Lambros Tsolkas Partner Credit Analytics · customer behaviour • Product pricing & re-pricing • Bad strategies and wrong segmentation of for the Loss making credit portfolio •

Lambros Tsolkas

Partner

Credit Analytics

Athens, November 9th, 2012

Copyright © 2012 Accenture All Rights Reserved. 2

European Credit Market

Despite economic headwinds that still impact the economic

and credit sector recovery, some positive signs are emerging

Source: ECB statistics

The volume of credit has

declined in Spain, UK and

obviously in Greece,

where the private sector

deleveraged due to high

debt levels and worries of

future economic

uncertainty fuelled by

sovereign debt crisis

Lending trend in Europe

(2007-2010, 2007=100)

90

95

100

105

110

115

120

125

130

135

140

145

2007 2008 2009 2010 2011 2012 2013

Nordics

CEE

ES

IT

DE

FR

UK


Credit Challenges

Credit market is currently facing four main challenges

• Large foreclosure &

write downs

• Failure of the

collection strategy

• Increased

delinquency

Credit losses

• Internal & external

frauds

• Changing fraud

patterns

• Increased fraud

losses

• Failure to detect the

fraud incidents

Fraud

• Failure to

understand

customer behaviour

• Product pricing &

re-pricing

• Bad strategies and

wrong

segmentation of

customers

Loss making credit

portfolio

• New addition to the

existing regulations

• Existing regulation

becoming strict in

adherence and

compliance

• Poor data quality

process and

technology to adapt

for the change

Regulatory

compliance

1 2 3 4


Source: Accenture Risk Analytics Study (2012)

High performing banks are increasing their risk analytics

investments in order to address the challenges efficiently

Change in investment

1% 1% 1%

10%

24%

49%

15%

Decrease greater than 20%

Decrease 10%-19,9%

Decrease 0%-9,9%

No change

Increase greater than 20%

Increase 10%-19,9%

Increase 0%-9,9%

30%

Area of risk analytics investments focus

32%

39%

55%

50%

58% 58% 56%

Data quality and sourcing

Software

Staffing

Reporting

Systems integration

Management use and acceptance

Business rules development

Modeling

Risk analytics investment Risk analytics investment focus

% % 10% - 20% increase on risk analytics investments


Credit Value Chain

Analytics will have significant role throughout credit value

chain

Credit Risk Mitigation

Credit policies

Credit Risk Management

Approval

and review

Early warning &

pre-collections

Marketing

and sales Servicing

Product

offering

Collections

& recovery

Credit Scoring Analytics

1

Fraud Analytics

2

• Credit Monitoring

Model

• Customer

Segmentation &

Credit Re-structuring

3

Recovery Management

4


Analytics – Credit Scoring Methodologies

Risk scores predict the likelihood of payment, default or

repayment taking into consideration a variety of variables

Acquisition credit scoring Internal behavioral scoring Collections scoring

Independent Variables

• Credit bureau tradeline history

• Past payment performance

• Depth of credit background

• Amount of existing outstanding credit

• Derogatory information – eg.

Collections, bankruptcy

Dependent Variables

• Pay vs. No-pay

• Response vs. No-response

• Churn/Attrition vs. Retention/Loyalty

• High-profit vs. Low-profit

• High LTV vs. Low LT

Performance Period

1

• Customer targeting

• Acquisition

• Order receipt & fulfillment

• Invoicing & revenue recognition

• Management & cash application

• Payment processing

• Treatment Optimization

• OCA/3rd Party Management

• Write-off & Bad-debt Recovery

Non-Exhaustive


Analytics – Credit Scoring Levels of Sophistication

Different level of credit scoring capabilities are required for

the different types of industries

High-level of credit scoring sophistication is required

Credit scoring sophistication

factors

• Availability of independent

data sources

• Balance per account

• Transaction volume

• History of use

1


Analytics – Consumer Credit Bureau Score

Risk scores predict the likelihood of payment, default or

repayment taking into consideration a variety of variables C

on

su

mer

Cre

dit

Bu

reau

Sco

re

Consumer

payment history

Outstanding debt

Credit tenure

Report inquiries

Types of credit

outstanding

35%

30%

15%

10%

10%

The more bills that have been sent out for collection, the lower the

overall score.

The more cards at/near the limit, the lower the score.

The longer the consumer has had established credit, the higher

the overall credit score.

The more recent these inquiries, the lower the credit score.

The number of loans and available credit from credit cards a

consumer has makes a difference.

Credit score

variables

% of total

score

Credit scoring approach

1

EXAMPLE


Analytics – Fraud Management Methodologies

There are several key analytic methodologies that enable

banks to prevent and detect fraud

2

ILLUSTRATIVE

Fraud Scoring Models Out of Pattern

Analysis Linkage Analysis

Rules / Decision Tree

Development


Fraud scoring is the most commonly used methodology for

fraud prevention and detection

Logistic Regression Methodology

Good client Bad client (FPD1) Good client Bad client (FPD)

Neural Networks Methodology

KS: Kolmogorov Smirnov Test statistic methodology

FDP: First Payment Default

Analytics – Fraud Scoring Model

2

EXAMPLE


Credit monitoring model is providing early warning guidelines

based on client risk profile

Analytics – Credit Monitoring Model Guidelines

3A

• Development of

clients’ risk profile

• Design of warning

“signals” report

Credit

Monitoring

Model

• Set of specific

actions according to

the client risk profile

• Set of leaned

processes per risk

profile

• Ratios based on

credit managers

experience

• Calculation rules to

“weight” even “early”

warning

• Actions tailored per

risk profile

• Timeframe tailored

per actions

Dete

ct

Man

ag

e

Guidelines

Continuous improvement

of credit quality and

reduction of bad loans

Innovation

Active credit portfolio

management with overall

reduction of losses and

default rates

Optimization of credit

collection with higher

recovery rates and lower

costs

Operational excellence

that delivers lower

operative costs

Value for Banks


The Model assigns client with a risk profile (color), linking

every color to specific and time based set of actions

Analytics – Credit Monitoring Model

Detecting Managing

Pro

ce

ss

Mo

del

an

d t

oo

l

Periodical

internal Rating

(e.g. monthly,

quarterly,..)

Other specific

credit ratio

+

Engine Output

yellow

amber

red

green

blue

“Regular”(1)

Monitor

Actions/ rating re-calculation

Actions/ disengage

Disengage

Input

Return

Goals Actions

1 2

Misalignments and

completion of data

missing in rating;

daily ratio to

manage issue

when it occurs

Client report Client report

Soft

Structural

EXAMPLE

3A


There are three main components that are determining “best-

fit” restructuring offer for customers

Analytics – Customer Segmentation & Credit Re-structuring

Decision to

restructure

Restructuring

offers

Customer profile

Decision tree

Restructuring

offer for

delinquent

customer

3B


The efficient customer segmentation and profiling is heavily

dependent on the analytics capabilities of the bank

565

0

1

2

3

4

5

6

7

8

9

10

800 700 600 500 400 300 200 100 0

3/EB

1.234

2/EB

1/EB 456

5

4

3

2

1

Key:

: Industrialized restructuring

: Selective restructuring

: Alternative handling

: Size of bubble proportional

to number of customers

Low =

High =

Willingness

to pay

(index)1

Total delinquent portfolio exposure(€m)

“Low hanging fruit”

6.605

4.635 1.476

5.305

Analytics – Customer Segmentation Example EXAMPLE

3B


From credit recovery to a proactive NPL management

Analytics – Recovery Management Accelerator

4

Enhancing predictive

ability

Dedicated MBO

(Management By

Objective) system

Technological

innovation to support

entire process

• Optimize the combination of strategies to different

distribution channels to improve rates of return and

recovery

• Support and reward best performance

• Provide retroactive guidance to improve entire

credit lifecycle

• Increase efficiency through automation of industrial

activities and the integration of different actors

• Making the updated data available in real time

Enablers

Reco

very

Man

ag

em

en

t A

ccele

rato

r


The model provides a predictive historical analysis of

customer behavior to improve overall recovery performance

Analytics – Recovery Management Accelerator

4

Historical

Data

Segmentation Data Mart Predictive models

0

50

100

150

200

250

300

350

400

0 25 50 75 100

Collection of

historical data of

customer behavior

Customer

segmentation

based on historical

data

Predictive model

construction to

estimate % of

recovered

customers

• Target specific

customers to

increase recovery

rates

• Assessing early the

effectiveness of

action on individual

customer segments,

thus reducing cost

of credit

EXAMPLE


Credit Analytics Benefits

Best in Class Credit Analytics can improve ROE BY 2-4%

Source: Bloomberg data and Accenture analysis

NPL

provision

increase

Post-crisis

ROE base

case*

Operational

excellence

& strategic

cost

reduction

Efficient risk

management

Higher cost

of funding

Pre-crisis

high

performer

ROE

Reduced

fee income

Simplify

business

model

De-

leveraging

Customer

excellence

and new

profit pools

Higher

capital ratio

Target ROE

26%

-4-7%

-5-8%

-6-9%

-3-5%

-4-7%

4%

3-4%

3-5%

2-4%

3-5% 15%

• Effective NPL management and recovery

• Better risk pricing for new and existing loans

• More efficient capital allocation

* Aggregate impact of each estimated ROE levers is not cumulative; sensitivity

analysis of a simplified bank financial model is based on a peer set of global banks

Documents

Lambros Tsolkas Partner Credit Analytics · customer behaviour • Product pricing & re-pricing • Bad strategies and wrong segmentation of for the Loss making credit portfolio •