

changes

changes

changes

changes


Posters on regulatory developments


Capital

▪ Going-concern capital: Tier 1 capital➢ Common Equity Tier 1 capital (CET 1 capital): common

shares and retained earnings

➢ Additional Tier 1 capital

to absorb losses; this should allow an institution to continue its activities and help prevent insolvency.

Going-concern capital

▪ Gone-concern capital: Tier 2 capital

would help ensure that depositors and senior creditors can be repaid if the institution fails.

Gone-concern capital


Basel I (1998)

▪ set of minimal capital requirements for banks

▪ enforced by law in the Group of Ten (G-10) countries in 1992.

▪ Basel I primarily focused on credit risk. Assets of banks were classified and grouped in categories according to credit risk, carrying risk weights of zero (for example home country sovereign debt), ten, twenty, fifty, and up to one hundred percent (this category has, as an example, most corporate debt)

▪ Banks with international presence are/were required to hold capital equal to 8 % of the risk-weighted assets

▪ Most other countries, currently numbering over 100, have also adopted, at least in name, the principles prescribed under Basel I the efficiency with which they are enforced varies, even within nations of the Group of Ten


Basel I (1998)

▪ Risk weights

➢ OECD sovereigns: 0%

➢ OECD banks: 20%

➢ Residential mortgages: 50%

➢ Synthetic: 20% super-senior, 0% cash-collateralised mezzanine, deduction or 100% first loss (with national variations)

➢ Unfunded commitments under one year: 0%

➢ Unfunded commitments over one year: 50%

➢ Everything else: 100%

Sample capital calculation

▪ €100 million corporate exposure

▪ 100% risk weight = €100 million risk weighted assets (RWA)

Capital▪ Capital charge = = 8% minimum

RWA

▪ Capital charge: €8 million


Basel II in a Nutshell (2008)

Three Pillars

Minimum capitalrequirements

Supervisory reviewprocess

Market discipline

Risk weightedAssets (RWAs)

Definition ofcapital

Credit riskOperational

riskMarket

risks

Standardised Approach

InternalRatings-based

Approach

BasicIndicatorApproach

StandardisedApproach

AdvancedMeasurement

Approaches

StandardisedApproach

ModelsApproach

CoreCapital

SupplementaryCapital

Capital ratio =Capital

RWAs

= 8.0%


Basel III/IV


Pillar II

ICAAP and Supervisory

audit procedures

Pillar III

Market discipline

Pillar I

Minimum capital

requirements

• Credit risks

• Market risks

• Operational risks

• Economic capital

• Audit of the bank's

risk estimations

• Enlargement of

the reporting

requirements

• Pillar I – Calculation of regulatory capital for three major components of risk.

• Pillar II \ ICAAP – Calculation of economic capital (framework for: systemic risk, concentration risk, strategic risk,

reputational risk, liquidity risk, legal risk, stress-testing and etc.

• IRB – Rules for own estimation of risk parameters (PD, LGD, EAD, M) for the purpose of calculating regulatory capital.

• RWA (REA) – Risk-weighted assets is a bank's assets or off-balance sheet exposures, weighted using risk parameters. Is

used for capital calculation.

• Regulatory capital – the amount of capital a bank has to hold as required by its financial regulator.

• Economic Capital – the amount of risk capital, which a firm requires to cover all the risks that it is running.

The three pillar architecture of Basel II and definitions


Credit

risksMarket

risks

Operational

risksLegal &

compliance

risks

Liquidity

& funding

risks

Credit

risksBusiness/

Strategic /

Reputation

risks

Market

risks

Governance (incl. policies)

Risk reporting (incl. limits, indicators)

Integrated risk management

Integrated capital & liquidity planning

Capital & liquidity planning

(point-in-time)

Capital & liquidity planning

(forward looking)Stress & scenario testing

Stressed capital &

liquidity planning

BoD

Business strategy and Risk Appetite

Operational

risks

Material risk identification

Risk assessment and quantification

Aggregation

Inte

rnal A

udit P

rocess V

alid

atio

n

Regulatory Review & Monitoring

Mark

et

Dis

clo

su

re

Risk modelling (PD, LGD, EAD, M, CCF) & risk control

Foundation – Business Processes and Data

Operational risk management & limit management

Data Governance (including Data Quality)

ICAAP report Process Methodology Models IT systems Legal reporting

REA calculation

Provisions

Legal reporting

ICAAP

IRB


What is ICAAP?

Business

Risk

Risk Management

Capital

Owners / BoD

ICAAP

• Assessment of the risks that the business

generates and the capital needed to cover those

risks

• Including future situation, covering any changed

conditions

• Market stress, Business model,…

ICAAP



Risks covered by ICAAP/SREP – more than just a Pillar 1 risk item

Operational risk

Market Risk

Credit RiskStandardised approach

IRB – Internal credit risk model

VaR & sVaR – price change risk

IRC & CRM – migration & default risk

Standardised approach

Concentration risk

IRRBB

Real Estate Risk

Defined Benefits Pension Plans

Potential add-on

Pillar 1 Pillar 2

Business Risk

Part of stress test,

no approval needed

Liquidity Risk

Risk managed, no capital held

Other risks monitored and managed*

Institutions shall have in place sound, effective and comprehensive strategies and processes to assess and

maintain on an ongoing basis the amounts, types and distribution of internal capital that they consider adequate to

cover the nature and level of the risks to which they are or might be exposed


Regulatory versus Economic CapitalEconomic capital can be viewed as the marginal contribution to the risk of not meeting minimum regulatory requirements


Today, Banks use Models of Economic Capital to varying Degrees


Risk limitation is a significant measure for ensuring risk bearing capacity




Minimum Regulatory Capital Requirements

Banks must hold a certain level of capital to its total Risk Exposure Amount (REA)

Regulatory Capital Base (Core Tier 1 + Tier 1 + Tier 2)

Total Risk Exposure Amount (REA)


Credit Risk - Overview of REA

Scali

ng

factor

Expected

lossDownturn default threshold

Conditional PD

( )( ) 5.1206.15.21)5.11()999.0(1

)()1(* 1

5.0

5.0 −+−

−

−+−= −− bMbLGDPDG

R

RPDGRNLGDEADREA

Minimu

m

required

capital

ratio

Maturity adjustment

Unexpected loss

As seen in the

RW- function,

it depends on

LGD, PD

Maturity. The

parameter R is

the correlation

factor which is

dependent on

size and PD = Internally estimate for

FIRB= Internally estimate for

AIRB

The REA function (FIRB/AIRB)


Credit Risk Can Be Calculated Using Different Approaches

Standardised Approach

IRB Foundation

IRB Advanced

• Risk weights (0%-150%) is applied to different counterparts

➢ Depending on exposure class, external rating and maturity

• Risk can be mitigated using

➢ Guarantees

➢ Credit derivatives

➢ Netting

➢ Some financial collateral

Approach Comments

• Risk exposure amount is a function of PD, LGD, M and exposure (EAD)

• Dependent on exposure class and size of counterpart (FIRB not relevant for

Retail portfolio)

• Institutions that are permitted to use foundation IRB will use their own estimates

of the risk parameter PD, but using Supervisory estimates for estimating LGD

and EAD

• Institutions that are permitted to use advanced IRB will use their own estimates

for all risk parameters PD, LGD, M and exposure (EAD)

• For the Retail portfolio (household and small business) only advanced IRB

approach is available

• To be permitted to use IRB Approach more than 100 minimum requirements

must be fulfilled ( e.g. validation of internal risk model, use test, corporate

governance etc.)


• Yearly validation of rating/scoring and risk parameters

• Predictive power (out-of-time and out-of-sample data)

• Traffic lights etc….

• Treatment of past due

• Reclassification of default

• Collateral Management/valuation

• 90 days past due

• Internal lending standards

• Annual re-evalution rating

• Overrides

• Use test

• Etc….

Exposure classes

Risk weights

Rating/

scoring methodology

PD/LGD/

CCF Methodology

Validation

Credit Process

Corporate governance

IT Infrastructure

• Sectorcodes

• Group of connected clients

• Borderline

• Specialised Lending

• Sovereign eligibililty

• etc• Size factor

• Financial data

• SME Reduction

• Maturity

• Asset value correlation

• Product type (Retail)

• Eligible credit risk mitigation

• Etc….

• Rating/scoring methodology

• Rating scale

• Each legal entity shall be rated

• Risk transfer

• Appropriate risk differentiation

• Documentation/instructions/

guidelines

• Definition of default/loss

• Long run average default

• Quality and length of

underlying data

• Explanatory factors

• Representative population

• accurate and robust estimates

• Collection process/data

• Etc…

• Independent credit risk control

function

• Education CEO, Board

• Management Reporting

• Internal Audit

• Etc…

• Robus systems

• Audit trail

• Rating- scoring data bases

• PE data bases

• CAD Reporting systems

• Etc….

Regulatory requirements for IRB Approach

To be permitted to use

IRB Approach more than

100 requirements must

be fulfilled ( e.g.

validation of internal risk

model, system reliability,

use test, corporate

governance etc.)


Methodology for Supervisors assessing Institutions on IRB ApproachKey focus points:

• Sound data quality

• IT systems used are safe, secure and reliable and the IT infrastructure is sufficiently robust

• The institution has to provide detailed documentation on the design and operational details of the rating

systems (RTS specify the minimum content of such documentation)

• Sound model validation and monitoring system in place (all rating systems should be equally verified

regardless whether they were built internally by the institution or obtained from third party vendor, including

understanding/ownership of calculations)

• IRB Approach goes beyond internal models and technical calculation of the own funds requirements, it defines

also the internal governance, including corporate culture and management of the institution

• The same data and parameters are used in the calculation of the own funds requirements, internal risk

management and decision making processes

• Collateral management, independence of the assignment of exposures to grades or pools, treatment of

multiple defaults and sufficient margin of conservatism

• Attention is drawn also to the application of human judgement at various stages of the development and use

of rating systems


IRB Credit Risk – Overview of Risk weights

1) Input to the calculation of RWA, Economic Capital and EL calculations

2) Input to the calculation of economic profit and capital base

PD (%)Probability of default =The likelihood that a customer will default

❑ Differentiated via rating/scoring

Correlation Factor R (1;-1)=The degree to how interlinked the default is to other exposures

❑ Differentiated by exposure class (retail)and size of customer (co&inst)

LGD (%)=Loss Given DefaultHow much of the exposure the bank expect to loose

❑ Differentiated by collaterals and industry type

Effective Maturity M (Years)=

The maximum remaining contractual maturity of the exposure

❑ FIRB - Standardised value for with default value of 2,5 years

❑ AIRB - Differentiated by exposure class and exposure size

Risk Weight Parameters

Other Parameter Connections

Expected Loss is compared to provisions

❑ Excess provisions are set to zero

❑ Shortfall provisions are deducted from CET1

PD (%) LGD (%)EAD(€) EL (€ )X X =

1) 2)


Introduction

• Exposure classes are the basis for the calculation and reporting of capital

requirements.

• The principles for the calculation of minimum capital requirements for credit risk

in the Internal Rating Based (IRB) approach differ between the exposure

classes.

• It is important that the principles and processes for assigning exposure classes

are appropriate and consistent over time and across business units.

Each exposure shall be assigned to an exposure class

❑ Sovereigns

❑ Institutions

❑ Corporate (sub-classes)

❑ Retail (sub-classes)

❑ Equity

❑ Securitisation

❑ Other non obligation assets

Exposure Classes


EAD and CCF

What is Exposure?

• Exposure is the monetary amount at risk in the event of a

default

• Exposure can be divided into;

➢ Utilised exposure at the time of measurement→ Accounted for

on the on-balance sheet

➢ Unutilised exposure at the time of measurement→ Accounted

for on the off-balance sheet

• Unutilised exposure = Current limit of exposure – Utilised

exposure

What is Exposure at Default?

• The exposure at default (EAD) refers to the expected

exposure amount at the time of default

• EAD = current utilised exposure at the time of measurement

+ An estimation of how much more of the unutilised limit that

will be used at the time of default


EAD and CCF

What is the Credit Conversion Factor?

• When estimating how much of the unutilised exposure that will be used at the time of default, the

Credit Conversion Factor (CCF) is used to convert the unutilised amount to expected utilised

exposure.

• Since institutions are only required to hold capital for commitments that they have currently taken on,

CF’s shall be estimated for current commitments.

• The CF is a percentage measurement of how much of the undrawn credit line that will be used

between the time of measurement and the time of default → shall be set zero or higher.

• Since unutilised exposure can be found on the off-balance sheet the EAD formula can then be

expressed as below.

• In the SA and FIRB approach, the CCF´s are standardised and differentiated by exposure categories.

• In the AIRB and RIRB approach, an institution can use own CCF’s if permitted by the authorities.


EAD and CCFSA and FIRB: Categories

1. Unconditionally cancellable credit lines and revolving

purchased receivables → CCF = 0%

2. Short term letter of credit arising from the movement of

goods → CCF = 20%

3. Other credit lines, note issuance facilities and revolving

underwriting facilities → CCF = 75%

4. Other off-balance sheet exposures with full risk → CCF

= 100%

5. Other off-balance sheet exposures with medium-risk →

CCF = 50%

6. Other off-balance sheet exposures with medium/low-

risk → CCF = 20%

7. Other off-balance sheet exposures with low-risk →

CCF = 0%

Example

Full Risk:

❑ Guarantees having the character of credit substitute

❑ Credit derivatives

❑ Acceptances

❑ Asset sale and repurchase agreements

Medium risk

❑ guarantees not having the character of credit substitutes

❑ Warranties and indemnities

❑ Undrawn credit facilities with an original maturity of more

than one year

❑ Note issuance facilities (NIFs) and revolving underwriting

facilities (RUFs)

Medium/Low

❑ Documentary credits in which underlying shipment acts as

collateral and other self liquidating transactions

❑ Undrawn credit facilities with an original maturity of up to

and including one year

Low

❑ Undrawn credit facilities with certain cancellation features

AIRB and RIRB

◼ Own estimates of CCF after approval from Supervisor


Credit Risk MitigationWhat is Credit Risk Mitigation?

• CRM means “a technique used by a credit institution to reduce the credit risk associated with

an exposure or exposures which the credit institution continues to hold”.

• REA, Expected loss (EL) and large exposures (LE) can be reduced by the recognition of CRM.

• CRM can only be used if REA or EL is reduced.

CRM

Unsecured

exposure

Secured

exposure

Unprotected part

Protected part

• In SA, the protection may

directly affect either the

exposure or the risk weight.

• In IRB, the protection may

directly affect either LGD or

PD.


Credit Risk Mitigation

Common Requirements for CRM Tools

❑ The credit institution must satisfy the regulator

that it has adequate risk management

processes to control those risks to which the

credit institution may be exposed as a result of

carrying out a credit risk mitigation practice.

❑ The credit protection arrangement is legally

binding in all relevant jurisdictions.

❑ In case of funded credit protection, the assets

relied upon should be sufficiently liquid and their

value stable over time.

❑ In the case of default, insolvency or bankruptcy

the institution should have the right to liquidate

or retain the asset in a timely fashion.

❑ The degree of correlation between the value of

the assets relied upon and the credit quality of

the obligor should not be unduly.

❑ The specific minimum requirements for the

CRM tool must be fulfilled.

Requirements for CRM Tools

• There are three types of requirements associated with CRM tools:

➢ Eligibility requirements: in general, collateral valuation guideline addresses the

eligibility criteria and the quantitative requirements

➢ Minimum requirements: are checked in the credit process

➢ Common requirements: applies to all CRM tools


Overview of Eligible CRM Tools

Additional CRM Tools in FIRB

UnfundedFunded

Eligible CRM Tools in All Approaches

❑ Financial Collaterals❑ Cash as security

❑ Equities or convertible bonds in main index

❑ Gold

❑ Securitisation positions

❑ Collective Investment Undertaking’s (CIUs)

❑ Debt securities

❑ On-balance sheet netting

❑ Master netting agreements

❑ Guarantees stemming from institutions,

insurance and reinsurance undertakings and

export credit agencies

❑ Immovable property collateral

❑ Receivables

❑ Other physical collaterals

❑ Leasing

❑ Guarantees❑ Guarantees that receives 0% risk weight in SA

❑ Central Counterparties - CCPs

❑ Public Sector Entities - PSE’s (under certain

circumstances)

❑ Institutions and other corporate entities (under

certain circumstances)

❑ Credit derivatives❑ Credit default swaps

❑ Total return swaps

❑ Credit linked notes to the extent of their cash

funding

Additional CRM Tools in AIRB/RIRB

❑ Internally estimated and approved LGD pools





LGD - Loss given default

• LGD measures the amount of discounted nominal loss including costs caused by the default

of a customer

• LGD is dependent on the type of collateral and borrower

LGDEAD – Recovery position + Cost of carry

EAD= =

EAD

Net loss

Exposure at

DefaultRecoveries and

Payments

Loss of Principal Cost of Carry Net Loss

Euro

+ ==-


LGD - Loss given defaultWays of measuring LGD

• Market LGD

• Implied Market LGD

• Implied historical LGD (LGD = RL/PD)

• Workout LGD

LGD Estimation Approaches

• Segmentation

• Expert based (Table lookup, Historical averages)

• Decision Trees

• Regression models

• Single equation (Beta transformation)

• Component based approach

• Two stage models

• Survival analysis (Zhang, Thomas 2009)

• Generalised Additive Neural Networks

• Simulation


LGD - Loss given default

• The LGD for AIRB consists of four different variables when determining the LGD value;

➢ Secured/unsecured exposure

➢ Collateral type

➢ Loan-to-value (LTV)

➢ Industry code

• The LGD model for RIRB consists of five different variables when determine the LGD pool

and LGD value for the exposure;

➢ CAD reporting country

➢ Collateral type

➢ Customer type

➢ Secured/unsecured

➢ Product code

Comments

RIRB

Approach

AIRB

• For the portfolios using Foundation IRB the following LGD estimates should be used

➢ Senior exposures without eligible collateral; 45%

➢ Subordinated exposures without eligible collateral; 75%

➢ Covered bonds may be assigned; 11.25%

➢ Senior purchased corporate receivables where an institution is not able to demonstrate

that its PDs estimates meet the minimum requirements; 45%

➢ Subordinated purchased corporate receivables exposures where an institution cannot

demonstrate that its PD meet the minimum requirements; 100%

➢ Dilution risk of purchased corporate receivables; 75%

FIRB


Effective Maturity (M)

What is Maturity?

• Maturity is defined as the period of time for which a financial instrument remains outstanding.

• Maturity can be calculated in 4 different ways

Original Maturity

• Calculated as End Date/Next cancellation date – Start Date

• Used to set risk weight in SA for some off-balance products, etc

Minimum Residual Maturity

• Calculated as End Date/Next cancellation date – Calculation Date.

Nominal Residual Maturity

• Calculated as End Date – Calculation Date

Effective Maturity

• Standardised Approach: N/A

• Foundation/Retail IRB: Set to 2.5 years (or 0.5 for repos).

• Advanced IRB: set between 1-5 years based on underlying cash flows for the exposure.


Correlation Factor (R)

What is the Correlation Factor?

• The correlation factor R is a measurement of how much a customer’s asset value

correlates with the asset value of other customers

Systematic and Idiosyncratic Factors

• The size of a customer’s earnings is a function of;

➢ Factors that affect all customers’ earnings – Systematic factors

➢ Factors that only affect a particular customer – Idiosyncratic factors

• The more affected a customer’s earnings, and consequently their ability to pay their debts,

is to systematic factors, the bigger the correlation is between their tendency to go into

default to other customers

• Since different kinds of customers are differently dependent of the systematic factors, the

calculation of the correlation factor differs between the exposure classes

Systematic Factors

❑ All systematic factors are treated as one single

factor in the IRB formula

❑ This single factor can be interpreted as the

global state of the economy

Idiosyncratic Factors

❑ Examples of customer specific factors are the

management’s skills and cost efficiency.

❑ Will tend to be cancelled out in a larger group

of customers


Rating: The Fundamental Element of credit risk estimation

Internal Rating

• Own assessment

• Based on own expertise and know-how

• Direct interaction with the customer

External Rating

• Based on assessments by third parties

• Usually large Rating Agencies

•


Internal Rating Scale

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Moody's Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 Caa1 Caa2 Caa3 Ca C D

S&P AAA AA+ AA AA- A+ A A- BBB+ BBB BBB- BB+ BB BB- B+ B B- CCC+ CCC CCC- CC C D

Fitch AAA AA+ AA AA- A+ A A- BBB+ BBB BBB- BB+ BB BB- B+ B B- CCC+ CCC CCC- CC C D

Investment Grade Speculative Default


IRB Credit Risk – PDWhat is Probability of Default?

• Probability of Default (PD) is defined as the likelihood that a customer will go into default

over a one year period.

• The PD is determined by estimating the repayment capacity of the customer.

• In order to use own estimates for PDs for specific portfolios, approval from the

supervisory authorities is required.

Two Approaches and PD Scales

• Two different approaches and PD scales are used to categorise customers into groups

that reflects their probability of default

➢ Scoring approach for the retail customers (RIRB/AIRB)

➢ Rating approach for the corporate and institutional customers (FIRB & AIRB)

Corporate

Retail

Scoring Risk Grade

Rating GradeRating

Customers PD


IRB Credit Risk – PDWhat is the Definition of Default?

• A default is considered to have occured when either or both of the following have taken

place:

➢ The obligor is past due more than 90 days on any material credit obligation to the

institution. 90 days may be replaced by 180 days, subject to the national supervisors*

➢ The obligor is unlikely to pay its obligation to the institution, the parent undertaking or

any subsidiary

• For retail exposures, the institution may apply the default definition on an individual credit

facility rather than the total obligations of a borrower

Due Date

0 18090

Days Past Due

Default Date Default Date*


IRB Credit Risk – PD

The PD Scales

• PD is based on customer specific

rating/scoring grades and reflects the

long term average default frequency

(ADF) in each grade/rating.

• The internal rating (corporates) and risk

(retail) scales consist of 18 grades

➢ Rating grades → A+ to F-

➢ Scoring grades → 6+ to 1-

➢ Defaulted customers are assigned to

one of the 3 grades that goes from 0+

to 0-

• The PD of an exposure shall be at least

0.03% and the PD for an obligor in

default shall be 100%.

Corporate Retail

Rating Grade PD

6+ 0.030%

6 0.034%

6- 0.048%

5+ 0.070%

5 0.104%

5- 0.156%

4+ 0.247%

4 0.353%

4- 0.553%

3+ 0.812%

3 1.247%

3- 2.307%

2+ 6.396%

2 7.060%

2- 9.863%

1+ 14.794%

1 20.712%

1- 26.926%

Unrated 2.500%

Risk Grade PD

A+ 0.080%

A 0.114%

A- 0.158%

B+ 0.220%

B 0.308%

B- 0.429%

C+ 0.597%

C 0.837%

C- 1.169%

D+ 1.638%

D 2.297%

D- 3.196%

E+ 4.472%

E 6.300%

E- 8.792%

F+ 12.279%

F 17.190%

F- 24.036%

Unrated 2.000%


Product DevelopmentCustomer Targeting / Cross-Sell

Account Acquisition

Account Activation

Usage Stimulation / Up-

sell

Profit Management

Collections/ Recoveries

Retention

Account acquisition Account management

• Predicting best product offer to customer

(maximizing response rate)

Product marketing models

• Predicting the likelihood of repayment

problem based on application and external

data

• Predicting losses in case of problems with

repayment based on application and

external data

• Predicting the likelihood of Fraud

Business decision, PD/PL, LGD/LGI, CCF,

collection models

• Predicting customer profitability and linking

price to real applicants’ risk

Risk based pricing models

• Predicting the likelihood of repayment

problem based on application,

behavioural and external data

• Predicting losses in case of problems

with repayment based on application,

behavioural and external data

Business decision, PD/PL, LGD/LGI,

CCF, collection models

• Predicting best action to improve

customer profitability

• Predicting upsell product offer to

customer

Limit management and Product

marketing models

• Predicting the likelihood of customers

going into collection

• Predicting next best action to improve

recoveries

• Predicting the likelihood of Fraud

Collection models

• Predicting the likelihood of customer

switching to competitor

Risk based pricing models

Credit Risk Modeling and Decisioning Models typically included into credit risk modeling portfolio


Credit Risk Modeling and Decisioning Payment issues estimation (application and behavioral)

Application and behavioural scoring

• A scoring model contains 9-12 characteristics taking into account different aspects of customer behavior on allaccounts. The scoring model produces a risk score. Target could be different 30+/6m, 90+/12m

• The behaviour scorecard provides a score (single number), derived from relevant data, that is used to predict thelikelihood of customer being bad from a credit risk perspective

• The output from a scorecard is a risk score, which is transformed to the odds or probability of default via the PDalignment process


Trended variablesTrended credit data contributes to a credit score by helping to assessthe trajectory of credit behaviors, measuring the magnitude anddirection of a consumer’s credit health in the last three to 24 monthsof time. Trended credit data can provide up to a 20 percentimprovement in predictive performance


Rating Assessment

Why ?

• IRB requirement

• Annual credit risk assessment - PD next 12 months

• Collect and store internal customer data

Which ? • All corporate customers with exposure > 250 TEUR

• Parent company and all companies with exposures

How ?

• Rating models

• Annual account and forecast

• Overrides and risk transfers

Calculated

rating

Model

calculation

Proposed

Rating

CRU

Opinion

Approved

Rating

Decision makers

opinion


Use of rating information

a) Input to Risk adjusted pricing calculation (RAROCAR)

2 Pricing

a) Risk categorisation ( RACA) Depth of analysis & documentation requirements

b) Power to act (CI) Decision body for the exposure

3 Credit risk process

a) Pillar 3 report / ICAAP

b) Risk Appetite

4 Reporting

a) Future rating models and internal ad hoc analysis

5 Data validation & analysis

a) PD – one main parameter in REA calculation & the PD curve

1 IRB - parameter


Rating models – Input & Output

Financial

Factors

(e.g. weight 70%)

Qualitative

Factors(e.g. weight 30%)

Customer

Factors

(+/- points)

Rating Model

Rating

Grade

Rating Current PD

6+ 0.030%

6 0.034%

6- 0.048%

5+ 0.070%

5 0.104%

5- 0.156%

4+ 0.247%

4 0.353%

4- 0.553%

3+ 0.812%

3 1.247%

3- 2.307%

2+ 6.396%

2 7.060%

2- 9.863%

1+ 14.794%

1 20.712%

1- 26.926%

PD

Customer

Information

Calculation

rules

PD

transformation

IRB

Parameter


Undesirable behavioral impact on data

Rating assessment should be objective, but data shows that’s not always the case

Example – Threshold for “High-risk” customers (2+ and lower)

Behavior effect → – increase PD for all customers

– large impact on capital cost


Rating model development

▪ The models/frameworks are developed when needed:

▪ Unsatisfactory results in yearly validations, requests from business area or if the

performance of current model not is satisfactory, requirement from FSA.

▪ Rating model development methodologies:

▪ Empirical: Default data is used for statistical modeling, requires many default observations.

▪ Benchmarking: Used when the number of defaults in the data is insufficient, so that external ratings

(e.g. S&P) have to be used as a performance measure in the rating model development

▪ Expert: Main input from credit analysts (Experts). This method is used if there are insufficient data

to use the empirical rating model development and there is no external rating to use as benchmark.

(The cut between different methodologies is not always clear, if default data exists but not enough to

draw statistical conclusions e.g. Large Corporate Rating Model, a mixture of empirical and expert

based methods can be used)

▪ Time series of internal customer for the relevant portfolio. Historical default observations, financial

statements (incl. adjustments), data on the qualitative factors and other relevant factors; age of

company, payment remarks etc.

▪ CRR states that at least 5 years of data is needed for the PD estimates. Due to the recent financial

crises , the FSA sometimes require ~7 years of data to include the 2008 downturn.


PD Estimation

Average PD = +Average ADF Safety Margin

• In order to obtain rating and scoring scales that divide customers into rating grades that reflect their

probability of default over a one year period, models are created

• The models are based on internal historical data

• For each of the rating and risk grades, the following calculation shall be fullfilled

Why no downturn add-on for PD?

For CCF and LGD a downturn add-on is applied to make sure that the values reflect

downturn conditions, which is the scenario of concern. However, in the case of PD,

average PD is transformed in the IRB formula in a manner that takes downturn

conditions into account. Hence, no additional downturn add-on is needed.

• Safety margin is Margin of Conservatism which adds conservatism to the PD estimates

• The PD models are validated annually to secure that the equation above holds. If not, re-estimation of

the PD estimates is initiated.


IRB Credit Risk – PD: Rating Model DevelopmentFrom Model Input to Rating (using fictive figures)

• The factors and factor categories are given a statistically derived risk weight depending

on their correlation to the factor categories and ADF respectively. However, the customer

factor score is directly added to the accumulated FF and QF score.

Score Rating grade

94.44 -

100.006+

88.89 - 94.44 6

83.33 - 88.89 6-

77.78 - 83.33 5+

72.22 - 77.78 5

66.67 - 72.22 5-

61.11 - 66.67 4+

55.56 - 61.11 4

50.00 - 55.56 4-

44.44 - 50.00 3+

38.89 - 44.44 3

33.33 - 38.89 3-

27.78 - 33.33 2+

22.22 - 27.78 2

16.67 - 22.22 2-

11.11 – 16.67 1+

5.56 - 11.11 1

1.00 - 5.56 1-

Financial Factors Factor value Risk Weight Score

EBITDA to Turnover 0.05 18% 16.67

Equity Ratio 0.23 39% 66.06

Interest Coverage 2.20 31% 27.83

Quick Ratio 1.94 12% 32.83

EBITDA to debt 0.38 0% 60.33

Risk weighted FF Score: 41.33

FF Risk Weight: 67%

Qualitative Factor Factor value Factor Weight Score

Financial Mgmt 4.00 24% 66.67

Management &

Competence5.00 76% 83.33

Flexibility/suppliers 3.00 0% 50.00

Market and prod. 5.00 0% 83.33

Risk Weighted QF Score: 79.33

QF Risk Weight: 33%

Customer Factor Factor value Score Impact

Age < 1 year No -14

Age 1<= x <= 2 years No -9

Age 1<= x <= 5 years No -6

Age > 5 years Yes 0

State/municpal owned Yes 6

Smaller Payment Remarks No -13

Considerable Payment Remarks Yes -14

CF Score: -8

Calculated score 41.33*0.67 + 79.33*0.33 -

8

= 45.87


IRB Credit Risk – PD: Rating Model DevelopmentSegmentation

• If different sub-segments of the model portfolio is expected to behave differently there

might be reason to have a model segmentation.

• Example of different types of segmentation:

➢ By industry

➢ By size

➢ By country

• Example of ways of segmenting the model

➢ Different factor value cut-offs (if a factor value is

expected to be associated with different risk levels

depending on segment)

➢ Inclusion of industry-specific factor in the

regression (e.g. country-specific macro-economic

factor, or Turnover as size factor)

➢ Different factor weights or different set of factors

(i.e. different models. If there are different risk

drivers between the segments.)

➢ Different mapping from scores to rating (same

model is applied, but the regression output is

mapped differently to the rating scale)

Rating Models

❑ Corporate Rating Model (CRM)

❑ Real Estate (RE)

❑ Shipping (SH)

❑ Large Corporate (LC)

❑ Bank

❑ Other Financial Institutions (FI)

❑ Hedge Fund (HF)

❑ Tenant Owners Association SE

❑ Tenant Owners Association FI


Risk Modeling and DecisioningTrends


The World is Changing...

TOTAL POPULATION

INTERNET USERS

ACTIVE SOCIAL MEDIA USERS

UNIQUE MOBILE USERS

Sources: World Bank, Statista, We Are Social, Morgan Stanley Research

2008

6,766m

1,547m

< 900m

~ 600m

2018

7,632m

3,578m

3,196m

5,135m

Millennials & Generation Z

Baby Boomers & Generation X

CONSUMPTION DRIVING

GENERATION


Financial Services are Affected

Regulatory Framework

Disruption

BCBS 239 AnaCredit

IFRS 9

Stress Testing

FRTB

FinTechs

Blockchain

Crowd Financing

Digital Banking

UX

… …


Demand for operational excellence

• Regulation, digitalization andFintech are forcing banks todeliver higher perceived qualityto motivate existing price /margins…

To hold leading position in the digital

world, bank will need to provide above

market quality targeted products and

servicing at a low operational cost,

what requires state of the art analytics,

modeling and integrated automation…


Traditional bank`s Fintech`s

Traditional VS Digital

Decisioning time

Credit score

Document handling

3-20 days 10-20 minutes

“Limited” Dynamic and comprehensive

Paper Digital

EffortMostly manual Automated


Observed Market trends - FinTech lending in US

• “…Their use of the latest technologies combined with cutting edge alternative and

trended data has likely helped them become leaders in the personal loan industry,” said

John Wirth, vice president of FinTech strategy and market development at TransUnion.

• “Counter to general assumptions about FinTechs, only around 10% of originated FinTech

loan balances are subprime, compared to 14% for the overall market for personal loans,”

added Wirth.

• At the end of 2016, FinTechs represented 30% of all personal loan balances, up from

about 4% in 2012 and less than 1% in 2010. This trend continued through the first six

months of 2017, with FinTechs now representing 32% of personal loan balances.

Source: “Fact or Fiction: Are FinTechs Different from Other Lenders?” 2017 TransUnion LLC


Observed Market trendsThe Small Business Credit Survey (2018), a national collaboration of the 12 Federal Reserve Banks“Last year 32% of credit-seeking small businesses applied to an online lender, up from 19% in 2016… Speed of decision-making and perceived chance of funding were the top reasons firms applied to online lenders.”

Source: Global FinTech Adoption Index 2019 by EY


Observed Market trendsThe Small Business Credit Survey (2018), a national collaboration of the 12 Federal Reserve Banks“Last year 32% of credit-seeking small businesses applied to an online lender, up from 19% in 2016… Speed of decision-making and perceived chance of funding were the top reasons firms applied to online lenders.”

A BCG survey in 2017 found that 45% of corporate banking divisions worldwide posted declining profits, and around half had pre-tax returns on capital that were below the hurdle rate (16%).


The future of risk management in the digital era


External data sources used in credit risk managementOverall landscape

* -including Insurance and Finance companies


Alternative data

Score450

Credit report

Application

Decisions are made on limited data.

Full picture of customer profile creates greater customer

experience.

1 – “The State of Alternative Credit Data” by Experian2 – “The State of Alternative Data” by TransUnion LLC

1

2

Score720

Credit report

Application

Social media

Mobile data

Browsing data

Social networks

Behavioral information

56% of lenders

using alternative data say the data has opened up new markets.

Consumers are open to sharing their credit data.

70% are willing to provide additional

financial information to a lender if it increases their chance for approval or improves their interest rate for a mortgage or car loan.


Data sharing

ACCENTURE: 2017 BEYOND DIGITAL: HOW CAN BANKS MEET CUSTOMER DEMANDS? ACCENTURE: 2019 GLOBAL FINANCIAL SERVICES CONSUMER STUDY



Value of alternative data

http://www.fico.com/en/blogs/analytics-optimization/using-alternative-data-in-credit-risk-modeling/


Mixing traditional and alternative dataThe correlation between the scorebased on the digital footprintvariables and the credit bureau scoreis approximately 10%. As aconsequence, the discriminatorypower of a model using both the creditbureau score and the digital footprintvariables significantly exceeds modelsthat only use the credit bureau score oronly use the digital footprint variables.This suggests that the digital footprintcomplements rather than substitutesfor credit bureau information and alender that uses information from bothsources (credit bureau score + digitalfootprint) can make superior lendingdecisions compared to lenders thatonly access one of the two sources ofinformation.

On the Rise of the FinTechs—Credit Scoring using Digital Footprints FDIC CFR September 2018


Machine Learning models

https://www.kdnuggets.com/2019/04/top-data-science-machine-learning-methods-2018-2019.html

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Wikipedia

https://www.kdnuggets.com/2019/04/top-data-science-machine-learning-methods-2018-2019.html

Company Conf ident ia l – For Internal Use OnlyCopyright © SAS Inst itute Inc. A l l r ights reserved.

“It’s not a matter of if but when — there is the need for us to be fast while the world is changing fast.”

The AI-Wave is coming …


Artificial Intelligence application in Financial Services

https://www.accenture.com/_acnmedia/PDF-68/Accenture-Redefine-Banking.pdf

https://www.sas.com/sas/events/19/asean-fsi-roadshow.html

https://www.accenture.com/_acnmedia/PDF-68/Accenture-Redefine-Banking.pdf

https://www.sas.com/sas/events/19/asean-fsi-roadshow.html


Some of ML applications in are of Risk modeling

• Building credit decision/debt collection/fraud models using Advanced ML techniques

- Variable selection

- Benchmarking

- Use as additional variable

- ML models AS IS

- Predicting traditional model output

- Optimal binning, reject inference, calibration and etc.

• Process automation

- Early warnings / Portfolio monitoring

- Data quality

- Contacts automation


Rates of AI Adoption by Risk Use Case

Source: SAS and GARP: Artificial intelligence in banking and risk management survey

Process Automation

Credit Scoring

Data Cleansing & Enhancement

Risk Grading

Model Validation

Model Calibration

Model Selection

Regulatory Reporting

Loan Approvals

Collections

Process Refinement

Loan Pricing

Loss Provisioning

percent use percent not use

20% 40% 60% 80% 100%0%


Analytically Enhanced Credit Models Can Improve Banks’ Returns In Four Ways

Source: McKinsey, Risk Analytics Enters Its Prime, June 2017. 1 Impact not additive and depends on the bank’s portfolio.


Current adopters are seeing the benefits of AI.

80.5%

Perspective on AI from the industry

Greatest Benefit Expected from AI over the Next Three Years

Faster insight from data

Reduced manual tasks

Improved decision-making

Higher productivity

Lower operating costs

Product quality/ customer

experience

Total responding “significant” or “major” benefit

78% 77% 77% 73% 66% 66%


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Model Interpretability

Key Challenges of AI adoption

Data Availability,Quality,

Biases

Costs & Time to benefits

Talent & Understanding

Technology changes / Deployment / Maintenance

Source: SAS and GARP: Artificial intelligence in banking and risk management survey


Talent & Understanding


Data Availability, Quality, Biases


Model Interpretability


Regulatory Compliance – Continuous Evolution

1997 2000 2002 2011 2014 2016/17

OCC 1997-24Risk Bulletin

OCC 2000-16Risk Bulletin

SOX2002-404

Federal ReserveSR11/7

EBA SREPGuidelines

EBA TRIMGuidelines

Defective portfolio-optimization model in major FI. Loss: $100m

Risk-hedging VaR model in global bank over-ridden. Loss: $bns

Market Events

Establish model inventory

Identify, monitor and manage model risk

Document regulatory compliance

Regulatory Expectations

Regulatory fines

Increased capital charges

Headline risk

Inspections and Non-Compliance

2018…

PRA Supervisory Statement



Ensure Risk models are of sufficient quality:

• all the aspects of the specification of the internal ratings and risk parameters, including the procedures for data

collection and data cleansing, the choices of the methodology and model structure, and the process for the

selection of the variables should be critically re-viewed,

• the adequacy of the implementation of internal ratings and risk parameters in IT systems and that grade and pool

definitions are consistently applied across departments and geo-graphic areas of the institution should be verified,

• the performance taking into account at least risk differentiation and quantification and the stability of the

internal ratings and risk parameters and the model specifications, and

• all changes related to internal ratings and risk parameters should be verified.

The validations must assess the accuracy and consistency of the model including its continued ability to adequately capture

the risk and uncertainty of the area it models. Historically, validating complex model was not deemed a high priority, and in-

house model validation teams lacked organizational and economic support.

Model development or recalibration can be driven by various reasons, including deterioration of existing models/model

systems. Issues around effort, complexity and turnaround time of validation process becoming more acute due to

advent of machine learning (ML) models.

Risk Model Validation


Technology changes / Deployment / Maintenance

Data Scientists don’t stay for long (average tenure = 2.3 years^) and are difficult to replace (6+ weeks on average^^)

There have been 23 versions of R* and 39 versions of Python** since 2014

* Source: https://cran.r-project.org/** Source: https://www.python.org/downloads/

^Source: https://www.datasciencecentral.com/^^Source: https://www.forbes.com

This doesn’t include different versions of individual packages

• Requirements to recode in Java, Spark etc. for deployment add extra time and complexity

• Organizations take on responsibility for regression testing by using open source (most don’t realize that)

https://cran.r-project.org/

https://www.python.org/downloads/

https://www.datasciencecentral.com/profiles/blogs/data-scientist-still-the-best-job-in-america-again

https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/#678213bc7e3b


DATASETS

• Various shared drivers and/or desktop disk used to create, update and store datasets

• No dataset naming convention or users don’t always follow it

• No versioning rules for datasets or some manual emulation of versioning (quality and existence vary by user)

• No documented overview of datasets or manual documentation process (quality and existence vary by user)

• Some banks are using Git, VSS, etc. in modeling teams, but quality and usage vary by user

• Issues with performance (transferring big table loads network and reduced speed of execution)

• Hard to find required dataset

• Hard to track and understand purpose of datasets

• Hard to understand which data set is final and latest

• Low reuse of datasets, users create additional overlapping dataset loosing time and consuming even more disk space

• Multiple datasets containing same data and consuming extra disk space

• Low protection of final dataset form being overwritten/deleted

• Hard to transferee knowledge within team, onboard newcomers and/or when reorganization happen

• Issues with data access management

• IT does mostly technical maintenance

• Issues with audibility and compliance

Credit Risk ModelingTypical issues related to code based modeling


Credit Risk ModelingTypical issues related to code based modelingCODE AND VARIABLES

• Coding style, techniques and quality vary by users

• Various shared drivers and/or desktop disk used to store data preparation and modeling code

• No code file and variables naming convention or users don’t always follow it

• No variables dictionary or poorly maintained

• No documented overview of code files or manual documentation process (quality and existence vary by user)

• No versioning rules for code files and variables or some manual emulation of versioning (quality and existence vary by user)

• Some banks are using Git, VSS, etc. in modeling teams, but quality and usage vary by user

• When binning various continues variables “if – then” logic used through copy-paste

• Reuse is done through copy-paste of code files or its parts

• Data preparation and modeling speed significantly vary depending on coding skills of user – harder to plan and deliver, performance is

subject to volatility when experienced team member is replaced with newcomer

• Higher, broader and more specific requirements toward candidates (e.g. not only good statistics, but also strong programming, preferably

specific language) narrowing candidates pool, shifting up potential salaries, increasing recruitment time and key personal risk

• Issues with performance (non-efficient code will run longer, affecting performance of everyone if run on server)

• Hard to find required code file or/and code to derive variable

• Hard to track and understand purpose of code files

• Hard to understand which code file or code to create variable is final and latest


Credit Risk ModelingTypical issues related to code based modeling

• Hard to understand which code file or code to create variable is final and latest

• Different names for same variables and/or same names for different variables leading to wrong results and inconsistency

• Takes additional time to marry own and copy-pasted code

• Important data quality fixes and/or data transformations may be missed during copy-paste leading to mistakes

• Low reuse of important data quality fixes and/or data transformations

• Reuse of manually written code can lead to inherited mistakes

• Low reuse of code and previously created variables, users recreate variables and data preparation steps, where it may lead to different

definitions and differences due to operational mistakes

• High operational risk during copy-paste use, especially increasing for code maintained and reused overtime by several people

• 100+ lines code and multiple code files to create one output (e.g. LGD) hard and time consuming to update and maintain

• Hard to transferee knowledge within team, onboard newcomers and/or when reorganization happen – takes much longer to understand

what code does

• Typically new person to maintain code have to write it to match his own programming style

• Some code get lost on personal computers

• Low protection of final code form being overwritten/deleted

• Risk of using wrong code when promoting between development and production environment

• Issues with audibility and compliance

• IT does mostly technical maintenance


Costs & Time to benefits

Applying Machine Learning Techniques at Centrica, Paul Malley & Spiros PotamitisDenver SGF18 9th April 2018


• The Methodology

• Exclusive patented machine learning technology producing regulatory compliant neural

networks for risk decisioning applications. Optimally constrained neural networks:

- Improved performance and accuracy

- Returns a risk score and reason codes

- Interpretable to customers and regulators

- Enables deeper learning of consumer behavior through complex non-linear attribute

interactions

• Bottom line

• For a portfolio bad rate of 9.5%, 1.3% more consumers are approved

• Captures 3.3% more bad consumers in the bottom 20% of the population

• Over 30% accuracy lift, scorable rate around 95% for income models

Case Study: Advanced Machine Learning and Regulatory Compliance

A large organization providing data and services to banks in United States wanted to improve ultimate accuracy of income models and risk scores

The ApproachDifferentiate with multisource data assets and machine learning, while conforming to FCRA & Reg B

820,000,000 consumers91,000,000 businesses


Artificial IntelligenceKey Ingredients for Success


d r i v e r s

Increased Competition

h o w

Risk Platform Transformation

› Time to intelligence

› Model precision and volume

› Total cost of ownership

› Speed and velocity

› Customer experience

b e n e f i t s

Efficiency

EffectivenessConsumer

Expectation

Technologydevelopment

w h a t

Ensure regulatory compliance

Optimize Operations

Evolve risk analytics

Behind Digital Transformation



Consistent Visual Interfaces

Programming Interfaces

API Interfaces

GUI user

Developer

Open Source

CoderSAS Coder

The SAS PlatformSupport all users


• Building extensive model inventory: PD, LGD, application, behavioral models, collection scoring

• Reducing model development lifecycle

• Model risk management

Credit risk management lifecycle

EDW

DDS

Customer aqusition / Loan Origination

Data management Credit risk modeling RWA and provisionsCapital management

and Stress testingPortfolio management

and Reporting

• Omichanel/ Digital banking

• Credit policy automation

• Real time decision management

• Risk based pricing

• Fraud prevention

• Cross&up sell

• Preapprovals

• Compliance Basel II/III Credit risk Pillar I (SA, F/A -IRB)

• IFRS-9

• Risk and finance reconciliation

• IRRB, FRTB, revised SA

• Economic capital calculation

• RAROC, RAPM

• REGcap vs ECcap

• Risk appetite

• Limit management

• Macroeconomic modeling

• Stress testing

• ICAAP

• Data quality

• Data availability

• Shortening time for business access to data

• Early repayments modeling

• Operational management reporting

• Balancesheetmanagement and optimization

• Liquidity risk management

• Debt collections


Transforming The World of Risk Analytics

Expected Credit Loss (IFRS 9/CECL)

Enterprise Stress Testing

Regulatory Risk Management

Model Risk Management

Risk Modeling& Decisioning

Insurance (Solvency/IFRS 17)

SAS Risk Platform powered by AI/MLOpen, sustainable and expandable technology

Flexibility and repeatabilityIntegrated data, AI/ML models, and reporting

Powerful, robust infrastructure


Risk Modeling & Decisioning 7.1 - Conceptual design

External data sources

LOS

CRM

Other internal systems

Corebanking

DWH

Foundation mart

Landing area

SAS Intelligent decisioning + ESP

SAS ADBFilters & ABTs

SAS EMModeling

SAS CSModel batch

SAS CSModel monitoring

SAS Model Manager

SAS Data Prep & DI

Real-time integration



ETL

SAS VA

Profiling

Standardization

ELT

DQ

Decision mart

SAS Information Maps

VDMMLModeling


Ho

w S

AS

Hel

ps

Increase revenue, reduce losses, control

costs

• Improve approval rates, omni-channel sales

• Reduce losses, provisions, RWA

• Improve recovery rates

Key

Dri

vers

R O I B u s i n e s s D r i v e r s

Improve Efficiency Improve customer experience

• Reduce attrition

• Increase loyalty

• Personalize customer experience

Compliance

• Regulatory models

• Performance Monitoring

• Use test

✓ Wider variety of data for models

✓ Quick access to data for discovery and analytics

✓ Advanced data governance and quality management

✓ Higher granularity of customer segmentation

✓ More and better models

✓ Decisioning automation

✓ Tools/systems integration within environment

✓ Best practice templates, frameworks and other content

✓ Model development and decisioning framework, process self-documentation

✓ Transparency, auditability and easy knowledge sharing/ on-boarding process

• Increase decision speed

• Decrease manual steps in decisioning

• Decrease product time to market


Risk Modeling and DecisioningCredit decisioning


Request/Reply

SAS Risk Modeling and Decisioning Credit Decisioning

Decision Flow

Business Rules

Analytics

Custom CodeDS2, Python

Rule Fired

Analysis

Path Analysis

Batch

Testing Execution

CAS

In-DB

Real Time

MAS

ESP

-----------------------------------

Front-office

Data Streams

Streaming dataEdge Analytics

Repository

Risk Decisioning

Monitoring & Retraining

Versioning & Governance

Model Manager

Operational database storing decisions

Data Preparation & Data Quality

Predictive Modeling

Machine Learning & AI

Analytics

Company Conf ident ia l – For Internal Use OnlyCopyright © SAS Inst itute Inc. A l l r ights reserved.

• Customer documents• Transactional data• Internal micro & macro market analytics

• Cloud accounting• Google / Yahoo• Other search engines• Amazon / Lazada

• Logistics companies / Shipping info• Various media pages / Data providers• Government DBs / credit bureaus• D&B / Refinitiv /Bloomberg

Co

rpo

rate

/ S

ME

Len

din

g d

ecis

ion

ing R

etail credit d

ecision

Early warnings

On-boarding

KYC

Credit risk assessment

Deal structuring

Approval & disbursement

On-boarding

Credit risk assessment 1




Batch, Real-time and Streaming data collection

Data preparation, Data quality, Text and sentiment analytics

Pre-trained self-learning ML/AI risk models

Configurable Credit Decisioning APIs

Pre-configured rules and strategies

Comprehensive and dynamic reporting

Data input and data collection process initiation: LOS, Web

CDD, AML, FATCA, Connected customer groups

Financial analysis, traditional and alternative credit risk assessment

Selecting optimal deal structure, pricing and covenants

Automatic / management approval, disbursement


Credit policy rules, application and internal data based credit risk assessment

External traditional and alternative data based credit risk assessment

Credit risk assessment based on internal manual input if required


Political and legislative analytics

Macro and industry monitoring

Portfolio monitoring business rules and ML/AI models

Interactive reports and dashboards


Risk Modeling and DecisioningLoan Origination


Loan disbursement process

Application for loan Decision taking Loan origination

Re

tail

Cre

dit

An

aly

st

(Un

de

rwrite

r)

Re

tail

Cre

dit

An

aly

st

(Un

de

rwrite

r)B

ack

offic

eB

ack

offic

eS

ecu

rity

Se

curity

De

cisi

on

ta

kin

g

pe

rso

n

De

cisi

on

ta

kin

g

pe

rso

nS

ale

s te

am

Sa

les

tea

m

SellingEntering an

application into

CRM

Document and

identity

verification

Security check

Credit analysis

(Underwriting)

Decision taking

Credit

analyst

repost

(Underwriting

report)

Customer

notification

Draw up of

documents

Contract

signing

Loan

origination

Money

transferReject

Yes

No

No

Identity

verification

No

Data File

Data File

Data File

Preliminary

evaluation

Typical process


Typical process

Business Risk Back-office

LOS LOS + XLS LOS + Core banking

Disbursement processDecision making processSales process

Deal processing – results in a

signed loan agreement

Collateral registration – result:

collateralisation

Loan disbursement – money gets

into customer account or

transferred to seller

Archiving – it is verified that

mandatory documents are

received in archive

Credit decision – result is approved

credit limit for a specific application

Product sales –

result is application

for a credit product

IT

syste

m

Pro

ce

ss

ow

ne

r


Typical rolesAuthorized persons Control activities (process step by step)

Sales team Verify authenticity of the documents, assures copies of the documents, is responsible for the data quality of application in CRM-system, verify that the

application comply with lending policies, forms an intermediate decision on application.

Fraud team Verify the feasibility of customer’s personal information, look up for customer on the black lists and lists of criminals, verifies employment, if necessary,

produces an interview with a client or visits the place of job or residence, also forms an interim decision on the credit application

Retail credit analysts

(underwriters)

Controls existence of interlocutory decisions, controls compliance with credit policies, examines the results of security-check, receives information on

customers credit history from the Credit History Bureau, makes judgment on the financial condition of applicant, analyzes fraud & social risk, verifies

income level and assess credit worseness of the customer, if necessary, requests additional documents, may require additional collateral, based on

the powers to act table determines level of authorized person who would make final conclusion.

Collateral valuation team For collateralized loans: verify announced value of the collateral, for mortgage loans, also checks appraisal report granted by independent approved

appraisal company.

Legal department

(mortgage only)

Review legal issues regarding collateral – there are a lot of legal restrictions in Russian law because of which collateral can not be recovered in case

of default (for example, when residents are minors) - so in some cases it is required to involve legal department into the process.

Decision maker Checks existence of all interlocutory decisions, goes behind conclusion of a credit analyst, studies information from the Credit History Bureau,

analyzes results of inspections, makes a decision and approves the final opinion on the application. The authorized person is acting within the

approved rules - a decision can not be made without a credit analyst, the amount approved by the loan must be at the limit of authority.

Back-office team Controls existence of approved decision, controls compliance of decision on the loan amount with the limits of powers of decision maker, checks the

completeness of client documents, prepares a set of credit documents for the client.

Signing person Controls presence of the approved decision protocol on the application, monitors the existence of interlocutory decisions and final conclusion, signs

loan documentation on behalf of the Bank.

Back-office team Controls the existence of signed decision protocol, controls the presence of signs of Bank and client on credit documents, forms internal transaction in

core banking (accounting) system.

Back-office supervisor Controls the existence of signed decision protocol, controls the presence of signs of Bank and client on credit documents, verify and accept internal

transaction in core banking (accounting) system.

Teller/ Cashier Transfer or allow money - documents must be signed by operator and supervisor, if cash is allowed, cashier also signs documents.


Typical Issues

#1 – Low level of decisioning automation and high human bias

#2 – Slow, IT resource consuming process to update “configurations” (e.g. scorecards)

#3 – Calculations (DTI, LTV, etc.) used in decisioning may be done outside of IT systems

#4 – Limited/No differentiation in processes between products, customer segments

#5 – High level of process duplications and unreasonable amount of 4eyes. Almost each delivery from

previous step is checked again

#6 – Poor data quality

#7 – Liner dependency between business growth and cost associated with origination


Credit calc

Application data input

Credit scoring & validation on credit policy compliance

Rejected

Accepted

Override

Archive

Fraud/Black list system

Risk level is high or fraud is confirmed.

Rejected

Rejected application database

VIP and existing customers

Fraud / Black list database

Debt collection database

Customer databaseOther sources Debt Collection

Fraud team if needed

Rejected

Accepted

Override

Manual underwriting(Retail credit

analyst)

Responsible person or

committee takes a decision

Rejected

APPROVED

Information about collateral or customer

should be updated

Credit history in other banks

Revalidation on credit policy compliance,

customer creditworthiness calculation

Accepted

Application needs additional

consideration

Application is fully compliant

Will be removed if

this system will be

implemented

Some views on process flow


Application input Hunter

Approved

Credit history

DELOS LOSLOS


ApprovedDELOS LOSLOS LOS DE LOS

Credit history


ApprovedDELOS LOSLOS

CRM

DE LOS

IAU CRM

Credit history


ApprovedDELOS LOSLOS

LOS

DE LOS

IAU LOS

Underwriting & committee

decision LOS

Rejected

Rejected

Rejected

Rejected

Example of possible simple flows differentiated based on product type and customer risk level


New process

Disbursement processDecision making processSales process

Decision Engine

LOSCore

banking

Various data sources


Credit Risk Modeling and Decisioning Best Practice Principles

#1 – Configurable balance between Cost per Decision, Time to Decision and Risk Appetite

#2 – Multi-step strategy based on value chain principle, data availability and logical flow

#3 – All decisions, which do not require human to human interaction and human judgment are moved into Decision engine from workflow

#4 – Decisions outside of Power of Acts are made by Decision engine, where humans provide judgments to be treated by standardized, agreed and approved strategy

#5 – Data acquired for decisioning on a needs basis through dedicated External calls to increase flexibility and decrease unnecessary data movement

#6 – Data aggregates required for existing customers are precalculated and loaded into dedicated Decision mart on a daily basis in batch

#7 – Intraday changes are acquired into Decision mart incremental based on triggers from source systems or based on schedule for further use in Decision engine

#8 – Decision strategy produces maximum possible assessment within minimum cost

#9 – Find best possible option for customer within acceptable Risk Appetite


Definitions for the training

Step 1 Step 2 Step 3

Approve

Reject

Refer

Risk model

Credit policy rules

Credit process / Origination process / Lending process

Decision strategyDecision flow

inside


Credit Risk Modeling and Decisioning Practicalities

• Decision strategy have to produce decision on 2 levels: decision on each applicant level and decision on overallapplication (where for example main applicant and overall application can be approved, but co-applicant can berejected, and main applicant may need to find another co-applicant, provide collateral, do nothing).

• Based on best practice principles Decision flow within decision strategy overall logically can be divided into atleast three stages:

• Analysis of customer’s application and existing internal customer data;

• Analysis of customer’s application and existing internal customer data plus automatically requested data from external sources;

• Analysis of customer’s application and existing internal customer data, data automatically requested from external sources aswell as manual verification results.

• Decision strategy should be executed fully in part of analysis of customer’s application and existing internalcustomer data before data request to external sources / assignment of manual verifications regardless ofwhether there are any single reasons for rejection of application / applicant. If result of one of decision nodesproduces automatic rejection, then all other calculations / decision nodes executions which happen before datarequest to external sources / assignment of manual verifications are undertaken to produce and save fullanalysis and history of application review and to collect all rejection reasons related to application / applicant toprovide them to front-office employee and further analytics.


Credit Risk Modeling and Decisioning Sample flow and rules










[140;165) [165;180) [180;210) ≥210 <140 ≥140

[100;140)

[140;180)

[180;205)

[205;240)

≥240

Description Approved

Declined

Approved by being offered Cross Sell

Scorecard Model B

Scorecard Model ASegmen Fixed Segmen Non-Fixed





Loan origination process and performance

Loan origination process and performance reports typically include following reports:• Time to market over time by:

o products and sub-products

o branches

o partners

o employees

o customer type

• Approval – Rejection rate over time by:


o scores and model

o branches

o partners

o employees

o customer type

o industry

• Number and volume of loan applications over time by:


o currency

o scores and model

o branches

o partners

o employees

o customer type (new/existing, gold/silver,corporate/walk in)

o collateral type and price

o loan size / maturity

o LTV / DTI / Industry

o expected RAROC/other profitability measures

o outcome (accepted/declined/disbursed/adjusted)

• Rejection/Adjustment reason/structure



















Risk Modeling and DecisioningDebt collection


Collection automationPayment pyramid and net loss

• Collect ahead competitors

• Collect More From the More Collectable Accounts

• Spend Less on Less Collectable Accounts


Collection automationCollection strategy

Collection strategy

Customer segments

When to act

How to act

Costs / Benefits

Factors to consider include:

• Different customers will respond differently to actions

• Efficiency vary significantly across the types of actions available

(e.g: reminding sms versus outbound call on 5th day)

• The success rates vary significantly across the types of actions

performed (e.g. phoning is more effective than sending a

letter)

• The cost of collection may outweigh the potential return

• It is often combination of actions that make a particular

customer repay

The strategy needs to define:

• Customer type

• Most effective combination and order of actions


Collection automationExample of Collection models

Model type Brief description

General collection scoring

Application data scoring assessing if customer:

• will be in Collection in upcoming x month

• will be in Collection 3 times within next 12 month or will stay more than 60 days

• contact score + recovery rate score

Pre-collection scoringApplication/behavioral data scoring assessing if customer will be in Collection on next

payment

Soft collection scoringApplication/behavioral data scoring assessing if customer will be move into Medium

collection

Medium collection scoring Application/behavioral data scoring assessing if customer will be move into Hard collection

Fraud collection scoring Application/behavioral data scoring assessing if this is a fraud case

Collection agency scoring Application/behavioral data scoring assessing if external agency should handle this case

Contact scoring Application/behavioral data scoring assessing when contact should be run

Other prediction models Application/behavioral data models predicting efficiency of actions


Collection automationSegments and Strategy examples

Stage DPD Action

Soft 1-5 Grace period

6-8 Soft telephone call

8 First reminding letter is sent, if no

contact/promise has been made

8-29 Soft phone reminder/SMS

Medium 30 Card/Account block

30-38 Medium phone reminder/SMS

38 Second letter is sent, if no

contact/promise has been made

39+ Medium phone reminder/SMS

Frequent phone calls

Summon/Internal Agency

Selective referral to the External Agency

Disposal to work place

Transfer to External Agency

Selective referral to Legal

Upload account to the Black List (Credit

Bureau)

Legal procedure

Write-off decision

Recovery

Legal 180+

60+

Hard 90+


xcxcxcxcxc Contract/loan level analysis

Contract/loan status assignment

Customer level analysis

Contract/loan status assignment

Days past dueBehavioral models /

customer segmentation

Strategy assignment / optimization

Strategy selection (channel, approach)Data transformation

Product attributes (interest rate,

collateral requirements)

Application data

DPD prediction

Pre-collection scoringProbability of repayment

Contact scoring (channel, type, frequency)

Strategy assignment

Filtering (VIP, fraud, dead)Soft/Medium collection scoring

Contacts

Current days past due

Payment behavior

Contract attributes (maturity, fees,

penalties)

Customer segmentation based on previous contact

historyContracts

aggregation

Calculation of additional variables

Champion/challengerRestructuring scoring

Collection automation


Risk Modeling and DecisioningSME/Corporate lending


SME / Corporate lendingTypical challenges

#1 – Legacy Application Process

• Current credit approval system is rigid and inflexible. It does not allow an application to go back in process incrementally

• There are manual process done outside of the system

#2 - Extensive documents attached to the application which are largely untapped

• Information in attachments are not ingested and hence not leveraged for future consumption

• Rarely reviewed or used for subsequent account or limit reviews

#3 – Credit Assessment and Approval is manual and impacted by human biases

• The decisioning process is currently discretionary and there is no standardization of credit risk factors

• There is heavy reliance on the Credit Officer’s experience and judgement

#4 – Limited data availability and variety

• There is a lack of information for the Medium Enterprise segment

• As a result, the bank can only rely on traditional information point in time (e.g. financial statements) which are not forward looking


New generation SME lending

Real-time data collection across various sources

Simple online Loan application

Automated customer

decisioning and origination


• Customer documents• Transactional data• Internal micro & macro market analytics

• Cloud accounting• Google / Yahoo• Other search engines• Amazon / Lazada

• Logistics companies / Shipping info• Various media pages / Data providers• Government DBs / credit bureaus• D&B / Refinitiv /Bloomberg

On-boarding

KYC

Credit risk assessment

Deal structuring


Batch, Real-time and Streaming data collection

Data preparation, Data quality, Text and sentiment analytics

Pre-trained self-learning ML/AI risk models

Configurable Credit Decisioning APIs

Pre-configured rules and strategies

Comprehensive and dynamic reporting


CDD, AML, FATCA, Connected customer groups

Financial analysis, traditional and alternative credit risk assessment

Selecting optimal deal structure, pricing and covenants


CLOUDHYBRID2-30 Days

1 day


Credit Assessment and Approval Standardization

Reuse of untapped information in attachments

Extend analytics with additional structured and unstructured data

SME / Corporate lendingPotential value chain based evolution of solution over years

- Semi-automated capture of traditional data required for credit decisioning

- Semi-automated decisioning based on formalized and standardized decision rules

- Extended analytical capabilities, e.g. FS forecasting, peer benchmarking

- Automated capture of traditional data required for credit decisioning

- Increased automation in decisioning process, e.g. Credit Memo draft generation

- Increased reusability of generated assets in credit process

What

What

What- Automated capture of additional

data required for credit decisioning

- Automated generation of suggested credit decisions with justification

- Comprehensive customer / customer group / deal level analytics with simulation capabilities


SME / Corporate lending

SAS SME / Corporate Risk Underwriting UIs: Home Page



SAS SME / Corporate Risk Underwriting UIs: Credit Application Summary


A Customer Group is typically defined as a group of Borrowers (two or more individuals or legal

entities) who are in association with each other and who, if there is no evidence to the contrary, may

be regarded as a common risk because:

• One or several companies within a group own more than half of the shares in one or several

of the other companies within the group or, otherwise exerts decisive influence on one or

several companies within the group; or

• If, without being involved in a relationship as referred to in item a), their mutual connections

are such that one or all members of the group would presumably find it difficult to meet

payment obligations if one or several others of the group were to suffer financial hardship.

Following b) above a Customer Group exists e.g., where:

• Interdependency exists between two or more customers e.g. through co-operation

agreements and other agreements or inter-company debt

• The debtor is a co-debtor (directly or in the form of joint ownership, partnership etc.)

• The Borrowers are married, cohabitants or registered partners




SAS SME / Corporate Risk Underwriting UIs: Connected entities review


The primary focus in credit analysis typically is to assess the risk for that the borrower may not fulfil its

debt obligation – the default risk.

As mentioned, if the customer is part of a customer group, it is important to evaluate how dependent

the customer is on the group in order to decide whether the analysis should focus on the group, parts

of the group, or only the customer itself.

Default risk is defined as the risk that the borrower may not fulfil its debt obligations. The assessment

typically is based on an analysis of the five risk components 1) country risk, 2) ownership and

management risk, 3) Industry risk, 4) company risk and 5) financial risk.

Furthermore, most banks evaluate if the commercial risk (industry- and company risk) is balanced with

an adequate financial strength. The higher the commercial risk, the lower the financial risk should be.

The analysis should lead to conclusions regarding repayment ability, financial flexibility and repayment

capacity. These conclusions should be aligned with the rating assessment. Factor typically used in

rating models include the last annual accounts as well as qualitative factors assessed, and customer

factors observed by the credit analysts at the time of the rating assessment.




SAS SME / Corporate Risk Decisioning: Sample qualitative questionsQualitative Factors Sub-questions

Financial management, control

and reporting

Financial performance Does the company have the ability to reduce costs according to demand reductions?

Do you expect the future sales of the company to be volatile?

Are there satisfactory future earnings prospects for the company?

Flexibility and suppliersFlexibility/suppliers Are the buildings and any production facilities in good condition?

Is the company flexible enough if the market changes?

To what extent is the company dependent on only a few suppliers?

Management and competence

Management and competence Does the management have the ability to innovate and adjust the company to new conditions?

To what extent is the company dependent on essential personnel?

Do the owners have resources and intention to support the company financially if needed?

To what extent do the cash and financial management seem to be efficient?

Have there been frequent changes in auditors and/or management?

Market and products

How do you consider the future prospects for the industry?

How strong is the company's position on its main market(s)?

To what extent is the company dependent on only a few customers?May the company's operations be seen as harmful to the environment or does the company sell/manufacture products that can have

such an effect?

How sensitive are the company's products/services to business cycles? Nature conditions or changes in fashion?



SAS SME / Corporate Risk Underwriting UIs: Qualitative Assessment


The understanding of a customer’s ability to generate sufficient cash to cover both interest and debt

amortization is essential. The operating cash flow should be sufficient to cover the customer’s

obligations at any time. In addition, the customer’s financial strength or capacity in terms of equity is

considered. To be able to assess both the Repayment Ability and the Repayment Capacity of the

customer, it is important to have analyzed up-to-date financial information.

When performing financial analysis typically to enable consistent input of financial factors used to

calculate ratios (in order to comply with Banks definitions of financial ratios) that go into rating model

changes to the financial statements as reported by the company are required.

It is essential to understand and assess the ability to generate cash from ordinary recurring business

operations, excluding gains and losses from business operations that are based on projects or similar

activities. Under adverse business conditions Bank can only rely on the individual company’s ability to

generate cash from its operations. These might be affected by weak demand, but do not depend on a

few transactions in a market that might be illiquid for a prolonged economic downturn. Thus, it is

important to assess repayment ability not based on customer expectations, but also based on Banks

own forecasts of adverse market conditions, which will impact generated free cash flow and

subsequently ratios and rating.






Sample financial ratios

Leverage(Interest Bearing Debt / EBITDA)

Debt Service (Interest coverage)((EBITDA + Financial Income) / Financial Expenses)

ProfitabilityReturn on Total Assets((EBIT + Financial Income - Foreign exchange - write-downs and re-evaluation) / Average Total Assets)

EBIT to Turnover(EBIT / Turnover)

Capital structureEquity ratio(Total Equity + Proposed dividend / Total Assets)

Interest Bearing Debt to Equity(Interest Bearing Debt / Total Equity)



Sample financial ratios

Interest Bearing Debt

+ Subordinated loans that are not comparable to equity

+ Interest bearing liabilities

+ Interest bearing liabilities to group companies

+ Leasing liabilities

+ Subordinated loans that are not comparable to equity

+ Interest bearing liabilities (incl. Current portion L/T debt)

+ Interest bearing liabilities to group companies

= Interest Bearing Debt



SAS SME / Corporate Risk Underwriting UIs: Quantative Assessment



SAS SME / Corporate Risk Decisioning: Financial Ratio calculation



Typically, the secondary focus is to assess Banks recovery position in the event of the customer’s inability to effect

payment. Recovery risk is defined as the risk that the collaterals and guarantees provided are insufficient to cover Banks

claim, in the event of the inability to effect payment. The assessment typically is based on an analysis of collaterals,

guarantees and documentation including credit structure and covenants. The conclusion includes the sufficiency of the

collaterals, guarantees and documentation established, in relation to Banks claim, in the event of the customer’s

inability to effect payment. Collaterals and guarantees should be regarded as “the second way out”, if the repayment

capacity becomes insufficient.

The importance of establishing financial covenants increases with the unsecured portion. The selection and level of

covenants should make it possible to react on early warning signals while the customer still is bankable or there is time

to take other action to mitigate the breach. Financial covenants should preferably be connected to the customer’s

repayment ability. Other covenants and clauses in the documentation could include cross default, cross collateral, cross

guarantees, cross acceleration, change of control, dividend restrictions, pari passu, capex restrictions, divestment

restrictions. These are typically established with the purpose to provide additional downside protection. SAS RM/CA UIs

enable Bank not only register collateral and covenant information, but also to spin-off separate process for proper

collateral valuation, including registration of various collateral value assessments, e.g. market value, forced-sale principle

based collateral value and etc.



SAS SME / Corporate Risk Underwriting UIs: Rating, Pricing and Credit Memo



SAS SME / Corporate Risk Decisioning: Rating calculation approach


Case Study: ING Working Capital Solution


Sberbank – short description

Key facts:

• The Largest bank in Russia (~30% of Russian Banking Sector)• Total assets – £ 390 billion• 14,000 branches, 22 countries• 92 mln active retail clients and over 2.4 mln corp clients• SAS customer since 2011

• Modelling• RWA• Operational Risk• Decisioning (Retail and Corporate)


Decisioning for SME & Corporates

Project key features

Success factors

• Time to decision

• Convenient service and interface

• Customization of credit parameters

TECHNIQUES

▪ NLP: Neural network(CNN + bi-LSTM, BERT + Attention и Few-shot Learning (Proto-NER),

▪ CV: Optical character recognition (OCR)

▪ DM: Credit decision making: SAS RTDM, neural network, XGBoost, LogReg + L1/L2 regularization

▪ Graph: Network and companies relationships

Disruption in Corporate decisioning

Full workflow automatization

Large corporatesCredit limit up to £ 20 mln*

Decision making without documents request

Automated Compliance & Security

Necessary conditions

• Have a current bank account

• Non project financing

Global fintech lending start-ups provide the limit up to £ 500 thousand


Interactivedashboard

Architecture

Client

Client user-interface

Docs

IR & text analysis

Internal data

Application Data

Compliance & Security check

External checks

Stop-lists, block-factors, simple rules

Credit Bureaus

Models call

Model repository

Credit parameters def

Batch processingBest-offer calculation

Data Lake

Batch processingRisk parameters calculation

Front-System

Sign-off

Offline environmentOffline model development

Money transferring

Credit product customization

SAS


Automated compliance & security check

Documents IR recognition, text analysis

Decision making rulesSAS

Company info Credit Decision

Compliance Division

Company name: LLC RenaissanceCEO: Michel BrownIndustry: Consumer goodsFounded: 25th of June 2014

Key figures▪ 10+ types of documents▪ 200+ entities▪ Accuracy of models NER 95%

• If [Date of the employment contract of the CEO] + [CEO term of work] < [Current Date], then “Decline”

• If [Official company registration number] ≠ [Company Number from Corporate Charter], then “Decline”

Double check*

*current Gini is about 95% which is enough to refuse the double-check

Internal data


Roadmap

DONE TO-BE-2019

▪ Money transferring automation

▪ Secured loans (collaterals)

▪ Credit limit up to £50 mln

▪ Expand sales channels

▪ Switch Revolving credit loans portfolio to “7 mins” technology

▪ Decision making and Documents preparation for Unsecured Corp lending for 7 minutes

▪ Credit limit up to £20 mln

▪ Preliminary Credit Risk parameters calculation

▪ Best Offer calculation (different risk parameters)

▪ Interactive client’s dashboard to customise the offer

▪ Atomized Compliance & Security checks

▪ Atomized credit decision workflow

Key figures

▪ Architecture landscape – 32 different systems

▪ 20 different risk models

▪ 9 months to design, implement and launch in prod

▪ 7 months to improve the system

▪ Launched in May 2019


Transforming Corporate banking with SAS AI/ML

DeployData

Traditional data Data Hub

Expert tuning

Front-office software Risk APIs powered with ML

Dep

loyEn

rich

Sto

re

Streaming data

ETL

Data

Ratios

Next generation Risk Management with Centralized Intelligent Automation

Faster decision, better decisions

Customer apps


Risk Modeling and Decisioning Credit Limit Management


SAS Credit Risk Modeling and Decisioning Pre-approved limits /Credit Limit Management

• With decreasing margins and higher capital and provisioning requirements, Financial Servicecompanies are focusing more on Utilization and Revenue optimization

• Organizations proactively offer various insensitive`s like Cash back, Points earning co-branded cards, various promotions and etc. Another angel used to increase sales whilekeeping capital and provisions growth rate at lower than average rate is Credit LimitManagement and Optimization.

Customer Control

Utilization

Risk level

Proactive Response Based Reactive

Issuer has complete control & unilaterally

determines population & increase amount

Line Increase action

requires explicit

customer consent

Customer requests for a

line increase & issuer

determines outcome

May be regulatory

restricted in a country

Additional

Considerations –

Marketing Costs &

Adverse Selection

Manual or Automated

decision making.

Includes temporary

line increase on

customer’s request


• Builds customer loyalty

• Encourages additional spending

• Generates higher, good quality revolving balances

in case of credit cards

• Reduces overall proportion of delinquent

balances

• 3 general types:

• Automated regular batch process

• Targeted marketing campaign (including up sell and

preapproved loans)

• Triggered by events in customer behaviour (e.g.

loan repayment, savings account opening,

increased card usage, web search data and etc.)

SAS Credit Risk Modeling and Decisioning Credit Limit Management

• Seen as a negative action

• Only accounts ready to be lost should be decreased

• Only effective on medium utilised accounts

• Only high risk accounts should be targeted

• Only decrease delinquent accounts (2+ cycles)

• Consider use of decrease warnings at 1+ cycles

• Decrease should be done to a percentage of

outstanding balance

• Small open limit should be left to cover any charges

• Decrease actions should be matched with

collections actions

Limit increase Limit decrease


SAS Credit Risk Modeling and Decisioning Credit Limit Management – Limit decrease example

1 2+

Behaviour

Score

Percent

Utilised

Delinquency

Level

VL L-M H+

L M-H

No Decrease to

105% of balanceNo NoNo

Decrease

• Here we see the 2+ cycle, Low to High utilized accounts are further segmented by risk.

• Only the high risk accounts are decreased. It is best practice to reduce the limit to slightly above theoutstanding balance so that they do not become over limit. In this example the standard of 105% is used.

• Accounts with low to medium risk do not have their limits reduced. This is because this action can causeadverse reactions.


SAS Credit Risk Modeling and Decisioning Credit Limit Management – Limit increase example (1/2)

6+

H

Behaviour

Score

H

L M H VH

M

VH L M H VH

No +500+400 +700 +800

YES NO

0

L

0

L H

L 130+

L M H

No +300No No No +300 +500+400 No No+600 No +600

Percent

Utilised

Credit

Limit

Months

Since Last Increase

Account

Up to Date?

Increase


SAS Credit Risk Modeling and Decisioning Credit Limit Management – Limit increase example (2/2)


SAS Credit Risk Modeling and Decisioning Credit Limit Management – Profit-based Approach

• Develop an existing account profitability model, so that profitability is known for each account

• Define and calculate revenues / Define and calculate costs

• Can be analyzed by characteristics: Behaviour score, Utilisation %, Spend, Risk and etc.

• Marginally increase limits for good, profitable accounts in medium score ranges - Increase profitability

• Low risk customers

• Less likely to use the increased limit

• Low utilisation

• Increased limits will be utilised by the ‘bads’ within the higher score ranges

• Cap the top-end limits

• Allow usage by low-risk ‘goods’

• Restrict usage (bad debt) for low-risk ‘bads’



• Increased revenue = increased profit

• Identify core revenue drivers on credit cards

• Usage-based

• Facility-based

• Payment-based

• Enquiry-based

• Ancillary products

• Consider increasing the initial usage

• Reduced costs = increased profit

• Identify primary cost drivers on credit cards

• Marketing costs

• Acquisition / Application costs

• Account cost

• Delinquency and write-offs

• Cost of funds

• Statement - postage cost

• Incur costs only if required

• Low-risk customers

• Profitable customers


SAS Credit Risk Modeling and Decisioning Credit Limit Management – Account profitability

Revenues 150 150 275 275

- Interest / service fees 100 100 200 200

- Interchange fees 30 30 50 50

- Insurance 20 20 25 25

Costs 130 273 126 419

- Application cost 80 80 80 80

- Account cost 30 30 30 30

- Marketing cost 7 7 3 3

- Delinquency cost 3 6 3 6

- Actual write-off 0 150 0 300

- Projected write-off 10 0 10 0

Account Profit 20 -123 149 -144

Cost: income ratio 0.9 1.8 0.5 1.5

Account 1 Account 3 Account 4

Low usage, Low

delinquency

High usage,

Low

delinquency

High usage,

High

delinquency

Low usage, High

delinquency

Account 2



Behaviour score

Profitability by

score curve3

1 Bad debt losses

- ‘Good’ & ‘bad’ usage

Bad debt losses

- Low ‘good’ usage

- Extensive ‘bad’ usage

3 Key areas of the profit curve

2Restricted usage

- ‘Good’ & ‘bad’ usage

- Low proportion of bads

- High proportion of goods

- Credit limits not restrictive



Behaviour score

Profitability by

score curve

Lowest risk

accounts

are not using

the high

limits

Is profitability maximised for these groups?



Behaviour score

Profitability by

score curve

Cap top-end limitsAllow increase on good balances



2004 © PIC Solutions

Behaviour score

Profitability by

score curve

?

Is the gradient correct?

Using profitability, various limits can

be champion-challenged to determine

the slope of the line.



Behaviour score

Champion-challenge capping

& marginal increases



A B C

TIME

High

Euro

AFTER

Profit-based account management

• Improves nominal profit

• Extends account activity

V

T


SAS Credit Risk Modeling and Decisioning Credit Limit Management - Influence Diagram

• For each customer and for each potential additional line increase, set of models executed to predict: Response rate, Utilization, Revenue, Loss, NPV and etc.


SAS Credit Risk Modeling and Decisioning Credit Limit Management – The challenge

• These models are combined to come up with the “ideal” additional line increase to offer to each account

• However, if you add up the “ideal” line amount for each account, the sum amounts to more exposurethan organization eager to issues due to capital, provisions and other costs constraints

• Therefore, the challenge is to come up with the most efficient way of reducing the overall exposure (andother measures) without giving up too much of the predicted NPV from the line

• Making account level line decisions with portfolio level constraints requires optimization


SAS Credit Risk Modeling and Decisioning Credit Limit Management – Optimization

Answers we are looking for (examples):

? How to pre-approve more customers

? How to limit credit risk and in same time

? How new sales will impact risk based pricing and in same time

? How to protect customers (indebtedness) and in same time

? How to control product portfolio risk and in same time

? What to offer to the customers to sell successfully and in same time

? What should we sell to maximize profit and in same time

? What is my portfolio business potential and in same time...

}}

Target

Constrains

Variables

Linear optimization … Maximizing target while keeping constrains under control


SAS Credit Risk Modeling and Decisioning Credit Limit Management – Optimization

Constrained –reflecting portfolio level constraints of

overall exposure and loss ratio

Unconstrained – each account receiving best

line individually

Note that Expected Profit has actually gone down by almost $2M but the overall exposure has gone down by $700MM!!


Risk Modeling and DecisioningEarly warnings


Early warningsBusiness approach

Key goal of early warning signals is to inform a bank about customer credit deterioration, so that a bank can

take actions lowering probability and volume of potential loss. Usually delivered as a reports, but requires an

extensive underlying foundation / infrastructure.

Data:

• Internal (industry analysis, media monitoring, trend in financial ratios, behavioral information, e.g.

corporate transactions, transfers; for households: salary amount, ATM transactions, merchant types)

• External (traditional, e.g. credit history bureau, government databases; non-traditional, e.g. telco,

social networks)

Ways to structure:

• Predictive models (EW models, PD models, EL models, Collection models, Attrition models, Fraud

models)

• Business rules (where models are not feasible for use and/or to compliment models)


Early warningsTechnical structure example


Early warningsTypical actions to take

Depending on previous customer relationship history, customer segment and reasons of credit

deterioration actions to be taken by bank significantly vary by type.

Typical set of actions include:

• Approaching customer to verify credit deterioration case and drivers of it

• Depending on the reason pushing either into debt collection or restructuring

• Actions towards exiting unused limits

• Proposing and discussing with a customer available options for restructuring

• Executing and monitoring post-restructuring situation



Risk Modeling and DecisioningCredit Risk Modeling lifecycle


Collaborate Across

Skill Levels

CompareMachineLearning

Algorithms

Monitor & Refresh

More ComplexData

Auto-Tune

More Granular Segments

FastIn-Memory

Multi-threadedTechnology

StreamlineData

Preparation

More FeaturesMore Models

IterateFaster


Risk Modeling lifecycle

1. Needs Identification

2. Data preparation, variable selection,

model development

3. Independent Review & Approval

4. Model Implementation

5. Model Execution / Usage

6. On - Going Monitoring

7. Periodic Review &

Validation, Ad-hoc review

Model Request Process (Intended Use, Business Impacts, Business & Technical Requirements)

Data collection and Data quality Variable creation and Variable selection Model development and selection Validation and backtesting Model Documentation and Development

Methodology (Data Sources & Variables Used, Model Assessment & Selection Criteria, Scoring Code)

Model Limitations & Assumptions

Verification and Assessment Report on Model Development Process

Findings, Action Plans, and Correspondence Records Between Stakeholders

Model Risk Materiality Assessments Approvals and Sign-Offs Record Inventory of Models, Non-Models, Policies, etc.

(Maintained and Updated by all lines of defense)

Implementation Documentation (Testing, Systems Integration)

Model Usage Request (Details on Usage, Business Justification)

Model Execution History Feedbacks on Model Usage

Performance Monitoring Reports Escalation Report

Periodic Review on Models (Consistent with the Model Risk Materiality Assessments)

Model Change Request (Modification & Decommissioning)

Documentation on Model Versions Model Inventory (Models Under Development & In

Production)


TIME to VALUE


SkillLatency

Lost

Val

ue

▪ Producing a new model or

adjusting an existing model

for the business often takes

too long to meet fast

changing markets.

▪ Complexity is added as

many stakeholders are

involved in the predictive

analytics process.

▪ Big data is adding to the

complexity.

▪ Implementation of a

process model is needed to

provide fast, repeatable

and high-quality results

Value

Time

DataLatency

DeploymentLatency

DecisionLatency

Lost Time

ModelingLatency

EvaluationLatency

Risk Modeling & Decisioning lifecycle and challenges


Credit Risk Modeling lifecycle and challenges

• Different data definitions/ taxonomies between business, IT and risk

units. Semi manual data collection from multiple systems

• Poor data quality and complex data transformation

• Long modeling lifecycle (from decision to develop until going into

production). Challenges with IP retention and succession when

employees leave

• Long, time and resource consuming loan origination process.

Incoherent customer view and misbalanced channel interactions

• Insufficient IT resources and infrastructure for robust model

implementation, execution and monitoring. High IT total cost of

ownership

• Solutions and models provided by external vendors are black boxes

and always require timely and expensive external involvement

IDENTIFY /

FORMULATE

PROBLEM

DATA

PREPARATION

DATA

EXPLORATION

TRANSFORM

& SELECT

BUILD

MODEL

VALIDATE

MODEL

DEPLOY

MODEL/PER

FORM

SCORING

EVALUATE /

MONITOR

RESULTS


Credit Risk Modeling lifecycle

Data Extract and Variable Selection

Model Development and Specification

BacktestingModel Deployment

and Scoring

Model Performance Tracking and Monitoring


232

Credit risk modeling - Data Gathering and ABT preparation

First phase of modeling project involves collecting and collating all datarelevant for a specific business task from disparate data sources andorganizing them. It includes merging and matching records for differentproducts, channels and systems in order to create a single customer view.

Within next step users write code / use GUI to read data and create dataset (“Analytics Base Table”) with which to develop specific model. Keytasks involved include:

• applying inclusion and exclusion conditions/filters to collected data,

• deriving and specifying “target variable(s)” – outcome model should

predict,

• specifying input variables to include for prediction power

exploration,

• deriving new input variables for prediction power exploration,

• specifying performance windows and sample windows,

• segmenting and sampling data.An

alyt

ics

Bas

e Ta

ble


233

Data integration and Data quality

• Intuitive point-and-click designer tool for the developer

• Quickly identify inputs and outputs and create business rules in metadata

• Push processing down to the database for ELT execution

• More than 300 predefined table and column-level transformations

ModelData

Rules


SAS® Data Access

Sybase IQOracle

Exadata

Sybase Oracle IBM DB2/UDB

Teradata Netezza

SAP BW

Aster DataVertica

PostgreSQL

OLE DB

SAP R3HadoopCloudera

Impala

MS SQL Server

SAP HANA

Text HDFSMS AccessExcelXML

MySQL

CSV

SAS SPDS

SAS datasets ODBC

Mainframe data

Direct access to a wide variety of sources and

file formats


Analytical Base Tables – preparing data for modeling









Reuse and share variables, filters and other collateral within modeling team




SAS® Credit Scoring - Enterprise Miner™

• Broad set of tools to support the complete data

mining process

• Build models:

- Application/Behavior Scoring (Rating)

- Parameter Estimation (PD, LGD, CCF)

- Collection scoring

• An open, extensible design for ultimate flexibility

- GUI + SAS Code Node + Extension Nodes

- Optimized score code and data transformations for

Batch or Real Time processing

- Champion / Challenger

- Able to create C and Java score code


SAS Enterprise Miner - Key features

• Integration with PMML, R, Python• Supervised Techniques

Regression / Credit ScoringGradient Boosting / Decision TreesNeural Networks / Bayesian NetworksSupport Vector Machines / etc.

• Unsupervised TechniquesClustering, Dimension reductionAssociations, Principal Components, etc.

• Ensembles• High-Performance Analytics• Survival Analysis• Time Series Data Mining• Group processing for multiple targets and

segments

• Analytical Data Prep• Data Exploration and Discovery• Ensemble Modeling• Model assessment

• Replace missing values• Interactive binning of input variables• Reassign and consolidate levels of input

variables• Transform variables to create new ones• User defined rules to define values for

outcome variables and paths to outcomes

• Access structured and unstructured data• Streaming data or data at rest• Data sampling and partitioning• Data filtering, including outliers• Time series data preparation and analysis• Create ad hoc data-driven rules and policies

• Easy-to-use Graphics Explore wizard Interactively linked plots, charts and tables

• Descriptive Analytics• Variable distribution and summary statistics• Univariate and Bivariate statistics and plots• Segment profile and interactive plots


SAS® Credit Scoring - Scoring nodes

• Automatic and interactive variable grouping

• Computes Weights of Evidence (WOE)

• Handle WOE values in the presence of frozen or imported grouping definitions

• GINI and Information Values for variable selection

• Scorecard construction

• Logistic regression based using WOE or group variables as inputs

• Parameterized score points scaling

• Assessment statistics and charts

• Avoid recalculation or overwritten of scorecard points if node is flagged to re-run.

• Reject inference

• Through the door impact analysis

• Integrate the results with the SAS® Credit Scoring


Open source integration

R MODELS

SAS MODELS


245

Model Specification


Model Performance Monitoring


Model Performance Monitoring








254



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