Business Intelligence & Data Mining-5

8/10/2019 Business Intelligence & Data Mining-5

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Population Identification

• The entire portfolio population is usually tooheterogeneous for a single model

• The population is segmented either based onsome broad criteria (e.g. based on age groups:

18-25, 25-45, 50-65, >65) or empirically, tosegments with widely different good / bad odds

• Scorecards need to be built for each segment

• Availability of data (both credit bureau data andinternal data) needs to be considered



Observation / Performance Timeline

Observation

Snapshot

Performance

Snapshot PRESENT

PAST

Observation

Period

Performance

Period

FUTURE



Good-bad Definition

• The accounts are classed as ‘good’ / ‘bad’ /‘indeterminate’ based on performance during the performance period. For example:

• Bad = bankrupt or 3 or more payments missed

within 9 months• Indeterminate = ever 2 payments missed or

always inactive or very low balance during 9month observation period

• Good = always up-to-date payments or ever 1 payment missed



Good-bad evolution

Good-Bad Evolution

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Months since Observation

C u m . B a d R a t e

69% of accounts that go

bad in 18 months can beidentified after 9 months



10/30/2013 35

Characteristic Selection• 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.

• Some credit bureau characteristics and some internal ones areselected based on their ‘marginal contribution’ to the outcome, and‘monotonicity’ of the odds:

0.00

2.00

4.00

6.00

8.00

10.00

12.00

1-50 50-70 70-80 80-85 85-90 90-95 95-97 97-100 100-101 Other

Bands

G o o d - B a d

O d d s

0

10

20

30

40

50

60

70

S c o r e s

G/B Odds

Scores

Ratio of current balance to maximum lifetime balance (all credit cards)



10/30/2013 36

Model Build

• Once the characteristics have been selected astatistical model can be developed.

• Multivariate statistical methods include

– Linear Regression – Logistic Regression

– Heuristics like Decision Tree / Neural Network



Model Build

0

0.2

0.4

0.6

0.8

1

0 200 400 600 800 1000

n

The model is built on dichotomous data. In this case a 1 for “Good”customers and a 0 for “Bad” customers.



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)

n

The logistic regression fits the probability better than Linearregression.



Models

• Logistic Regression has the following form:

∑ ==

−k

j j j x p

p01

ln β( )∑∑

=

=

+=

k

j j j

k

j j j

x

x p

0

0

exp1

exp

β

β



Reject Inference and Validation

• Reject Inference

– Reject Inference is necessary for application scorecards because there is no performance information for therejected applications

• Applications that are rejected should be included in the finalmodel

– 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 is used to test the model.



Comparison of Scoring Systems

ScoringSystem A

Cutoff

Bads Goods

10% % A

P P L I C A N T S

5%

Cutoff

SCORE

Bads Goods

20% % A

P P L I C A N T S

5%

ScoringSystem B



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

C u m u l a t i v e B a d s

Scorecard performance can be judged on the level of discrimination

• Two measure that can be used are:

Gini (or ROC) – the area between Lorenz Curve and random line

PH - % of Goods below 50% of bads

Gini = 62%

Measures of Discrimination



Advantages of Scoring

– Defines degree of credit risk for

each applicant

– Ranks risk relative to other applicants

– Allows decisions based on degree of risk – Enables tracking of performance

over time

– Permits known and measurable adjustments

– Permits decision automation

but BI in Consumer Lending goes much beyond ...



Maximizing Lifetime Customer Value

• Combine predictions –

risk, revenue - into asingle metric or objective

• Assign an optimal actionfor each account whilesatisfying businessconstraints

• Matrix of Segments

and Scores toincrease granularityof decisions

• Collapses

information into aScore to rank-orderpopulations basedon accountcharacteristics

• Groups of

accounts with asimilar profile

OptimizationOptimizationStrategiesPredictive

ModelsCohort or

“Segments”

Focus on Value of Customer to Organization - Lifetime Customer Value

FirstPremierNSFModelSeg1:NSFlast30 days

Scorecomparisons

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 % 1 0% 2 0% 3 0% 4 0% 5 0% 6 0% 7 0% 8 0% 9 0% 1 00

%

Cumulative%of CureAccounts

C u m u l a t i v e % o

f N o C u r e A c c o u n t s

Perfect

Random

Building

Validation

Total

Fico

Behavior score



Evolution of Methods

• Optimized

Strategies

• Optimization

Engines

• Acct-Level Profit

• Acct-Level Actions• In-Market (Closed-

loop) Testing

• Enhanced

Strategies

• Behavioral Risk

• Response

• Revenue

• Attrition

• CRM Platforms

• Simple Strategies

• Decision platforms

• Profiling andsegmentation

• Champion /

Challenger Testing

1985 1990

• Little / No data

• Criteria-basedrules

• Decision impacts

not understood

1995 2000

Increasing Customer View (Data and Models) and Competitive Pressures

Criteria Based Rules

Risk Scoring

Behavioral Scoring

Optimization





Action-Effects Models

Action-Effects models are:

• A prediction of customer reaction to an action taken

• What your models look like depend on your action set as well as what

information is known about the customer

• Action-Effects models are sensitive to actions (traditional predictive

models aim to be robust over possible actions)

Example: How does a Credit Limit Action affect customers’

behaviour?



Attrition Rate . Conseq Months Revolver L12 Months

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

£500 £1,000 £1,500 £2,000 £2,500 £3,000 £3,500CL Action

A t t r i t i o n

R a t e

>2 Months

<2 Months

Extrapolation

No Historical Inf :Extrapolation

Actio n effected the

behaviour

Example: Action-Effect Model of AttritionGiven Credit-Limit Change



Improved portfolio performance

HighCutoffs

E[Volume]

E [ L o s s ]

LowCutoffs

LowCutoffs

HighCutoffs

E[Profit]

E [ L o

s s ]

HighCutoffs

LowCutoffs

E[Volume]

E [ P r o

f i t ]

Single Score

OptimizedScores

Single Score

OptimizedScores

Single Score

OptimizedScores

Efficient Frontier



Value of Strategy Optimization

Strategy Optimization is delivering

outstanding results to over 25

world-class organisations, driving

true competitive advantage

Example users

Some Caselets Follow



New Business : Initial Credit Limit

Assignment

*Results normalised to a customer base of 1 million

Challenge

• Determine the most profitable initial credit card limits• Must not increase overall exposure or bad debt when

compared to existing rule based strategy

Solution

• Optimization used in real-time during credit card application process

• Identifies which customers receive which initial limit*

Leading

Retail Bank

Revenues

CreditLosses

Profit

Other Costs

£111.2M

£48.4M

£10.6M

£52.2M

Revenues

CreditLosses

Profit

Other Costs

£118.2M

£46.3M

£20.7M

£51.2M

+ 6%

- 4%

+ 96%

Adaptive control rules Customer level optimization Benefit

- 2%



• Generates incremental profit of £3 per account per annum when optimizing tree-based

strategies (estimated at £7 per account per annum when optimizing individual

customer strategies)

• Optimization used during end-of-month statement process

• Identifies which groups of customers receive an increase in credit limit and by how much

• Implemented through a refined strategy tree

• Determine the best increase in credit limit to maximisecustomer profitability across a 10 million customer base

• 5 different percentage-based increases to consider

• Must not increase overall exposure or bad debt when

compared to existing strategy

• Implemented through tree based logic

Leading

Credit Card Issuer

Solution

Challenge

Account Management : Limit Increase



Benefit

• Generates incremental profit of 7% over

the existing Champion strategy for same

bad debt and exposure levels

• Now considering optimal timing of

increases to coincide with seasonal

patterns in retail category spend

Solution

• Optimal limit applied during end-of-

month billing process

• Considers off line retail transaction

data to predict seasonal card usage

Challeng

e

• 5 million retail store card customers• Determine best limit increase for each eligible

customer to maximise overall retail and credit

profitability

• Must not increase overall exposure or bad debt when

compared to existing strategy

• Existing champion strategy fine-tuned over many years

Limit Increase : Large Retailer



New Business : Loan Pricing

• 13% increase in profit contribution for the same lending amount bad debt

value

Solution

• Optimization used to determine the optimal price (APR) to offer new customers

• Optimal decision based on:

• Deal profitability, Propensity to take up the offer and Credit risk losses

• Optimization applied dynamically at the individual customer level to maximise

decision performance

Challenge

• Improve new personal loan customer profitability at

point of sale

• 7 different APR rates to consider

Leading UK

Personal Finance

Lender

C Pl i T 10 UK



Contact Planning : Top 10 UK

retail bank • Shifting focus: from campaign-centric marketing to customer

data-driven forecasting of outbound contacts

• Product maturities, renewals, end of term and other cross sell

opportunities;

• Exploit a wealth of customer information to drive timely,

appropriate and profitable customer contacts

Challenge

• Optimization used to schedule million’s

of customer contacts over a financial

year

• Adheres to strict customer contact

frequency policies

• Identifies best opportunities to meet

financial year budget, sales and revenueexpectations across product marketing

units

• Finds contact opportunities missed by

previous approaches

Solution Benefit

• 20% increase in number of customer

contact opportunities in the financial

year;

• Improved predictability of sales

volumes, budgets and revenues over

time;

• Highlights gaps between customerneeds and the supporting propositions

that could be delivered;

• Scenario planning process reduced

from days to less than an hour;



Business as usual

Cross Sell : Top 5 UK retail bank

Challenge

• 10 million customers

• Cross selling within personal

finance customer base:

• Loans, Cards and Cheque Accounts

• Must not increase overall budget

spend or outbound channel usage• Maximise Net present Value

Solution

• Optimization used to select offers for

customers in monthly telemarketing

and direct mail campaigns

• Fully utilises available models

Revenue

s

BudgetProfits

£6.66M

£572K

£6.08M

Offers Sales

1.969M

14,202

- 3% + 19% + 32% + 35% - 5%

Revenue

s

BudgetProfitsOffers Sales

£8.80M

£543K

£8.25M1.914M

16,886

Benefit

Optimization

Documents

Business Intelligence & Data Mining-5