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8/10/2019 Business Intelligence & Data Mining-3-4
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Credit Scoring
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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 eliminate risk taking in order to
reduce failure rates to zero, we will, by
definition, have eliminated the purpose of the
banking system.
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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
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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
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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?
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Everyday Questions
Balancing Marketing and Risk
Will the consumer
pay as agreed?attrite too early?
build large balances?
repeat purchase?buy add-on services?
be profitable?
How will Icontinue toinfluence?
Net income
VALUE
Portfolio size
# accounts Receivables
Risk
Yield
Losses
Growth in each
Costs
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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
pay as agreed?attrite too early?
build large balances?
repeat purchase?buy add-on services?
be profitable?
How will Icontinue toinfluence?
Increase value by improvingdecisions
Use BI to optimize multipleobjectives
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Decision areas
Solicitations New applications Account management
Credit line Authorization Collections Reissue
Cross-sell
Keep / sell
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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
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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
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The BI Solution: Scoring Models
OUTCOMEMODELDATA
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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
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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
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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
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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.
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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
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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
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Bad Rates of Major FICO Score
Ranges
Score Range Bad %
300-500 48%
500-599 30%
600-699 11%
700-850 1.50%
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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
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Residence
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CREDIT BUREAUREPORTS
CREDITAPPLICATION
Sources of information
Credit reports
Application data
Public records
Prior experience
Demographics
Billing file
Deal terms
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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
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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
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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.
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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.
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ODDS
64 / 1
16 / 1
4 / 1
1 / 1
SCORE
220
180
140
100
=
=
=
=
Credit score = odds (risk)
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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.
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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
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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