business intelligence & data mining-5
TRANSCRIPT
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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
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Observation / Performance Timeline
Observation
Snapshot
Performance
Snapshot PRESENT
PAST
Observation
Period
Performance
Period
FUTURE
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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
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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
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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
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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
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50
60
70
S c o r e s
G/B Odds
Scores
Ratio of current balance to maximum lifetime balance (all credit cards)
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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
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Model Build
0
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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.
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Logistic Regression
0
0.2
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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.
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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
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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.
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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
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Lorenz Curve
10%
0
0. 1
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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
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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 ...
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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
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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
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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?
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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
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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
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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
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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%
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• 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
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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
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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
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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;
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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