Credit Scoring ppt.ppt

Retail Decision Models
Group Risk - Retail Risk
8/06/2023 1
Credit Scoring
Development and Methods
James Marinopoulos
Head of Retail Decision Model

Alan Greenspan:
President, Federal Reserve Board
May 1996
“… We should not forget that the basic economic function of these
regulated entities (banks) is to take risk. If we minimise risk
taking in order to reduce failure rates to zero, we will, by
definition, have eliminated the purpose of the banking
system.”

Risk Families
We are managing different groups of Risk
Customer fails
to pay
Losing money
Wrong Strategy
Change in
market
prices
Processing failures and
frauds
Regulatory compliance
Customer fails
to pay
Losing money
Wrong Strategy
Change in
market
prices
Processing failures and
frauds
Regulatory compliance

8/06/2023 4
Retail Decision Models Responsibilities
 Policy
– Set Group policy on Decision Models
– Approve Decision Model policy changes
 Monitor, Validate and Approve
– New Scorecard Developments
– Existing Scorecard Functionality
– Proposed changes to Decision Models Processes
– New Decision Models Systems functionality
– Decision Models Systems functionality changes
 Governance
– Monitoring
– Undertake bank validations, reports and presentations for APRA
 Risk Measurement
– Set risk benchmarks for scorecards
– Risk grading models
 Advise
– Worlds best practice in Decision Models
– Risk related issues surrounding Decision Models

8/06/2023 5
RDM Structure and Responsibilities
Graduate
Janet Long
Senior
Decision Model Manager
(Developments)
Quyen Pham-Nguyen
Manager
Decision Model Validation
Kathy Zovko
Manager
Decision Model Monitoring
Valentina Dragan
Graduate
Maria Demetriou
Senior
Decision Model Manager
(Validations)
Nicholas Yannios
Senior
Systems Assurance Manager
Graeme Judd
Head of
James Marinopoulos
Relationship
Developments
Change Requests
Systems
Ongoing Validations
Monitoring
Data Analysis

8/06/2023 6
Presentation Topics
Scorecard Modelling
Business Objectives
World Banks
Monitoring
Future Direction
Overview of scoring

8/06/2023 7
What is credit scoring?
 A statistical means of providing a quantifiable risk factor for a given
customer or applicant.
 Credit scoring is a process whereby information provided is
converted into numbers that are added together to arrive at a score.
(“Scorecard”)
 The objective is to forecast future performance from past behaviour.
 Credit scoring developed by Fair & Isaac in early 60s
– Widespread acceptance in the US in early 80s and UK early
90s
– FICO scores make 75% of US Mortgage loan decisions
– Behavioural scoring accepted as more predictive than
application scoring
 Decision Models are used in many areas of industries:
– Banking and Finance
– Insurance
– Retail
– Telecommunications


8/06/2023 8
Application Scoring
 Application scoring is a statistical means of assessing risk at the
point of application for credit
– The application is scored once
 Application scoring is used for:
– Credit risk determination
– Loan amount approval
– Limit setting
Credit
Decision

8/06/2023 9
Behavioural Scoring
 Behavioural scoring is a statistical means of assessing risk for
existing customers through internal behavioural data
– Customers/accounts scored repeatedly
 Behaviour scoring is used for:
– Authorisations
– Limit increase/overdraft applications
– Renewals/reviews
– Collection strategies
Risk
Grading
Debit $1344. 12
Debit $234. 01
Debit $987.56
Debit $6543.22
Debit $32423.11
Total $2556.00
Debit $1344. 12
Debit $234. 01
Debit $987.56
Debit $6543.22
Debit $32423.11
Total $2556.00
Debit $1344. 12
Debit $234. 01
Debit $987.56
Debit $6543.22
Debit $32423.11
Total $2556.00

8/06/2023 10
Sample scorecard characteristics
 Financial
 Assets
 Liabilities
 Monthly repayment
 Total Monthly income
 Bureau
 No. of bureau defaults
 Adverse ANZ behaviour
 Application
 Purpose of loan
 Deposit
 Security
 Characteristics used in scorecards are similar to those used in
traditional judgemental lending, e.g.:
 The difference being that attributes within these characteristics are given
formal weights (scores) and added to produce a resulting score
Character
Time at current employment
Residential status
Time at current address

8/06/2023 11
Scorecard points (example)
Residential status
Owner Renter LWP/Other
+25 -30 +10
Time in employment (years)
<2 3-4 5-6 7+
2 10 15 25
Total monthly income
0 <$500 <$1000 <$1500 <$2000 <$3000 >$3000
0 15 25 31 37 43 48
Total defaults
No Defaults 1 2+
0 -70 -250

8/06/2023 12
Other Types of Scoring
 Attrition
 Authorisations
 Recovery
 Response
 Profitability
 Customer

8/06/2023 13
Presentation Topics
Overview of scoring
Business Objectives
World Banks
Monitoring
Future Direction
Scorecard Modelling

8/06/2023 14
Good/Bad Odds
 A scoring system does not individually identify a good performer
from a bad performer, it classifies an applicant in a particular
“Good/Bad odds” group.
 An applicant belonging to a 200 to 1 group, appears pretty safe and
profitable.
 If the applicant belongs to a 4 to 1 risk group, we would no doubt
find the risk unacceptable.
 There is a “cut-off” point where it is not profitable for the bank to
accept a certain Good to Bad ratio
 Based on the above, it is accepted that there will be some “bads”
above the cut-off level set, and some “goods” below the cut-off level
set.

8/06/2023 15
'Good/Bad' Discrimination
 The objective of a scorecard is to have characteristics which
discriminate between Good and Bad accounts with a sufficiently
high probability.
– Some characteristics are legally or ethically not used
 The score will be a measure of the probability of being a Good or
Bad performer.
 If the scorecard is performing well then the average scores of ‘Bads’
are lower than the average scores of the ‘Goods’.
0
4
0
8
0
1
2
0
1
6
0
2
0
0
2
4
0
2
8
0
3
2
0
3
6
0
4
0
0
4
4
0
4
8
0
5
2
0
5
6
0
6
0
0
6
4
0
6
8
0
7
2
0
7
6
0
8
0
0
Score
N um ber
O f Clients
Goods
Bads

8/06/2023 16
Performance Charts
 The Good/Bad Odds at
each score can be
determined and plotted
onto a Performance chart
0
40
80
120
160
200
240
280
320
360
400
440
480
520
560
600
640
680
720
760
800
Score
Number
Of Clients
Goods
Bads
8
1
Graph 2 - Log Odds Performance Chart
0
5
25
128
645
3250
16400
0
40
80
120
160
200
240
280
320
360
400
440
480
520
560
600
640
680
720
760
800
Good/Bad
Odds
0
2
4
6
8
10
12
14
Log
GBOs
(Base
2)
8 to 1 2 to 1
3

8/06/2023 17
Application Scorecard Construction
Flow Chart
•Characteristic Analysis
•Multivariate model build
•Reject Inference
Statistical Analysis
Customised Scorecard
•Product Identification
•File Data Availability
•Sampling
•Data Extraction/Cost
Data Integrity
Set cut-off Score
Implementation
Validation
Generic Scorecard
•External Data Source
•Scorecard Vendor
Outsourcing
Scorecard Monitoring

8/06/2023 18
Model Build
 Once the characteristics have been selected a statistical
model can be developed.
 Multivariate statistical methods include
– Logistic Regression
– Stepwise methods
– Residual analysis
 Not all predictive characteristics are used in the model.
– An inter-correlation effect may exist between variables.
– For example, age may be correlated with time at current
employment and therefore only one is necessary in the model.

8/06/2023 19
Models
 Expert Systems
 Decision Trees
 Linear Regression
 Logistic Regression has the following form:
 Neural Networks
 










k
j j
j x
p
p
0
1
ln 
 
 





 k
j j
j
k
j j
j
x
x
p
0
0
exp
1
exp



8/06/2023 20
Model Build
0
0.2
0.4
0.6
0.8
1
0 200 400 600 800 1000
 The model is built on dichotomous data. In this case a 1 for “Good”
customers and a 0 for “Bad” customers.

8/06/2023 21
Logistic Regression
0
0.2
0.4
0.6
0.8
1
0 200 400 600 800 1000
Good/Bad Probability
Logistic
Linear (Good/Bad Probability)
 The logistic regression fits the probability better than Linear
regression.

8/06/2023 22
Reject Inference and Validation
 Reject Inference
– Reject Inference is only necessary for scorecards were there is no
performance information for rejected applications
• Applications that are rejected must be included in the final model.
– Behavioural scorecards deal only in existing customers, therefore
do not require reject inference.
 Validation
– A randomly selected control group (hold out sample) or proxy
portfolio to test the model.

8/06/2023 23
Measures of discrimination
 Receiver Operating Curve (ROC)
– The Receiver Operating Curve is the area under the curve generated when
the cumulative Bads are plotted against the cumulative goods (Lorenz
Curve).
 Gini coefficient (G)
– This discrimination measure is geometrically defined as the ratio of the area
A of the shaded semi-circular area to the area B of the triangle in the Lorenz
diagram.
 PH (percentage Good for 50% Bad)
– This is defined as the cumulative proportion of Goods up to the median
value of the Bads.
)
1
(
2
1

 G
ROC
Gini.xls

8/06/2023 24
Lorenz Curve
10%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
Cumulative Goods
Cumulative
Bads
• Scorecard performance can be
judged on the level of
discrimination
• Two measure that can be used are:
Gini (or ROC)
PH - % of Goods below 50% of
bads
• 1% of PH could mean an additional
3% approvals
• 1% of PH could mean an reduction
of 0.2% bad debts
Gini=0.62%
Measures of discrimination – (I)

8/06/2023 25
Measures of discrimination –(II)
 Discrimination measures should be determined for discrete
attributes
– Chi-Squared
– Fico (Kullback Divergence)  








i
i
i
i
B
G
B
G ln
)
(
100


Exp
Exp
Obs 2
)
(
Based on a book by Solomon
Kullback
“Information Theory and Statistics”

8/06/2023 26
Issues for Successful Implementation
 Cultural Change
 Requires top management support
 Operational process
– Redesign to minimise manual intervention and maximise cost
savings.
 Data Integrity
– Quality of the overall decisions, and subsequently the Portfolio, is
dependant upon the accuracy of the data input. The first time!
 Setting the Cut-off score correctly

8/06/2023 27
Presentation Topics
Overview of scoring
Scorecard Modelling
World Banks
Monitoring
Future Direction
Business Objectives

8/06/2023 28
Business Objectives
 Increase consistency of lending decisions
– Consistent & unbiased treatment of applicant
• Customers with the same details get the same score
– Total management control over credit approval systems
• Allows for loosening or tightening of lending through credit cycles
• Potential increase in approvals
 Reduce operating costs
– Increase in automated processing
 Improve customer service
– Fast and consistent decisions at application point
– More appropriate limit and authorisation decisions
– Reduction in collection actions on low risk accounts
– Risk based allocation of credit limits and issue terms

8/06/2023 29
Business Objectives (cont)
 Improved portfolio management
– Manage credit portfolios more effectively and dynamically
• Better prediction of credit losses
• Management ability to react to changes fast & accurately
• Ability to measure & forecast impact of policy decisions
• Quick and uniform policy implementation
– Improved Management Information Systems (MIS)
• Permits MIS to be developed to assist business needs and marketing
activities
• MIS can be fed back into future scorecard developments and collection
activities

8/06/2023 30
Presentation Topics
Overview of scoring
Scorecard Modelling
Business Objectives
Monitoring
Future Direction
World Banks

8/06/2023 31
World Banks
 ANZ
 European Banks
– Banking market in Europe is restructuring
– Banks are merging across country boundaries
 UK bank visits
– Bank A - bank with many recent acquisitions
– Bank B - bank dealing with mainly credit cards
– Bank C - ex building society now owned by bank
– Bank D - large diverse bank
 National Australia Bank

8/06/2023 32
Mortgages - Y Y - Y Y
Personal Loans Y - - Y Y Y
Current Accounts Y - Y - Y Y
Credit cards Y - - Y Y Y
LMI - In House In House - External External
Retail FUM ? £58b £47b £8b $100b+ $60b
Scorecards 20 -
? App Scrds
1 Beh Scrds
70 ? 50 (12)
Application scorecards New
Under
Development
New New All All
Behavioural scorecards Existing -
Best 40%
Existing
> 6 months on
Books
Product Just Developed
Data Storage Adequate Good Good Good Good Average
Bureau
B & W
(Equifax)
B & W
(Equifax)
B & W
(Experian)
B & W
(Experian)
Black (Credit
Advantage)
Black (Credit
Advantage)
Scoring Modelling Staff 20+ 3 30+ ? 40+ 15
World Banks
UK Banks AUS Banks

8/06/2023 33
Bureaus
 Fair Isaac is the main bureaus in USA
– “White” and “Black” data is supplied to and from all financial institution
 Fair Isaac (Equifax) and Experian are the two main bureaus in UK
– “White” data is supplied to a financial institution if the supply to bureau
– Currently few banks supply and receive “white” data
• Mergers are leading most banks to look at this option
– Fair Isaac is trying to beat Experian in having bureau scores in the UK
• This is only possible when all banks supply “white” data
 Credit Advantage is used in Australia
– Provides “Black” data only
– Linked with Decision Advantage (previously Equigen)
– Bureau scores used for ANZ Small Business
• We could use Dunn & Bradstreet for over $250k lending
 Baycorp is used in New Zealand
– Provides “Black” data only
– Baycorp is also a collections agency
– NZ puts the smallest amount lost as a default
 Baycorp and Credit Advantage have just merged

8/06/2023 34
Country No Scoring
Data Collection/
Centralisation
Generic
Scorecards
Application
Scorecards
Only
Behavioural
Scorecards -
Product Based
Behavioural
Scorecards -
Customer
Based
Customer
Relationship
Management Bureau
UK W & B
USA W & B
Canada W & B
South Africa B
Spain B
Australia B
New Zealand B
Italy B
Germany B
France -
Belgium -
Czech Republic -
Hong Kong B
Singapore -
Thailand -
India -
Korea -
Lebanon -
Saudi Arabia -
Credit Scoring & Bureaus Around the World
“We are not alone!”
B
B
B
B
B
B
B

8/06/2023 35
BASEL - The New Accord
 The New Accord will give banks with sophisticated risk
management capabilities increased flexibility
 More emphasis on bank’s internal measures of risk, supervisory
review and market discipline
 Decision support technology has an important role to play
 Incentivise better risk management
 Data warehouses are fundamental to addressing many of the
requirements
 SMB sector will be key
 More risk sensitive
 Competitive equality
Paul%20Russell%2013a[1]
The New Basel Capital Accord
Pillar 1 :
Minimum capital
requirement
Pillar 2 :
Supervisory
review
process
Pillar 3 : Market
discipline

8/06/2023 36
Pillar 1 : credit risk
 Internal Rating Based (IRB) approach
– Foundation
• Bank sets Probability of Default (PD)
• Standard Exposure At Default (EAD)
• Standard Loss Given Default (LGD)
– Advanced
• Banks sets PD, EAD & LGD
 Better recognition of credit risk mitigation techniques
 Behavioural scoring
– Internal
– External
 Data storage

8/06/2023 37
Future direction of scoring
 “Adaptive Control” first implemented 1985 in USA
– Champion/Challenger processes for determining actions
based on scores
– Required 10 years to be widespread in US
 Customer Relationship Management
– Profitability (NIACC)
– Attrition
– Propensity to Buy (Cross Sell)
– Life time revenue
 Recovery scorecards
 Operations Research Methods
– Simulation modelling

8/06/2023 38
Presentation Topics
Overview of scoring
Scorecard Modelling
Business Objectives
World Banks
Future Direction
Monitoring

Monitoring Examples
 1. Operation Stability Reports
– The four types of front end monitoring reports:
1.1 Approval Statistics Report
1.2 Population Stability Report
1.3 System Rules Referral Report
1.4 Portfolio Statistics Report
– Operational statistics can be obtained as soon as an automated
decision process is implemented
– Early warning indicators of decision functionality error and
scorecard validity
– Should be produced by Business Units or MIS

8/06/2023 40
Loan Approval/Declines by Score
Approva/Declinal Rates by Score
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<=500
501-550
551-600
601-650
651-700
701-750
751-800
801-850
851-900
901-950
951-1000
>1000
Score Bands
Percentages
Auto Declined
Manually Declined
Manually Approved
Auto Approved

8/06/2023 41
Population Stability
 Compare each characteristic and attribute
– over time
– against benchmarks
 Plot score distributions over time for potential change
 Indicates potential drift in performance
NO YES
Dec-96 25% 75%
Mar-97 23% 77%
Jun-97 24% 76%
Sep-97 22% 78%
Dec-97 21% 79%
Mar-98 19% 81%
Jun-98 19% 81%
Sep-98 22% 78%
Dec-98 20% 80%
Mar-99 20% 80%
Jun-99 18% 82%
Sep-99 18% 82%
Dec-99 17% 83%
Benchmarks 29% 71%
Population Stability
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
NO YES
Dec-96
Mar-97
Jun-97
Sep-97
Dec-97
Mar-98
Jun-98
Sep-98
Dec-98
Mar-99
Jun-99
Sep-99
Dec-99

Monitoring Requirements
 2. Performance Analysis
– The two types of back end monitoring are:
2.1 Scorecard Performance Report
2.2 Characteristic Analysis Report
2.3 Dynamic Delinquency Report
– Performance Analysis is undertaken once a certain level of
customer maturity has been established
– Should be produced by BU and Group Risk

8/06/2023 43
Loans - Approval & Delinquency Rates
 Even with manual assessment below the cut-off score of 350 the
delinquency rates are higher
Loans Approval & Delinquency Rates
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1-300 301-
350
351-
400
401-
450
451-
500
501-
550
551-
600
601-
650
651-
700
701-
750
751-
800
>800
Score
Approval
Rates
0%
5%
10%
15%
20%
25%
Delinquency
Rates
% Approved (LHS)
Delinquency Rates (RHS)

8/06/2023 44
Scorecard Performance
 Scorecard performance based on 30+ delinquency
– Good/Bad odds increase as expected by score
Score Distribution & G/B Odds
0
500
1000
1500
2000
2500
3000
3500
4000
<=500
501-550
551-600
601-650
651-700
701-750
751-800
801-850
851-900
901-950
951-1000
>1000
Score
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Non Delinq
Delinq
HL GB Odds

8/06/2023 45
Presentation Topics
Overview of scoring
Scorecard Modelling
Business Objectives
World Banks
Monitoring
Future Direction

8/06/2023 46
Future Direction
 Modelling
 Experimental Design
– Champion/Challenger Strategies
– Hypothesis testing (uni & multi- dimensional)
 Quality Control Techniques
– Control Charts
 Operations Research
– Optimisation techniques
– Simulation Models
– Stress Testing

8/06/2023 47
Conferences
 Fair Isaac and Experian are the two main credit scoring companies
world wide
 Fair Isaac (Every year, alternating in Europe and USA)
– Main bureau and FICO Scores in USA
– Equifax in UK
– Systems included TRIAD
– Conference was mainly selling FICO products and systems (but also
Technical)
 Experian (Every year, in Europe)
– Formerly CCN
– Systems include Transact and Hunter
– Conference on world wide banking, financial, telecommunications and
predictive modelling usage (Business and/or Management)
 University of Edinburgh (Every 2 year in Edinburgh)
– Very technical academic papers
– Proposal to run alternate years in a USA university

8/06/2023 48
low
high
high
E
[
Volume
]
Three Portfolio Dimensions:
Volume, Loss, and Profit
Low
cutoffs
High
cutoffs

8/06/2023 49
Efficient Frontiers in two dimensions
OP
High
Cutoffs
E[Volume]
E[Loss]
Low
Cutoffs
0.6
0.0
0.2
Low
Cutoffs
High
Cutoffs
E[Profit]
E[Loss]
OP
0.9
0.6
0.0
0.2 0.6
High
Cutoffs
Low
Cutoffs
OP
E[Volume]
E[Profit]
0.6
0.2
0.2 0.9
Efficient Frontier

8/06/2023 50
Improved portfolio performance
OP
High
Cutoffs
E[Volume]
E[Loss]
Low
Cutoffs
0.6
0.0
0.2
Low
Cutoffs
High
Cutoffs
E[Profit]
E[Loss]
OP
0.9
0.6
0.0
0.2 0.6
High
Cutoffs
Low
Cutoffs
OP
E[Volume]
E[Profit]
0.6
0.2
0.2 0.9
Single Score
Combined
Scores
Single Score
Combined
Scores
Single Score
Combined
Scores
Efficient Frontier

8/06/2023 51
Best Practices
 Combining Application & Behavioural scores (Bayesian estimates)
Reject set with
combined scores
Accept set with
combined scores
s
t
Equal- odds
line c (s, t)

8/06/2023 52
Other Techniques
 Customer Relation Management
 Survival Analysis
 Multiple Indicator Multiple Cause
Proportional Hazards.ppt
Measuring Customer Quality.doc

Credit Scoring ppt.ppt

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