In light of the analysis report and upon fulfillment of the above stated conditions, I recommend revolving Warehouse receipt financing facility limit of birr 36,000,000.00(thirty six million birr) to be utilized for buying of Wheat for further period of 150 days from the date of disbursement or 30 days prior to the expiry date of Warehouse receipt, whichever is shorter. However, the amount to be disbursed shall not exceed 70% of the total estimated market value as mentioned on the warehouse Receipt. Moreover, the facility is to be secured against Warehouse receipt to be issued by Warehouse operator
Name _____________ Signature__________________ Date, ___________________
1. Recommendation of the Customer Relationship Manager/Consumer Loan:
Reasons of Recommendation
With the view of supporting the new credit product implementation, as a success to our bank.
The new product may assist the growth of agricultural sector and enhance the availability of agricultural raw material and adequate collateral offered;
Because of purchasing Italy brand new macaroni machine and expansion of the project the tangible net worth of the company is decreasing.
Long-time business experience with qualified management members
The commodity to be pledged by the customer wheat will be demandable all the time
The requested facility will be secured against warehouse receipt to be issued by warehouse operator
I recommend revolving Warehouse receipt financing facility limit of birr 40,000,000.00(Forty million birr) to be utilized for buying of Wheat for further period of 150 days from the date of disbursement or 30 days prior to the expiry date of Warehouse receipt, whichever is shorter. However, the amount to be disbursed shall not exceed 70% of the total estimated market value as mentioned on the warehouse Receipt. Moreover, the facility is to be secured against Warehouse receipt to be issued by Warehouse operator.
In light of the analysis report and upon fulfillment of the above stated conditions, I recommend revolving Warehouse receipt financing facility limit of birr 36,000,000.00(thirty six million birr) to be utilized for buying of Wheat for further period of 150 days from the date of disbursement or 30 days prior to the expiry date of Warehouse receipt, whichever is shorter. However, the amount to be disbursed shall not exceed 70% of the total estimated market value as mentioned on the warehouse Receipt. Moreover, the facility is to be secured against Warehouse receipt to be issued by Warehouse operator
Name _____________ Signature__________________ Date, ___________________
1. Recommendation of the Customer Relationship Manager/Consumer Loan:
Reasons of Recommendation
With the view of supporting the new credit product implementation, as a success to our bank.
The new product may assist the growth of agricultural sector and enhance the availability of agricultural raw material and adequate collateral offered
1. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 1
Credit Scoring
Development and Methods
James Marinopoulos
Head of Retail Decision Model
2. Retail Decision Models
Group Risk - Retail Risk
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.”
3. Retail Decision Models
Group Risk - Retail Risk
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
4. Retail Decision Models
Group Risk - Retail Risk
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
5. Retail Decision Models
Group Risk - Retail Risk
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
Retail Decision Models
James Marinopoulos
Relationship
Developments
Change Requests
Systems
Ongoing Validations
Monitoring
Data Analysis
6. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 6
Presentation Topics
Scorecard Modelling
Business Objectives
World Banks
Monitoring
Future Direction
Overview of scoring
7. Retail Decision Models
Group Risk - Retail Risk
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. Retail Decision Models
Group Risk - Retail Risk
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
9. Retail Decision Models
Group Risk - Retail Risk
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
10. Retail Decision Models
Group Risk - Retail Risk
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
11. Retail Decision Models
Group Risk - Retail Risk
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
12. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 12
Other Types of Scoring
Attrition
Authorisations
Recovery
Response
Profitability
Customer
13. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 13
Presentation Topics
Overview of scoring
Business Objectives
World Banks
Monitoring
Future Direction
Scorecard Modelling
14. Retail Decision Models
Group Risk - Retail Risk
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.
15. Retail Decision Models
Group Risk - Retail Risk
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
16. Retail Decision Models
Group Risk - Retail Risk
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
17. Retail Decision Models
Group Risk - Retail Risk
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
18. Retail Decision Models
Group Risk - Retail Risk
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.
19. Retail Decision Models
Group Risk - Retail Risk
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
20. Retail Decision Models
Group Risk - Retail Risk
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.
21. Retail Decision Models
Group Risk - Retail Risk
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.
22. Retail Decision Models
Group Risk - Retail Risk
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.
23. Retail Decision Models
Group Risk - Retail Risk
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
24. Retail Decision Models
Group Risk - Retail Risk
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)
25. Retail Decision Models
Group Risk - Retail Risk
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”
26. Retail Decision Models
Group Risk - Retail Risk
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
27. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 27
Presentation Topics
Overview of scoring
Scorecard Modelling
World Banks
Monitoring
Future Direction
Business Objectives
28. Retail Decision Models
Group Risk - Retail Risk
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
29. Retail Decision Models
Group Risk - Retail Risk
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
30. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 30
Presentation Topics
Overview of scoring
Scorecard Modelling
Business Objectives
Monitoring
Future Direction
World Banks
31. Retail Decision Models
Group Risk - Retail Risk
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
32. Retail Decision Models
Group Risk - Retail Risk
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
33. Retail Decision Models
Group Risk - Retail Risk
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
34. Retail Decision Models
Group Risk - Retail Risk
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
35. Retail Decision Models
Group Risk - Retail Risk
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
36. Retail Decision Models
Group Risk - Retail Risk
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
37. Retail Decision Models
Group Risk - Retail Risk
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
38. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 38
Presentation Topics
Overview of scoring
Scorecard Modelling
Business Objectives
World Banks
Future Direction
Monitoring
39. Retail Decision Models
Group Risk - Retail Risk
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
41. Retail Decision Models
Group Risk - Retail Risk
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
42. Retail Decision Models
Group Risk - Retail Risk
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
44. Retail Decision Models
Group Risk - Retail Risk
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
45. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 45
Presentation Topics
Overview of scoring
Scorecard Modelling
Business Objectives
World Banks
Monitoring
Future Direction
46. Retail Decision Models
Group Risk - Retail Risk
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
47. Retail Decision Models
Group Risk - Retail Risk
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
48. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 48
low
high
high
E
[
Volume
]
Three Portfolio Dimensions:
Volume, Loss, and Profit
Low
cutoffs
High
cutoffs
49. Retail Decision Models
Group Risk - Retail Risk
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
50. Retail Decision Models
Group Risk - Retail Risk
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
51. Retail Decision Models
Group Risk - Retail Risk
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)
52. Retail Decision Models
Group Risk - Retail Risk
8/06/2023 52
Other Techniques
Customer Relation Management
Survival Analysis
Multiple Indicator Multiple Cause
Proportional Hazards.ppt
Measuring Customer Quality.doc