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Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Page 1: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

Ris

erv

ato

& c

on

fid

en

zia

le

Istanbul, 18 December 2014

Rating system validation, LGD model and Risk appetite

Page 2: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

| 2 |

AGENDA

Rating system validation

LGD models development

Early warning for risk appetite

Page 4: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Calculation of capital minimum requirements and risk parameters

PD, LGD and EAD are the fundamental risk parameters for evaluating the credit risk level and consequently defining the minimal capital requirements

PD

Credit risk Parameters

EADLGD

Reflects the Probability of Default of the counterparty

Reflects the expected % of

loss on a facility after default

Reflects the expected amount of exposure at the time of the default

Internal Rating Based Foundations

Internal rating

Supervisory values (*)

Internal Rating Based Advanced

Standard

Internal rating

Internal LGD Internal EAD

Supervisory values

External rating

Supervisory values (*)

Supervisory values

Page 5: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Validation Process

Regulatory Back-ground and Framework

Accuracy ConsistencyInternal Validation

ProcessRisk estimation

system

Assessment on internal rating system performance (backtesting) but for IRB banks.

Judgmental analysis on methodological approach applied in the rating system in comparison to the best practices.

Application and use of rating in all business areas (use test) and in the firm-wide decisioning governance.

Constant monitoring on the quality of all the informative tools performing in the rating system.

The Basel Committee defines widely the concept of validation..

“…500 : Banks (adopting IRB approach) must have a robust system in place to validate the accuracy and consistency of rating systems, processes, and the estimation of all relevant risk components. A bank must demonstrate to its supervisor that the internal validation process enables it to assess the performance of internal rating and risk estimation systems consistently and meaningfully.”

Page 6: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Validation Process

Validation Process in the Basel Scheme

Internal Validation

Supervisory Examination

Validation of rating system

Validation of rating process

Internal Use by credit officers

Reporting and problem handling

Data qualityRisk Components

BenchmarkingBacktesting

PD LGD EAD

Model Design

IT SYSTEM

The scheme below shows key components of Validation Process in Basel Scheme

Page 7: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Validation Process

Validation Process in the Basel Scheme

The scheme below shows major actors and “theoretical” steps of the validation process

Governance /Reporting

Information Systems

Model design

Use test

Data

Risk components

VALIDATION OF THE RATING SYSTEMS

Supervisor examination

Internal audit/compliance

Validation/Authorization

dossier

VALIDATION OF THE RATING PROCESS

Individual bank Supervisor

Regulatory capital

RWA= x8%

EAD 1,06xxPD LGD ;f (

x

(

Credit approval

Credit risk management perspective

Regulatory perspective

M

(

;

Credit monitoring

……

Authorization to the use of IRB

parameters

Default risk -PD

Loss given default

LGD

IRB parameters

Exposure at default

EAD

Page 8: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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PDDEVELOPMENT

Basel Standars

IMPLEMENTATION

LGD EAD

Internal credit models

QualitativeVALIDATION

The internal credit models system should be validated in order to be compliant to Basel Standards; the validation process takes into account two different areas: quantitative and qualitative

Validation of internal credit risk models

Quantitative

Page 9: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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The model’s design is validated on the basis of the rating model’s documentation. In this context, the scope, transparency and completeness of documentation are already essential validation criteria

• Delineation criteria for the rating segment• Description of the rating method/model

type/model architecture used• Reason for selecting a specific model type• Completeness of the (best practice) criteria

used in the model• Data set used in statistical rating

development• Quality assurance for the data set• Model development procedure• Quality assurance/validation during model

development• Documentation of all model functions• Calibration of model output to default

probabilities

Data qualityModel design Internal Use test

In statistical models, data quality stands out as a goodness-of-fit criterion even during model development. Moreover, a comprehensive data set is an essential prerequisite for quantitative validation

• Completeness of data in order to ensure that the rating determined is comprehensible

• Volume of available data, especially data histories

• Representativity of the samples used for model development and validation

• Data sources• Measures taken to ensure quality and

cleanse raw data.

Validating the internal use of the rating models (use test) refers to the actual integration of rating procedures and results into the banks in-house risk management and reporting systems. With regard to internal use, the essentialaspects of the requirements imposed on banks using the IRB approach under Basel II

• Design of the banks internal processes which interface with the rating procedure

• as well as their inclusion in organizational guidelines

• Use of the rating in risk management (in credit decision-making, risk-based

• pricing, rating-based competence systems, rating-based limit systems, etc.)

• Conformity of the rating procedures with the banks credit risk strategy

• Functional separation of responsibility for ratings from the front office

• Employee qualifications• User acceptance of the procedure• The users ability to exercise freedom of

interpretation in the rating procedure

Validation – Qualitative Area

The qualitative test is meant to certify the relevance and quality of the data used and the correct application of the quantitative methods. A rating process should only be carried out if the internal credit models system receives a positive assessment during the qualitative test

Page 10: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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The term discriminatory power refers to

the fundamental ability of a rating

model to differentiate between bad

(credit default occurs) and good (credit

default not occurs) cases

stabilityperformance calibration

The assignment of default

probabilities to a rating models

output is referred to as calibration.

The quality of calibration depends

on the degree to which the default

probabilities predicted by the

rating model match the default

rates actually realized

• Frequency Distribution of Good and Bad Cases

• Transaction matrix

• Binomial Test• Chi-Square Test• PD actual vs PD fitted

• Accuracy Ratio• ROC Curve• Errors the 1° and 2° type• Cumulative frequency bad /good cases• Denisty function for bad/good cases• Kolmogorov-Smirnov Test• CAP Curve

• Changes in the discriminatory power of a

rating model given forecasting horizons of

varying length and changes in

discriminatory power as loans become

older

• Changes in the general conditions

underlying the use of the model and their

effects on individual model parameters

and on the results the model generates

benchmark

back-testing

Validation – Quantitative Area

Quantitative validation is required for all credit models in use and it should primarily be performed with the data gained through use of the model in the bank. A quantitative test could provide the information concerning the performance of the credit models

Page 11: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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AGENDA

Rating system validation

LGD models development

Early warning for risk appetite

Page 12: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Approaches to LGD estimation

There are broadly 2 ways of measuring LGD

Focus of the presentation

MARKETLGD

WORKOUTLGD

It makes use of Market Data

The Loss Value of the market assets after the default event, is the base for the LGD Estimation

It needs a liquid asset market before and after default

CorporateSMERetail

It makes use of Internal Data

It needs as first step the analytic calculation of the LGD observed on historical default events, on the basis of expenses, charges and recoveries, observed in a period of time

It needs the collection of the cash flow characterizing the contracts after the default event, and the adoption of appropriate actualization hypothesis.

-Sub-Task- -Description-

Large CorporateFinancial Instit.Sovereign

-Counterparties-

Page 13: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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The loss is measured according to an “economic logic” (not accounting) considering the effect of time in the loan recovery

The perimeter is represented by the default positions which have completed their process of debt recovery

LGD is usually defined as the ratio of losses to exposure at default, and it considers:

the set of estimated recovery cash flows to be received by the lender resulting from the workout and/or collections process, properly discounted

Workout expenses (collections, legal, etc)

Downturn: LGD must reflect an appraisal of the expected loss on a transaction in case of default and in a downturn scenario (periods where PD are above the average).

Approaches to LGD estimation

What do we know and we expect about Loss Given default?

A

Recovery flows

Exposure at default (EAD)

B

LGD=

EAD – discounted recovery flows + discounted expenses

EAD

A – B + C

A

=

Methodological foundations

Expenses

C

Closing of the collection actions

Definition

Defa

ult

Page 14: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Approaches to LGD estimation

Workout estimation can approached differently in terms of Model Design

Performing DEFAULTPerforming

LOSSLGDFull

model

MODEL FULLY COMPLIANT AND ALIGNED WITH THE RECENT EVOLUTIONS OF THE REGULATOR

WRITE-OFF 1BLGD

1BLGD

crcure

Default B2LOSS danger

Performing

LOSS

cr1

p

p1

2

11 11

B

BB EAD

EADcrppLGDLGD

2BEAD 1BEAD

PerformingPerforming

Cure rate (Partial) Model

Page 15: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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High level project outiline – LGD workout estimation

Preliminary assessment

Credit portfolio analysis

Credit Management Process analysis

■ Analysis of the active loan portfolio with a breakdown by:

I. Product

II.Collateral

III.…

■ Interviews with process owners

I.Debt collection

■ Data extractions from identified archives

■ Construction of Development Data mart

■ Estimation of LGD parameters

Gap analysis

Identifying the relevant dimensions of analysis

Identification of Key processes

characteristics Any limitations

or constraints to the estimation process

Pro

ject

ph

ases

Ou

tpu

tA

cti

vit

y

To properly address LGD estimation, CRIF proposes a project framework composed of 6 worksteps

Modelingframework

■ Defining the methodological framework for calculating LGD in compliance with the Basel requirements

Technical meetings on LGD modeling methodologyin line with the process and data characteristics

Data extractions and risk parameters estimation

Improvement expectations

■ Identification of areas for improvement of the LGD estimates

■ Prioritization and planning of future activities

Analysis which aims to identify main areas of improvement and gap towards local regulatory requirements framework and international best practice

Data request Presentation of

the results (estimates, performance,.)

Proposed action plan

Page 16: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Project outiline –LGD workout estimation

Preliminary assessment

Credit portfolio analysis

Credit Management Process analysis

■ Analysis of the active loan portfolio with a breakdown by:

I. Product

II.Collateral

III.…

■ Interviews with process owners

I. Debt collection

■ Data extractions from identified archives

■ Construction of Development Data mart

■ Estimation of LGD parameters

Gap analysisIdentifying the relevant dimensions of analysis

Identification of Key processes

characteristics Any limitations or

constraints to the estimation process

Pro

ject

ph

ases

Ou

tpu

tA

cti

vit

y

High level description of Preliminary Assessment phase

Modelingframework

■ Defining the methodological framework for calculating LGD in compliance with the BII requirements

Technical meetings on LGD modeling methodologyin line with the process and data characteristics

Data extractions and risk parameters estimation

Improvement expectations

■ Identification of areas for improvement of the LGD estimates

■ Prioritization and planning of future activities

Default definition Discount rate Analysis of

the expenses

Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice

Data request Presentation of

the results (estimates, performance,.)

Proposed action plan

Downturn LGD

Areas of investigation

s

Page 17: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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It allows the use of relevant factors: statistically meaningful model variables

It easily allows the usage of continuous explanatory variables

It provides a measure of the marginal contribution of each variable as well as their interaction

High level project outiline -workout estimation

Preliminary assessment

Credit portfolio analysis

Credit Management Process analysis

■ Analysis of the active loan portfolio with a breakdown by:

I. Product

II.Collateral

III.…

■ Interviews with process owners

I. Debt collection

■ Data extractions from identified archives

■ Construction of Development Data mart

■ Estimation of LGD parameters

Gap analysis Identifying the relevant dimensions of analysis

Identification of Key processes

characteristics Constraints to the

estimation process

Pro

ject

ph

ases

Ou

tpu

tA

cti

vit

y

High level description of Modeling framework phase

Modelingframework

■ Defining the methodological framework for calculating LGD in compliance with the BII requirements

Technical meetings on LGD modeling methodology

Data extractions and risk parameters estimation

Improvement expectations

■ Identification of areas for improvement of the LGD estimates

■ Prioritization and planning of future activities

Traditional/empirical approach

Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice

Data request Presentation of

the results (estimates, performance,.)

Proposed action plan

Approach to LGD estimation Econometric model

It is based on empirical evidences (descriptive statistics)

It does not give evidence of the statistical significance of adopted risk drivers

It is strongly affected by sample size

Page 18: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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High level project outiline -workout estimation

Preliminary assessment

Credit portfolio analysis

Credit Management Process analysis

■ Analysis of the active loan portfolio with a breakdown by:

I. Product

II.Collateral

III.…

■ Interviews with process owners

I. Debt collection

■ Data extractions from identified archives

■ Construction of Development Data mart

■ Estimation of LGD parameters

Gap analysis Identifying the relevant dimensions of analysis

Identification of Key processes

characteristics Any limitations or

constraints to the estimation process

Pro

ject

ph

ases

Ou

tpu

tA

cti

vit

y

High level description of Data extractions and Risk Parameters estimation phase

Modelingframework

■ Defining the methodological framework for calculating LGD in compliance with the BII requirements

Technical meetings on LGD modeling methodologyin line with the process and data characteristics

Data extractions and risk parameters estimation

Improvement expectations

■ Identification of areas for improvement of the LGD estimates

■ Prioritization and planning of future activities

DM Design DM Validation

LGD Estimation

StructuralSpecification

Data Requirements definitions

Data mart building

Data QualityCriteria definitions

CorrectiveActions

Data Aggregation Criteria definition

LGD Workout

VintageCorrection

Final LGD Model

Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice

Data request Presentation of

the results (estimates, performance,.)

Proposed action plan

Key milestones

Page 19: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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High level project outiline -workout estimation

Preliminary assessment

Credit portfolio analysis

Credit Management Process analysis

■ Analysis of the active loan portfolio with a breakdown by:

I. Product

II.Collateral

III.…

■ Interviews with process owners

I. Debt collection

■ Data extractions from identified archives

■ Construction of Development Data mart

■ Estimation of LGD parameters

Gap analysis Identifying the relevant dimensions of analysis

Identification of Key processes

characteristics Any limitations or

constraints to the estimation process

Pro

ject

ph

ases

Ou

tpu

tA

cti

vit

y

High level description of Data extractions and Risk Parameters estimation phase

Modelingframework

■ Defining the methodological framework for calculating LGD in compliance with the BII requirements

Technical meetings on LGD modeling methodologyin line with the process and data characteristics

Data extractions and risk parameters estimation

Improvement expectations

■ Identification of areas for improvement of the LGD estimates

■ Prioritization and planning of future activities

Segments Type of facilityWith/without

collateral/guarantees

Identifying main areas of improvement and gap towards local regulatory requirements framework and international best practice

Data request Presentation of

the results (estimates, performance,.)

Proposed action plan

Segmentation criteria

The LGD model will be differentiated according to any segmentation criteria in order to capture the peculiarities and specific business practices in debt collection management

•Retail•SME•Corporate

•Unsecured•Personal •Mortgages•…

•Committed•Uncommitted• …

Page 20: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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AGENDA

Rating system validation

LGD models development

Early warning for risk appetite

Page 21: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Overview

The recent financial crisis has shown that an effective RAF is an useful and relevant way to obtain a good Governance of a financial institution

For this reason national and international regulators are placing greater attention to this topic in order to ensure that there is consistency between the risks actually undertaken and those perceived by decision-making bodies of the Institute

Overview

The RAF has the objective to support the corporate bodies for the decision making in order to increase the awarness about the risks linked to the business model

The financial institutions should develop an effective RAF that is institution-specific and that reflects its business model and organisation, as well as to enable financial institutions to adapt to the changing economic and regulatory environment in order to manage new types of risk

Objective

Capital Risk

Strategy

Shareholders

Rating agencies

Customers

Regulator

Top management

The Risk Appetite Framework (RAF) defines the amount and the type of risks that the financial institution wants to assume according to its ability and to the strategic objectives and business that it has agreed

Page 22: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Key Definitions

The maximum level of risk that the financial institution can assume given its current level of resources before breaching constraints determined by regulatory requirements or other restrictions imposed by the shareholders

Which is the level of risk that the bank can undertake?

dimension 1

dimension 3dimension 4

dimension 2dimension 5

Risk Capacity

Risk Profile

It is the risk actually undertaken, measured in a certain moment

Which is the level of risk that the bank is undertaking at the moment?

Risk Appetite

The aggregate level and types of risk a financial institution is willing to assume within its capacity to achieve its strategic objectives and business plan

Which is the level of risk that the bank wants to undertake?

The RAF is an useful tool that is able to synthesize the risk profile of an Institution. It expresses the capacity (Risk Capacity), the appetite (Risk Appetite) and the actual amount of risk (Risk Profile) undertook both at organization level and both for each individual business unit / type of risk (size)

Page 23: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Risk Appetite Framework

Process to define the Risk Appetite(1)

(1) The process should be linked to the size and the proprety of the financial institution

Methodology definition

Approval process

Risk LimitsReporting

and monitoring

Early warning

1

2

34

5Actors involved• Board• Commitees• Risk

Management• Finance• Business Unit

1. Methodology definition of RA: process to definine the position of the target risk appetite in terms of quality and quantity, based on both internal and external aspects

2. Approval process of RA: determination of the phases and the key players to approve the placement of the risk appetite defined

3. Conversion of RA in Risk Limits: application of the position of the RA in the ordinary management of the institute by establishing limits and specific processes

4. Reporting and monitoring: development and definition, at the overall level, of a system of analysis and communication of trend risks that is specific to business unit and type of risk

5. Early warning: development of a system of risk thresholds and definition of the means to monitor these thresholds, in addition to the definition of the underlying management process

The development of an effective Risk Appetite is an interactive and circular process in order to achieve a continuous improvement of the methodologies, governance processes and operational tools. The Early Warning system represents the main tool useful for monitoring the correct application of the RAF Framework

FOCUS OF THE NEXT SLIDES

Page 24: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Framework

Early Warning Systems have to be indented within the ongoing monitoring of the performing (non delinquent) credits

Anticipating, as much as possible, situations of potential deterioration providing to the managers a solution to prevent the default of the counterparty or limit the damage. (Control of the First Level)

Objectives1

Perimeter2Performing credits at high risk, or credit lines not yet expired/exceed but showing a higher degree of risk (i.e: fault signals) or because of a review by the relationship manager on other performing loans.

Tools3Warning signals and internal behavioral indicators or external data integrated with internal rating (where available) based on statistics and implemented as a decisional tree.

IT4Credit Monitoring solutions and sophisticated systems able to produce a monitoring report connection with a “laboratory” environment; new outsourcing services (EWAAS)

Page 25: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Objectives

• As within any decision-making system, the Early Warning system requires a definition of a target

event to be developed and subsequently measured.

• Since the objective is to anticipate the transition to the default, the target event is designed to

intercept a 'preliminary‘ stage which is made to match with the "deterioration" of the positions,

combining quantitative elements (increased days overrun) with “managerial” elements (worsening

of the Customer position).

• From the operational point of view the target of the "deterioration" of the positions can be

analyzed through the exploration of "roll rate", as shown in the following example:

• Within this framework, Early Warning models’ target type deviates significantly from the definition

of “default” both in terms of events and time horizon considered.

PerformingUnder

monitoring Credit control Past due 90 Past due 180 Ordinary breach Restuctured Credit Severe breach Inconclusive

Performing 96%

Under monitoring 10% 80%

Credit control 75%

t0 statusWorst status over last 6 months

5%

4%

10%

20%

Page 26: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Perimeter

The scope refers to a segmentation of the “Banking book”, well represented (in reverse order of severity)

Handling class DescriptionA. Trial, legal handling Credit collection through legal actions

B. Deteriorated position Substandard loans, past due, restructured

C. Breaches Formally performing, but showing significant breaches (i.e. expired or exceeded credit)

D. Performing high risk Credit lines not yet expired/exceed but showing a higher degree of risk (i.e: fault signals as early warning; classification under observation for other reasons; speculative grade rating; etc.).

E. Performing high risk Credit lines not yet expired/exceed for which has been given rise to a review of the relationship (increase of overdraft due to a prolonged tension in the use, increased maturity, etc.)

F. Performing All other performing credits

Early Warning Sysyetms usually take into account cases D, E and F

Page 27: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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• «Performance» definition

• Historical indicators data analysis

• Implementation of a decision tree

• Supplement the decision tree with breaches and Business evaluation

• Grouping breaches in order to prioritize the correction actions to be carried out («Traffic light»)

• Workload measurement and evaluation

• Functional document aimed at supporting the actual implementation

• It’s a shared process with credit expert, sales network and external suppliers

• Drafting a Long List with different priority levels (High, Medium, Low) for each (Corporate, SME and Retail)

• Availability analysis of the internal/external data sources

• Technical analysis support

Acti

vit

ies

Delivera

ble

s

BacktestingModel developmentLong list definition «Traffic light» design Implementation

1 2 3 4 5

• Choosineg an «out of time» sample

• Data Analysis

• Performance measurement and evaluation

Approach – Designing a decision tree

Page 28: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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The long list of indicators is formed based on ”groups” which differ in information source used

Sources

GROUP Sources

STAGESInternal "stage" (Handling status - custom classification)

PUBLIC INFORMATIONAll public information, based on their availability on the market

RISK INDICATORS External sources info (i.e. Credit Bureau) INTERNAL INFO Whatever available within the BankBALANCE SHEETS If/where publicly availableCOMPOSED INDEXES Any available index / indicator already computed

Page 29: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Indicators

• Exceeding the granted limit (from Credit Bureau)

When the Customer is in the situation of exceeding a limit granted by a competitor Bank

• Trend of usage for overdrafts

When the Customer tends to use the overdraft often close to its limit

• Persistent breaches

Exceeding a certain ‘%’ of the overdraft limit for more than a ‘X’number of days:

- [a] number of days

- [b] ‘%’ of excess of the overdraft limit

1. “Light” breach – if [a] < 90 days AND [b] <= 3%

2. “Medium” breach – if [a] > 90 days OR [b] > 3%

3. “Heavy” breach – if [a] > 90 days AND [b] > 3%

E X A M P L E

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Actions execution and continuous monitoring

Analysis and definition proposal of the handling class, action plan and provisions

1

E X A M P L E

• All operations in the credit monitoring are based on an iterative process, starting from the reports of

early warning or recognition of specific anomalies. They are divided into the following (theoretical)

phases:

Early Warning report / Breaches detection

2

3

Decision about of the handling class, action plan and provisions4

The IT solutions - Design

Page 31: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Integration with other internal

procedures (Core Banking, CRM, …)

Link to other procedures

(Core Banking, CRM, …)

Regular

Monitor

Initial past due iniziale

Past due 30

Past due 90 / 180

Anomalies

Restructured

Breaches

Charge off

Actions

Classification

Ordinary handling

Special Credits

Special Credits

Branch, Regular Credits

Branch, Non-regular Credits

Non-regular Credits

Branch

Branch

Non-regular Credits

Early Warning Credit Monitoring System

Perf

orm

ing

Defa

ult

Segment definition(Retail, SME, Corporate)

Decision processMonitoring

external toolsOther tools

Analysis and definition

E X A M P L E

Action plan Provision

The IT solution – a functional example

Page 32: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Performance measurement

The presence of a laboratory environment, where basic information, indicators, suggested actions, timing and methods of application and quantitative results are historically tracked in order to monitor and correct the Early Warning System, is a pre-condition for the evolution of the system itself.

EW Data Mart

Actions execution and continuous monitoring

Analysis and definition proposal of the handling class, action plan and provisions

1

Early Warning report / Breaches detection

2

3

Decision about of the handling class, action plan and provisions

4

Info out of the process but potentially useful

Provisions/corrections

Effectiveness BenchmarkingPrediction capacity

Simulation New system

The IT solutions – Laboratory

Page 33: Riservato & confidenziale Istanbul, 18 December 2014 Rating system validation, LGD model and Risk appetite

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Risk Management Practice

Crif Decision Solutions

Via M. Fantin 1-340131 Bologna

Tel.: + 39 051 4176111Fax.: + 39 051 4176010