business intelligence & data mining-3-4

8/10/2019 Business Intelligence & Data Mining-3-4

1/29

Credit Scoring


2/29

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 eliminate risk taking in order to

reduce failure rates to zero, we will, by

definition, have eliminated the purpose of the

banking system.


3/29

Types of Lending Risk

Customer fails

to pay

Losing moneyWrong Strategy

Change in

marketprices

Processing failures andfrauds

Regulatory compliance

Customer fails

to pay

Losing moneyWrong Strategy

Change in

marketprices

Processing failures andfrauds

Regulatory compliance

Borrowerfails to pay


4/29

How can I efficiently manage resources while

meeting business and operating constraints?

How can I create and re-create strategies in a very

dynamic environment?

How can I achieve these benefits with minimal

change to current systems infrastructure?

Profits

Profits

Losses

Unused

Capacity

Attrition

The Universal Balancing Act


5/29

Everyday Questions

Balancing Marketing and Risk

Should Itarget this

consumer?

Will theconsumer

hear it?

with whatmessage?

Will theconsumer

apply?

Should Iapprove?

at whatcredit level?

Will theconsumer

use it?

Will the consumer

pay as agreed?attrite too early?

build large balances?

repeat purchase?buy add-on services?

be profitable?

How will Icontinue toinfluence?


6/29

Everyday Questions

Balancing Marketing and Risk

Will the consumer




be profitable?


Net income

VALUE

Portfolio size

# accounts Receivables

Risk

Yield

Losses

Growth in each

Costs


7/29

Everyday Questions

Balancing Marketing and RiskShould I

target thisconsumer?

Will theconsumer

hear it?

with whatmessage?

Will theconsumer

apply?

Should Iapprove?

at whatcredit level?

Will theconsumer

use it?

Will the consumer




be profitable?


Increase value by improvingdecisions

Use BI to optimize multipleobjectives


8/29

Decision areas

Solicitations New applications Account management

Credit line Authorization Collections Reissue

Cross-sell

Keep / sell


9/29

10/18/2013 9

Business Objectives

Increase consistency of lending decisions

Consistent & unbiased treatment of applicant

Customers with the same details get the same treatment

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


10/29

10/18/2013 10

Business Objectives

Improved portfolio management

Manage credit portfolios more effectively anddynamically

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 Information Permits information gathering to assist business needs and

marketing activities

Information gathered can be fed back into future scoringsystems developments, collection activities and strategyoptimization


11/29

The BI Solution: Scoring Models

OUTCOMEMODELDATA


12/29

BI in the Consumer Credit Industry

Numerous quantitative advances have emerged in the

consumer credit risk area to support business strategythroughout the customer life cycle - beyond simplecredit scores.

At credit origination, analytical models are used to:

Identify likely consumers who are likely to be profitable Predict propensity to respond to a credit offering

Align consumer preferences with products

Assess borrower credit worthiness

Determine line/loan authorization

Apply risk-based pricing Evaluate relationship value of the customer


13/29

Throughout loan servicing, analytical methods are used to: Anticipate consumer behaviour (risk) or payment patterns

Determine opportunities for cross-selling

Assess prepayment risk

Identify any fraudulent transactions

Optimize customer relationship management Prioritize the collections effort to maximize recoveries in the event

of delinquency

Analytical models are fast becoming the back-bone of

efficient consumer credit risk management.

Consumer lending represents an analytically robust anddata-rich environment for credit risk and capital

measurement.

BI in the Consumer Credit Industry


14/29

Primary Decision:Reduce Loss

Secondary Decision:Risk-Related

Specialty RiskAssessment

Removing Credit:Additional Precision

TRIAD adaptivecontrol system Debt Manager-RMS RMS, BridgeLink

ACCOUNT STATUS

ON TIME DELINQUENT LATE-STAGE

COLLECTIONS

RECOVERY

Behavior score

Custom collection score

Bureau-basedrecovery score

Custom recoveryscore

FICO score

Other bureau scores orcustom scores

Transaction score

Account Life-cycle Scoring Progression


15/29

Risk Analytics in Consumer Lending

Credit risk in consumer banking has been (traditionally)

driven by 3 Cs of lending (based on judgment):1.Character willingness to re-pay debt

2.Collateral incentive to re-pay debt

3.Capacity ability to re-pay debt

The presence of a large number of consumers makes this

environment ideal for empirical modeling to predictborrower behaviour as the basis for acquisition and

management of customers.

Markets with robust credit bureaus further provide the

impetus to use models to predict borrower behaviour.

Credit scores can summarize the details of credit reportand application data into a single actionable metric.


16/29

Basic Concept of 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 (hence it is anexample of Generalized Additive Models) to arrive at a score(using a Scorecard).

The objective is to forecast future performance from pastbehaviour.

Credit scoring developed by Fair & Isaac in early 1960s

Widespread acceptance in the US in early 80s and UKearly 90s

FICO scores make 75% of US Mortgage loan decisions Behavioural scoring, introduced later, has been accepted as

more predictive than application scoring


17/29

National (US) Distribution of

FICO Scores

1%

5%

8%

12%

16%

19%

28%

11%

0%

5%

10%

15%

20%

25%

30%

Up to 499 500-549 550-599 600-649 650-699 700-749 750-799 800+

%o

fPop

ulation

FICO Score Range


18/29

Bad Rates of Major FICO Score

Ranges

Score Range Bad %

300-500 48%

500-599 30%

600-699 11%

700-850 1.50%


19/29

Evaluating the credit applicants: JudgmentVersus Scoring

Time at present addressTime at present jobResidential statusDebt ratioBank referenceAgeIncome

# of Recent inquiries% of Balance to avail. lines# of Major derogs.

Overall

DecisionOdds of repayment

CHARACTERISTICS

++-++

N / A-

-++

+

Accept?

JUDGMENT

12205

2128155

-71035

212

Accept46:1

CREDIT SCORING


20/29

Residence


21/29

CREDIT BUREAUREPORTS

CREDITAPPLICATION

Sources of information

Credit reports

Application data

Public records

Prior experience

Demographics

Billing file

Deal terms


22/29

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 / Credit card application approval

Loan amount / Credit limit setting

Credit

Decision


23/29

Behavioural Scoring

Behavioural scoring is a statistical means of

assessing risk for existing customers throughinternal 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


24/29

The Objective

The objective of a scorecard is to use characteristics that

discriminate between Good and Bad accounts with sufficiently

high accuracy.

The score is a measure of the probability of being a Good or

Bad performer.

If the scorecard is a good one then the mean score of Bads is

lower than the mean score of the Goods.


25/29

Good/Bad Odds (probability)

A scoring system does not individually identify a goodperformer 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 wouldno 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 somegoods below the cut-off level set.


26/29

ODDS

64 / 1

16 / 1

4 / 1

1 / 1

SCORE

220

180

140

100

=

=

=

=

Credit score = odds (risk)


27/29

Scores are Calibrated (Aligned) to Odds(on a Log Scale)

-5

-4

-3

-2

-1

0

1

505 515 525 535 545 555 565 575 585 595

Ln(Odds)

Score

Actual Data

Std Line

For any given score the probability of Bad can be foundusing the equation of the Log-odds (straight) line.


28/29

How Is a Credit Scoring Model

Developed ?

Analysis of a large set of consumers (>= 1Million)

Identification of common variables that

define behavior Statistical models are then built that assign

weights to each variable

Adding all variables combines to make an

individual score


29/29

Scorecard Construction

Characteristic Analysis

Characteristic Selection

Multivariate model build

Reject Inference

Statistical Analysis

Customised Scorecard

Population Identification

Data Availability

Data Extraction

Sampling

Data Gathering

Set cut-off Score

Validation

Generic Scorecard

External Data SourceScorecard Vendor

Outsourced

Scorecard Monitoring

Implementation

business intelligence & data mining-3-4

Documents