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    12/01/10 1Retail Decision Models

    Group Risk - Retail Risk

    Credit ScoringDevelopment and Methods

    James Marinopoulos

    Head of Retail Decision Model

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    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 risktaking in order to reduce failure rates to zero, we will, bydefinition, have eliminated the purpose of the bankingsystem.

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    Risk Families

    We are managing different groups of Risk

    Customer fails

    to pay

    Losing moneyWrong Strategy

    Change in

    market

    prices

    Processing failures and

    frauds

    Regulatory compliance

    Customer fails

    to pay

    Losing moneyWrong Strategy

    Change in

    market

    prices

    Processing failures and

    frauds

    Regulatory compliance

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    Retail Decision Models Responsibilitiess Policy

    Set Group policy on Decision Models Approve Decision Model policy changes

    s 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

    s Governance Monitoring

    Undertake bank validations, reports and presentations for APRA

    s

    Risk Measurement Set risk benchmarks for scorecards Risk grading models

    s Advise Worlds best practice in Decision Models

    Risk related issues surrounding Decision Models

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    RDM Structure and Responsibilities

    G r a d u a tJ a n e t L o

    S e n i o r D e c i s i o n M o l

    ( D e v e l o p m

    Q u y e n P h a

    M a n a g eD e c i s i o n M o l l i i

    K a t h y Z o

    M a n a g eD e c i s i o n M o l i i

    V a l e n t i n a

    G r a d u a tM a r i a D e m i

    S e n i o r D e c i s i o n M o l

    ( V a l i d a t i o

    N i c h o l a s Y i

    S e n i o r S y s t e m s A s s

    G r a e m e J

    H e a d o f

    R e t a i l D e c i s i l

    J a m e s M a r i l

    Relationship

    Developments

    Change Requests

    SystemsOngoing Validations

    Monitoring

    Data Analysis

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    Presentation Topics

    Scorecard Modelling

    Scorecard Modelling

    Business Objectives

    Business Objectives

    World Banks

    World Banks

    Monitoring

    Monitoring

    Future Direction

    Future Direction

    Overview of scoring

    Overview of scoring

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    Retail Decision Models

    Group Risk - Retail Risk

    What is credit scoring?

    s A statistical means of providing a quantifiable risk factor for a givencustomer or applicant.

    s Credit scoring is a process whereby information provided is converted intonumbers that are added together to arrive at a score. (Scorecard)

    s The objective is to forecast future performance from past behaviour.s 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 applicationscoring

    s Decision Models are used in many areas of industries:

    Banking and Finance

    Insurance Retail

    Telecommunicationss

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    Application Scoring

    s Application scoring is a statistical means of assessing risk at the point ofapplication for credit

    The application is scored once

    s Application scoring is used for:

    Credit risk determination

    Loan amount approval

    Limit setting

    Credit

    Decision

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    Behavioural Scoring

    s Behavioural scoring is a statistical means of assessing risk for existing customersthrough internal behavioural data

    Customers/accounts scored repeatedly

    s Behaviour scoring is used for:

    Authorisations

    Limit increase/overdraft applications

    Renewals/reviews Collection strategies

    Risk

    Grading

    Debit $1344. 12

    Debi t $234. 01

    Debit $987.56

    Debit $6543.22

    Debit $32423.11

    Total $2556.00

    Debit $1344. 12

    Deb it $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

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    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

    s Characteristics used in scorecards are similar to those used intraditional judgemental lending, e.g.:

    s The difference being that attributes within these characteristics are givenformal weights (scores) and added to produce a resulting score

    Character

    Time at current employment

    Residential status

    Time at current address

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    Scorecard points (example)

    Residential statusOwner Renter LWP/Other

    +25 -30 +10

    Time in employment (years)

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    Other Types of Scoring

    s Attritions Authorisations

    s Recovery

    s Response

    s

    Profitabilitys Customer

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    'Good/Bad' Discrimination

    s The objective of a scorecard is to have characteristics whichdiscriminate between Good and Bad accounts with a sufficientlyhigh probability. Some characteristics are legally or ethically not used

    s The score will be a measure of the probability of being a Good orBad performer.

    s If the scorecard is performing well then the average scores of Badsare lower than the average scores of the Goods.

    040

    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

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    Performance Charts

    s The Good/Bad Odds ateach score can bedetermined and plottedonto a Performance chart

    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

    NumberOf Clients

    Goods

    Bads

    8

    1

    Graph 2 - Log Odds Performance Chart

    0

    5

    25

    128

    645

    3250

    16400

    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

    Good/BadO

    dds

    0

    2

    4

    6

    8

    10

    12

    14

    LogGBOs(Base

    2)

    8 to 1 2 to 13

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    Application Scorecard Construction

    Flow Chart

    Characteristic AnalysisMultivariate model build

    Reject Inference

    Statistical Analysis

    Customised Scorecard

    Product IdentificationFile Data AvailabilitySamplingData Extraction/Cost

    Data Integrity

    Set cut-off Score

    Implementation

    Validation

    Generic Scorecard

    External Data SourceScorecard Vendor

    Outsourcing

    Scorecard Monitoring

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    Models

    s Expert Systemss Decision Trees

    s Linear Regression

    s Logistic Regression has the following form:

    s Neural Networks ==

    k

    j jjx

    p

    p01

    ln ( )( )

    =

    =

    +=

    k

    j jj

    k

    j jj

    x

    xp

    0

    0

    exp1

    exp

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    Model Build

    0

    0.2

    0.4

    0.6

    0.8

    1

    0 200 400 600 800 1000

    s The model is built on dichotomous data. In this case a 1 for Goodcustomers and a 0 for Bad customers.

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    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)

    s The logistic regression fits the probability better than Linearregression.

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    Reject Inference and Validation

    s 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.s Validation

    A randomly selected control group (hold out sample) or proxyportfolio to test the model.

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    Measures of discrimination

    s 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).

    s 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 Lorenzdiagram.

    s 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+= GROC

    Gini.xlsGini.xls

    http://gini.xls/http://gini.xls/http://gini.xls/http://gini.xls/http://gini.xls/http://gini.xls/
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    Measures of discrimination (II)

    s Discrimination measures should be determined for discreteattributes

    Chi-Squared

    Fico (Kullback Divergence)

    i

    iii

    B

    GBG ln)(100

    Exp

    ExpObs 2)(

    Based on a book by SolomonKullback

    Information Theory and Statistics

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    Issues for Successful Implementation

    s Cultural Changes Requires top management support

    s Operational process

    Redesign to minimise manual intervention and maximise costsavings.

    s Data Integrity

    Quality of the overall decisions, and subsequently the Portfolio, isdependant upon the accuracy of the data input. The first time!

    s Setting the Cut-off score correctly

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    Presentation Topics

    Overview of scoringOverview of scoring

    Scorecard ModellingScorecard Modelling

    World BanksWorld Banks

    MonitoringMonitoring

    Future DirectionFuture Direction

    Business ObjectivesBusiness Objectives

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    Business Objectives

    s 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 approvalss Reduce operating costs

    Increase in automated processing

    s 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

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    Business Objectives (cont)

    s 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 marketingactivities

    MIS can be fed back into future scorecard developments and collectionactivities

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    Presentation Topics

    Overview of scoringOverview of scoring

    Scorecard ModellingScorecard Modelling

    Business ObjectivesBusiness Objectives

    MonitoringMonitoring

    Future DirectionFuture Direction

    World BanksWorld Banks

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    World Banks

    s ANZs European Banks

    Banking market in Europe is restructuring

    Banks are merging across country boundaries

    s 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

    s

    National Australia Bank

    W ld B k

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    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 Scrds70 ? 50 (12)

    App lication scorecards NewUnder

    DevelopmentNew New All All

    Behavioural scorecards Existing - Best 40%Existing

    > 6 months oBooks

    P roduct Just D evelope

    Data Storage Adequate Good Good Good Good Average

    BureauB & 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 BanksUK Banks AUS Banks

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    Bureauss Fair Isaac is the main bureaus in USA

    White and Black data is supplied to and from all financial institution

    s 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

    s 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

    s 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

    s Baycorp and Credit Advantage have just merged

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    Country No ScoringData Collectio

    Centralisatio

    Generic

    Scorecards

    ApplicationScorecards

    Only

    BehaviouralScorecards -

    Product Base

    e avoura

    Scorecards -Customer

    Based

    CustomerRelationship

    Managemen Bureau

    UK W & B

    USA W & B

    Canada W & B

    South Africa B

    Spain BAustralia B

    New Zealand B

    Italy B

    Germany B

    France -

    Belgium -

    Czech Repub lic -

    Hong Kong B

    Singapore -

    Thailand -

    India -

    Korea -

    Lebanon -

    Saudi Arabia -

    Credit Scoring & Bureaus Around the WorldWe are not alone!

    B

    BBBBB

    B

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    BASEL - The New Accords The New Accord will give banks with sophisticated risk

    management capabilities increased flexibilitys More emphasis on banks internal measures of risk, supervisory

    review and market discipline

    s Decision support technology has an important role to play

    s Incentivise better risk management

    s Data warehouses are fundamental to addressing many of therequirements

    s SMB sector will be key

    s More risk sensitive

    s Competitive equality

    Paul%20Russell%2013a[1]

    The New Basel Capital AccordThe New Basel Capital Accord

    Pillar 1 :Pillar 1 :Minimum capitalMinimum capital

    requirementrequirement

    Pillar 2 :Pillar 2 :SupervisorySupervisory

    reviewreview

    processprocess

    Pillar 3 : MarketPillar 3 : Market

    disciplinediscipline

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    Pillar 1 : credit risk

    s 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

    s Better recognition of credit risk mitigation techniques

    s Behavioural scoring

    Internal

    External

    s Data storage

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    Future direction of scoring

    s

    Adaptive Control first implemented 1985 in USA Champion/Challenger processes for determining actions based on scores

    Required 10 years to be widespread in USs Customer Relationship Management

    Profitability (NIACC)

    Attrition

    Propensity to Buy (Cross Sell) Life time revenue

    s Recovery scorecardss Operations Research Methods

    Simulation modelling

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    Presentation Topics

    Overview of scoringOverview of scoring

    Scorecard ModellingScorecard Modelling

    Business ObjectivesBusiness Objectives

    World BanksWorld Banks

    Future DirectionFuture Direction

    MonitoringMonitoring

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    Monitoring Examples

    s 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 andscorecard validity

    Should be produced by Business Units or MIS

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    Loan Approval/Declines by Score

    Approva/Declinal Rates by Score

    0%

    10%

    20%

    30%

    40%

    50%

    60%

    70%

    80%

    90%

    100%

    1000

    Score Bands

    Percentages

    Auto Declined

    Manually Declined

    Manually Approved

    Auto Approved

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    Population Stabilitys Compare each characteristic and attribute

    over time against benchmarks

    s Plot score distributions over time for potential change

    s 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

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    Monitoring Requirements

    s 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 ofcustomer maturity has been established

    Should be produced by BU and Group Risk

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    Loans - Approval & Delinquency Rates

    Even with manual assessment below the cut-off score of 350 thedelinquency 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

    ApprovalRates

    0%

    5%

    10%

    15%

    20%

    25%

    Delinquen

    cyRates

    % Approved (LHS)

    Delinquency Rates (RHS)

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    Scorecard Performances 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

    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

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    Presentation Topics

    Overview of scoringOverview of scoring

    Scorecard ModellingScorecard Modelling

    Business ObjectivesBusiness Objectives

    World BanksWorld Banks

    MonitoringMonitoring

    Future DirectionFuture Direction

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    Future Direction

    s

    Modellings Experimental Design

    Champion/Challenger Strategies

    Hypothesis testing (uni & multi- dimensional)

    s Quality Control Techniques

    Control Chartss Operations Research

    Optimisation techniques

    Simulation Models

    Stress Testing

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    Conferencess Fair Isaac and Experian are the two main credit scoring companies world wide

    s 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)

    s Experian (Every year, in Europe)

    Formerly CCN

    Systems include Transact and Hunter

    Conference on world wide banking, financial, telecommunications and predictivemodelling usage (Business and/or Management)

    s University of Edinburgh (Every 2 year in Edinburgh)

    Very technical academic papers

    Proposal to run alternate years in a USA university

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    lowlow

    highhigh

    highhigh

    E[Vo lu

    me

    ]

    E[Volu

    me

    ]

    Three Portfolio Dimensions:

    Volume, Loss, and ProfitLowLow

    cutoffscutoffs

    HighHigh

    cutoffscutoffs

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    Efficient Frontiers in two dimensions

    OP

    HighCutoffs

    E[Volume]

    E[Loss]

    LowCutoffs

    0.60.6

    0.00.00.20.2

    LowCutoffs

    HighCutoffs

    E[Profit]

    E[Loss]

    OP

    0.90.90.60.6

    0.00.00.20.2 0.60.6

    HighCutoffs

    LowCutoffs

    OP

    E[Volume]

    E[Profit]

    0.60.6

    0.20.2

    0.20.2 0.90.9

    Efficient Frontier

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    Improved portfolio performance

    OP

    HighCutoffs

    E[Volume]

    E[Loss]

    LowCutoffs

    0.60.6

    0.00.00.20.2

    LowCutoffs

    HighCutoffs

    E[Profit]

    E[Loss]

    OP

    0.90.90.60.6

    0.00.00.20.2 0.60.6

    HighCutoffs

    LowCutoffs

    OP

    E[Volume]

    E[Profit]

    0.60.6

    0.20.2

    0.20.2 0.0.9

    Single Score

    CombinedScores

    Single Score

    CombinedScores

    Single Score

    CombinedScores

    Efficient Frontier

  • 8/8/2019 Credit Score 02

    51/52

  • 8/8/2019 Credit Score 02

    52/52

    Other Techniques

    s

    Customer Relation Managements Survival Analysis

    s Multiple Indicator Multiple Cause

    Proportional Hazards.ppt

    Measuring Customer Quality.doc