Business Intelligence & Data Mining-3-4

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Credit Scoring

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.”

Types of Lending Risk

Borrower
Customer fails
failstotopay
pay
Losing money
Wrong Strategy

Change in
market
prices

Processing failures and
frauds
Regulatory compliance

The Universal Balancing Act
Profits

Profits

How can I efficiently manage resources while
meeting business and operating constraints?

Losses

How can I create and re-create strategies in a very
dynamic environment?

Unused
Capacity

How can I achieve these benefits with minimal
change to current systems infrastructure?

Attrition

Everyday Questions
Balancing Marketing and Risk
Should I
target this
consumer?

… with what
message?

Will the
consumer
hear it?

Should I
approve?

Will the
consumer
apply?

… at what
credit level?

Will the
consumer
use it?

How will I
continue to
influence?

Will the consumer…
pay as agreed?
attrite too early?
build large balances?
repeat purchase?
buy add-on services?
be profitable?

Everyday Questions
Balancing Marketing and Risk
Net income
How will I
continue to
influence?

Will the consumer…
pay as agreed?
attrite too early?
build large balances?
repeat purchase?
buy add-on services?

Costs

VALUE

Portfolio size
• # accounts
• Receivables
Risk
Yield

be profitable?

Losses
Growth in each

Everyday Questions
Balancing Marketing and Risk
Should I
target this
consumer?

… with what
message?

Will the
consumer
hear it?

Should I
approve?

… at what
credit level?

Will the
consumer
apply?

Will the
consumer
use it?

Increase value by improving
decisions
Use BI to optimize multiple
objectives

How will I
continue to
influence?

Will the consumer…
pay as agreed?
attrite too early?
build large balances?
repeat purchase?
buy add-on services?
be profitable?

Decision areas
• Solicitations
• New applications
• Account management





Credit line
Authorization
Collections
Reissue

• Cross-sell
• Keep / sell

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

10/18/2013

9

Business Objectives


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 Information
• Permits information gathering to assist business needs and
marketing activities
• Information gathered can be fed back into future scoring
systems’ developments, collection activities and strategy
optimization
10/18/2013

10

The BI Solution: Scoring Models

DATA

MODEL

OUTCOME

BI in the Consumer Credit Industry
• Numerous quantitative advances have emerged in the
consumer credit risk area to support business strategy
throughout the customer life cycle - beyond simple
credit 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

BI in the Consumer Credit Industry
• 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 and
data-rich environment for credit risk and capital
measurement.

Account Life-cycle Scoring Progression
ACCOUNT STATUS
ON TIME

DELINQUENT

LATE-STAGE
COLLECTIONS

Behavior score
Custom collection score
FICO score

RECOVERY
Bureau-based
recovery score
Custom recovery
score

Other bureau scores or
custom scores
Transaction score

TRIAD adaptive
control system
Primary Decision:
Reduce Loss

Debt Manager-RMS

Secondary Decision:
Risk-Related

Specialty Risk
Assessment

RMS, BridgeLink
Removing Credit:
Additional Precision

Risk Analytics in Consumer Lending


Credit risk in consumer banking has been (traditionally)
driven by 3 C’s 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 predict
borrower 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 report
and application data into a single actionable metric.

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 an
example of ‘Generalized Additive Models’) to arrive at a score
(using a “Scorecard”).
• The objective is to forecast future performance from past
behaviour.
• Credit scoring developed by Fair & Isaac in early 1960s
– Widespread acceptance in the US in early 80s and UK
early 90s
– FICO scores make 75% of US Mortgage loan decisions
– Behavioural scoring, introduced later, has been accepted as
more predictive than application scoring


National (US) Distribution of
FICO Scores
30%

28%

% of Population

25%

19%

20%
16%
15%
12%
10%

11%

8%
5%

5%
1%
0%
Up to 499

500-549

550-599

600-649

650-699

FICO Score Range

700-749

750-799

800+

Bad Rates of Major FICO Score
Ranges

Score Range

Bad %

300-500
500-599
600-699
700-850

48%
30%
11%
1.50%

Evaluating the credit applicants: Judgment
Versus Scoring
CHARACTERISTICS

Time at present address
Time at present job
Residential status
Debt ratio
Bank reference
Age
Income

••

# of Recent inquiries
% of Balance to avail. lines
# of Major derogs.
Overall
Decision
Odds of repayment

JUDGMENT

CREDIT SCORING

+
+
+
+
N/A
-

12
20
5
21
28
15
5

+
+
+

-7
10
35
212


••

Accept
?


••

Accept
46:1

Risk Scorecard - Example
Own
Rent
15
25
Years at
<.5
.5-2.49
address
12
10
Prof
Semi-Prf
Occupation
50
44
<.5
.5-1.49
Years on job
2
8
Dept St /
None
Dept-St
Major CC
0
11
Bank
Chq A/c Sav A/c
reference
5
10
<15
15-25
Debt ratio
22
15
No. of recent
0
1
inquiries
3
11
<.5
1-2
Years in file
0
5
# of Loan
0
1-2
Accounts
5
12
% Credit line 0-15%
16-30%
utilization
15
5
Worst
No Rcrd Maj. Def.
reference
0
-59

Residence

Other
NI
10
17
2.5-6.49 6.5-10.49 >10.49
15
19
23
Mgr
Offc.
Bl.Col
31
28
25
1.5-2.49 2.5-5.49 5.5-12.49
19
25
30
Maj-CC
Both
Several
16
27
30
Chq&Sav Other
NI
20
11
9
26-35
36-49
50+
12
5
0
2
3
4
3
-7
-7
3-4
5-7
8+
15
30
40
3-5
6+
8
-4
31-40% 41-50%
>50%
-3
-10
-18
Unsatisf 1 Satisf 2 Satisf
-14
17
24

NI
14
Retired
31
>= 12.5
39
NI
12

NI
13
5-9
-20

3 Satisf
29

Other
22
Retired
43

No Rcrd
0

NI
27
NI
20

Sources of information
CREDIT BUREAU
REPORTS
CREDIT
APPLICATION









Credit reports
Application data
Public records
Prior experience
Demographics
Billing file
Deal terms

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

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
Debit
$1344. 12
Debit
$1344.
Debit
$234.
01 12
Debit
$1344.
Debit $987.56
$234. 01 12
Debit
Debit $987.56
$234. 01
Debit
Debit
$6543.22
Debit $6543.22
$987.56
Debit
Debit
$32423.11
Debit
$6543.22
Debit $2556.00
$32423.11
Total
Debit $2556.00
$32423.11
Total
Total

$2556.00

Risk
Grading

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’.

Good/Bad Odds (probability)










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.

Credit score = odds (risk)
SCORE

ODDS

220

=

64 / 1

180

=

16 / 1

=

4/1

=

1/1









140




100













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

1
0

Ln(Odds)

-1
Actual Data

-2

Std Line

-3
-4
-5
505

515

525

535

545

555

565

575

585

595

Score

For any given score the probability of ‘Bad’ can be found
using the equation of the Log-odds (straight) line.

How Is a Credit Scoring Model
Developed ?
• Analysis of a large set of consumers (>= 1
Million)
• 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

Scorecard Construction
Data Gathering
•Population Identification
•Data Availability
•Data Extraction
•Sampling

Statistical Analysis
•Characteristic Analysis
•Characteristic Selection
•Multivariate model build
•Reject Inference

Outsourced
•External Data Source
•Scorecard Vendor

Generic Scorecard
Validation
Set cut-off Score
Implementation

Customised Scorecard
Scorecard Monitoring

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