credit scoring reshape - credit research centre · advanced analytics- credit scoring to the rescue...

SW-EUB022-2007-03-19-CMD-V7

Credit Scoring Reshape because of and thanks to

the Financial Crisis

Zoom – Focus – Synthesis

Christos Glimidakis, Fotis Tsiamas, Maria Bakogeorgou July 2013

STRICTLY CONFIDENTIAL

Macroeconomic Indicators of Greece

Sources: Hellenic Statistical Authority- Bank of Greece


Financial Indicators of the Bank

From 2009 and onwards (Crisis Period) the 90+

dpd for the Mortgage Portfolio increased to

relatively high levels. The 90+ dpd for the year

2012 was at a considerable higher rate than

2008, though quite less than the average Greek

Market.

The major hit from the crisis was at the

Consumer lending portfolio. Again, from 2009

and onwards (Crisis Period) the 90+ dpd for the

Consumer Portfolio had reached high levels. The

90+ dpd for the year 2012 was at a significant

higher rate than 2008 and at similar rates of the

whole Greek Market.

Sources: Investors’ Presentations


Information- Before Crisis Vs. After Crisis

Before the crisis:

• The market was at infant stage and most banks main objective was increasing volumes

• The Credit Policy took into consideration only One Dimension information

• No combinations between Existing Score Models (Application & Behavioral) with Policy rules

• Limited information based on competitors

After the crisis:

• Mature Portfolio

• Availability of Information regarding Bureau Score (2010 & onwards)

• Drill down analysis in areas that allowed advanced analytics to take a leading role

Examples Before Vs. After Crisis regarding information in the portfolio


Bank’s Responsiveness towards the Crisis

It was apparent that the Bank turned towards the judgmental process (Human Underwriting).

From 4% before the crisis 2007 the judgmental contribution to the Total %Approval Rate increased to 20%.

This was technically enlarged because the bank tightened all policy rules and subsequently assigned to the

Credit officers the task of balancing positive and negative characteristics of the applications.


Zooming in the Bank’s Decision

Analyzing the data it was quite obvious that the most important override reasons from the

judgmental process were categorized into 3 main groups:

• Credit History (Behavioral Scores, Bureau Scores, Buckets of the Bank, Buckets of

Competitors etc.)

• Debt to Income (Balances, Exposures, Limits, Income etc.)

• Demographics & Other (Customer Characteristics, Product etc.)

The increase of the human intervention led to important fluctuations in regards to the handling of

applications with a similar profile.

This was due mainly to the psychological factor which played a crucial role since the country was in

turmoil.


Advanced Analytics- Credit Scoring to the Rescue

3 Independent Models founded on the specific reason of Rejection

• Credit History Model- based on the Behavior of the Applicant

• Debt to Income Model- based on Financial Situation of the Applicant

• Demographics & Other Model- based on Demographics & Product Related Information

1 Dependent Model

• Synthesis Model (Combining all 3 models into 1)

Demographics & Other

Debt To Income

Credit History

Synthesis Model


Credit History Related Variables D

elin

que

ncie

s

Recent

White Bureau

Own

Max Ever

Bank’s

Spouse

Me

mb

ers

Sin

ce

Bank’s

White Bureau

Bu

rea

u S

co

res

Own

Spouse

Behavio

ral S

core

s

Bank’s


DTI Related Variables D

ebts

Consumer

White Bureau

Own Mortgage Bank’s

Spouse Small Business

Unused Consumer Limits

Requested Amount & Product

Incom

e

Own

Family

De

posits

Bank’s


Demographics & Other Related Variables

Dem

ogra

phic

s

Post Code

Marital Status

Residential Status

# of Children

Years in Job

Years in adress

Age

Profession

Gender

Car

Education Level

Nationality

Available Phones

Real Estate

Pro

du

ct

Product

Channel

Deposit (Auto)

Loan Duration

Guarantor

Add-on

Oth

er

Visa

Master

Diners

Amex

Other Card

Previous Rejection s

Applications in last 1.3.6 m

Group Sales

Staff

Private Banking

Customer/No Customer


Synthesis: Combine 3 Models into a Single one

Synthesis Risk Class

Demographics & Other

Debt & Income

Credit History

Tree

1

1-2

1-5

1

…

…

10

7

… 8-10

…

… 5-6

…

…

…

… 10

1-4

1-5

…

6-10

…

… 8 - 10

…

…

…

10

Decision Tree

3 Partial Risk Classes ->

1 Final Synthesis


Models Essentials

Bad Definition: Ever 90+

dpd & Restructuring

Outcome:

12 months

Exclusions:

Rejected for Other

Reasons

Sample:

All Consumer

Applications 2010

Score

card

s C

rite

ria

Credit History 11

Debt To Income 12

Demographics 12

Sco

reca

rds K

-S

Credit History 47,2%

Debt To Income 45,2%

Demographics 41,8% S

ynth

esis

Model

K-S

44.5%


Reject Inference- Focus

RJ Inference Methodology

Approved/

Rejected Sample

Applications

Rejected

Unknown

Known Performance from Other Products in

Bank

Approved Known Performance

Misalignment Adjustment (Outliers):

If

%Approval Rate Aki << %Approval Rate Ai

then

%Approval Rate Aki = %Approval Rate Aj

i,j = 1,2,…,10 (Risk Classes)

Ak= subgroup of variable A

Parceling Inference

Reject Only

Credit History

Reject Only

Debt To Income

Reject Only

Demographics &

Other

Models

Model

Credit History

Model

Debt To Income

Model

Demographics &

Other

Synthesis Model

Final Model


%Inferred Bad & Approval Rate per Risk Class


Synthesis Model – Swap Set

Theoretically, the Synthesis Model should reduce the levels of risk significantly from 1,7% to 1,0% by

increasing the %Approval Rate from 55,7% to 62,5%, hence increasing the overall market share of the Bank.

However, when these models are applied in production the Actual Approval Rate increases by 2,1%.

57,8 %

Actual %Approval Rate

1,0%

Estimated %Bad Rate

Theoretical:

Implementation:


Challenger Model

Sample:

All Consumer Applications 2010

Exclusions:

Clear Cut Policy Rejected

Outcome:

12 months

Bad definition:

90+ dpd

Challenger Model

∙ Add-on /Guarantor

∙ Age

∙ Area

∙ Family Status

∙ Gender

∙ Group Sales

∙ #of Appl.last 6 m

∙ # of previous rejections

∙ Percent Deposit

∙ Profession

∙ Requested Tenor & Product

∙ Residential

∙ Time in Job

Demographics (13)

∙ Type of Detrimental

∙ Max Bucket In Bank

∙ Max current Bucket in Bank

∙ Max Current Bucket in Bureau

∙ Max Bucket in Bureau

∙ Months in Bureau

∙ Months Since Detrimental

Credit History (7)

∙ Amortized Balance in Bank

∙ Deposits amount

∙ Family Income to Requested

Amount

∙ Total Balance in Bureau to Total

Exposure in Bureau

Debt to Income (4)

K-S

43,3% Va

ria

ble

s


Synthesis Vs. Challenger Model Validation

Sample Period: 2011 Normal Applications

Bad Definition: 90+dpd or Restructured @12 months

Synthesis Model Validation

Comparison Synthesis to Challenger Model (K-S)

44,5%

53,8%

Synthesis Model

43,3%

50,5%

Challenger Model Development Period

Performance Validation Period

PSI<10%

K-S =53,8%


Advantages of the Synthesis

•Time in job

More Variables

35 Synthesis Vs.

24 Challenger

Synthesis Model

Reject Inference

Focus on specific Reject Reasons

Crystal

Clear

Adverse

Reasons

Higher K-S

& PSI

stable

throughout

time

Flexible

Monitoring

& Submodels

Re

Development

Macroeconomics

Data

Future Steps of the Models

Inclusion in the

Re Development Process


Future Next Steps

• Different Modeling Approach for the Synthesis Model e.g. Clustering, Regression etc.

• Inclusion of Macroeconomics Data in the Models

• Further investigation regarding the swap set Analysis

e.g. Why do we still have Rejections in the best Risk Class (RC 1) is it due to Regulatory, Policy restrictions?

• Misalignment between two different periods before crisis and after crisis.

Why for the same Risk Class we have diverse Bad Rates?

What factors have contributed to this change throughout time within each Risk Class?


THANK YOU !

Christos Glimidakis: [email protected]

Fotis Tsiamas: [email protected]

Maria Bakogeorgou: [email protected]

mailto:[email protected]







credit scoring reshape - credit research centre · advanced analytics- credit scoring to the rescue...

Documents