Modelling the 3600 Customer View
Neill Crossley
Consulting Director, Analytic Solutions
FICO
16 November, 2010
Confidential. This presentation is provided for the recipient only and cannot
be reproduced or shared without Fair Isaac Corporation's express consent.
1
© 2010 Fair Isaac Corporation.
Agenda
» Introduction to Predictive Analytics
» Trends in Data
» Trends in Predictive Models
» Benefits of Frictionless Model Deployment
» Summary
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© 2010 Fair Isaac Corporation. Confidential.
© 2010 Fair Isaac Corporation. Confidential.
Introduction to Predictive Analytics
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© 2010 Fair Isaac Corporation. Confidential.
What is Predictive Analytics?
“Predictive Analytics encompasses a variety of
techniques from statistics, data mining and game
theory that analyze current and historical facts to
make predictions about future events.”
“One of the most well-known applications is
Credit Scoring, which is used throughout
financial services.
Scoring models process a customer’s credit
history, loan application, etc., in order to
…rank-order individuals by their likelihood
of making future credit payments on time.”
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© 2010 Fair Isaac Corporation. Confidential.
Predictive Analytics Give You
Better Predictions, Better Decisions
Descriptive
Models
Profiling &
Segmentation
Predictive
Models
Predictive
“Scores”
Decision
Models
Decision
Modelling &
Optimisation
Decision
Strategies
Economic
Forecast
p(able to
pay)
Bureau Data
(FICO Score,
Bureau debt)
p(foreclose)
Monthly
Payment
Income,
household
size, etc.
Current loan
amount, interest
rate and term
PV(Loss |
foreclosure)
Interest Reduction;
Principal Reduction;
Term Externsion
p(walk away)
NPV
PV(Revenue |
good)
Client predictive
models and other
key data
Future
Value of
Home
Time to
foreclosure
Current Value
of Home
Region
Establishes broad
segments based
on customer
profile data
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© 2010 Fair Isaac Corporation. Confidential.
Rank orders
prospects /
customers on a
single dimension
Combines Predictive
Models with
business rules to
create Micro
Segments
Complete Decision
Framework
Assigns the best
action given specific
business constraints
Achieving a 3600 Customer View
ASSETS
CHARACTER
INCOME
Current
Account
Overdraft
ABILITY
Savings
Credit Card
Accounts
Accounts
BORROWING
3600
Basel II
PREFERENCES
Customer
View
Retirement
Accounts
Mortgage
Accts
Installment
Loan
Non
Financial
Transactions
INTERESTS
LIFESTAGE
SECURITY
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© 2010 Fair Isaac Corporation. Confidential.
COMMITMENTS
Analytics Impact Business Results
… at All Points of the Life Cycle
Predictions
Decisions
Acquisition
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
Whom to target

What product to
offer
Approve/decline

Line management

Collection priority

Credit amount

Re-pricing/renewal

Collection action

Price

Transaction
authorizations

Channel placement

Agency placement
Channel

Features

Timing

Up-sell

Capital

Response

Revenue

Risk

Capacity
© 2010 Fair Isaac Corporation. Confidential.
Collections
& Recovery



Customer
Management
Origination

Cross-sell

Capital
Risk (Loss/BK)

Risk (Loss/BK)

Bankruptcy

Revenue

Revenue

Charge-off

Capacity

Capacity

Roll

Pre-payment

Pre-payment


Response to an
up-sell

Response to a
cross-sell
Expected collection
amount

Fraud

Fraud
Trends in Data
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© 2010 Fair Isaac Corporation. Confidential.
Predictive Analytics Data – 10 + years ago
Acquisition
Origination
Customer
Management
Demographic
x
X
X
Negative Credit Bureau
x
X
X
x
x
X
x
Account Behaviour
Limited Access to Clean Accurate Data
Predictive models were built using <100 variables
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© 2010 Fair Isaac Corporation. Confidential.
Collections &
Recoveries
Predictive Analytics Data - Now
Acquisition
Demographic
Negative Credit Bureau
x
Origination
Customer
Management
X
X
X
X
x
X
x
X
X
X
X
X
x
x
x
X
x
x
X
Account Behaviour
Customer Behaviour
Positive Credit Bureau
Financial Transaction
Fraud
Deposit Data
Offer & Take up
Collections
Non Financial Transaction
Geo-demographic
X
x
X
X
X
X
X
X
X
X
x
X
x
x
x
Collections &
Recoveries
x
X
x
x
Model Developers often have hundreds and thousands of potential variables
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More Data Means Typical Data Challenges
Become a Bigger Issue
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© 2010 Fair Isaac Corporation. Confidential.
Clean
Data
Deploy
Models
Create
Characteristics
Develop
Models
Problem – Amount & Correlation of Data
» Correlation has always been an issue in model development
IV = 3
IV =2
IV=1
What about the impact of correlation?
Which two Characteristics are best?
MC=1
MC=2
» Increased Problem: more data = more potential for correlation
» Can impact some statistical techniques
» Leading to unusual weight patterns
» Which of the Fully or Heavily Correlated Variables should you use?
» In what way are they correlated?
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© 2010 Fair Isaac Corporation. Confidential.
Problem – Amount & Correlation of Data
» FICO Model Builder Score Engineering technology
» Through use of Score Weight Constraints & Optimization algorithms
» Patterns, Grouping, In-weighting, Cross-Restrictions, No-Inform
» Entry-Exit based on Marginal Contribution to Divergence
» Interactive what-if analysis
» Variables Tiers – stepwise and diversity variable selection
» Looking at interactive functionality to further improve variable reduction
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© 2010 Fair Isaac Corporation. Confidential.
FICO Model Builder uses
unique technology building blocks
Fitting Objectives
Score
Formula
(Segmented)
Generalized Additive
Model
 Capture nonlinear
relations and
interactions accurately
whilst being
interpretable by
business users and
regulators
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© 2010 Fair Isaac Corporation. Confidential.
Divergence,
Range Divergence,
Bernoulli Likelihood,
Multiple Goal,
 Tune score weights to
optimize and trade-off
business objectives
Score Weights
Constraints
Patterns, Grouping,
In-weighting,
Cross-Restrictions,
No-Inform
 Impute domain
knowledge to obtain
palatable and robust
models
Optimization
Algorithms
Various Solvers, Goal
Programming,
Automatic Variable
Selection
 Quickly and reliably
solve for optimal
model.
 The FICO approach
helps overcome the
multi co-linearity
issues seen with
standard regressions.
Trends in Predictive Model Types
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© 2010 Fair Isaac Corporation. Confidential.
Trends in Predictive Models Types
Attrition / Retention Models
» “Customer retention has returned as a core value to
financial services institutions. Analytics can play a major
role.” - Tower Group
Customer Level Models
» Use all available customer information across a range of
different products, to measure the likelihood of a specified
future event, in a given time horizon.
Action Effect Models
» Action Effect models predict the outcome based on impact
of the different actions that could be undertaken e.g. how
customers react to different collections actions.
Capacity / Affordability Models
» Anticipatory risk measures that rank orders customers
according to their ability to manage new or increased debt
safely on top of their existing debt commitments
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© 2010 Fair Isaac Corporation. Confidential.
Use of Transaction Scores
Positives
» There’s lots of Transaction data
» Captures detail about a customer’s habits, lifestyle etc
» Can help you make more accurate and timely decisions
Negatives
» There’s lots of Transaction Data
» Difficult to store over time
» Difficult to operationalize & process in decision management
» Not always clean or detailed enough on all accounts
FICO has been modeling and operationalizing
transaction-based credit risk scores since 1996
»
FICO® Transaction Scores™ (FTS)
produces a new score for every transaction
so changes in risk profile are picked up
quickly
»
Based on FICO patented transaction profiling
technology used in FICO™ Falcon
»
Results in average 6% improvement in
predictive power over behaviour score
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© 2010 Fair Isaac Corporation. Confidential.
FICO Transaction Scores
UK Consortium Model Performance (KS)
% of Total
Accounts
% of Total
Bads
FITS +
Behavior
Score KS
Behavior
Score KS
% Lift in K-S
Current Very Mature w/
Cash (>6 years time on books)
9.2%
8.8%
62.15
54.68
13.7%
Current Mature w/ Cash (7
11.4%
20.1%
48.35
42.51
13.7%
Current Young w/ Cash (<7
2.2%
6.1%
49.28
44.06
11.9%
Current Mature w/o Cash
w/ Frequent Transactions
52.0%
15.6%
67.47
61.88
9.0%
Current Mature w/o Cash
w/o Frequent Transactions
18.3%
8.4%
60.92
57.01
6.9%
Current Young w/o Cash
4.5%
2.0%
48.59
38.19
27.2%
Delinquent 1 Cycle
2.0%
22.3%
48.75
45.59
6.9%
Delinquent 2 Cycle
0.4%
16.7%
39.88
38.04
4.9%
Overall
100%
100%
70.67
66.70
6.0%
Segment
mo – 6 years time on books)
mo on books)
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© 2010 Fair Isaac Corporation. Confidential.
Based on FITS UK Consortium Models v.1.0 – Out-of time validation results (Sep ‘08, Mar ’09, Sep ‘09)
FICO Transaction Scores –
Timing Impact in identifying Bad accounts
A larger amount of bad accounts were triggered first by the
transaction score, than by the cycle score
Transaction scores triggered first
Cycle scores triggered first
Percentage of 3+ Cycle Accounts
Triggered at the same time
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100%
80%
60%
72%
65%
40%
20%
5%
6%
60%
7%
53%
6%
29%
34%
41%
23%
5%
10%
15%
20%
0%
© 2010 Fair Isaac Corporation. Confidential.
Score Cutoffs - Predicted Bad Rate
Model When Things Will Happen – Time to Event
Issue
» Traditional models focus on whether something is likely to occur, not when
» Significant information from existing data being overlooked
» May significantly impact key business decisions:
» When to increase a limit
» When to offer another product
» Whether to accept a loan application
Example:
» A low risk Instalment Loan applicant with no other relationship with the Bank
» Is predicted to repay in full within 4 months and thus may actually be value destroying
FICO has been pioneering Time to Event Models over the last 5-6 years
»
Have been successfully utilised in innovative work
with a number of US Retailers – large increases
in offer take up rates
»
Showing potential applications in Financial Services:
»
»
»
»
»
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Predict time to default?
Predict time to closure?
Predict mortgage prepayment?
Predict duration of a call center call?
Predict time to up-sell a product enhancement?
© 2010 Fair Isaac Corporation. Confidential.
Model Economic Conditions - Economic Impact Service
Issue
“We have been driving a car by looking out the rear view mirror”
» Models have been built ignoring the impact of past economic conditions
» And how these may change - “The future is never exactly like the past”
Requirements going forward
» “Be anticipatory, not just reactive” “Incorporate a forward looking view of our business”
» Regain control of your portfolio
» Increase confidence in your risk management tools, even in these times of change
FICO Economic Impact Service
» Helps translate how current and projected market
conditions will impact the expected risk levels
associated with given score bands
» Clients can more scientifically adjust strategies to
reflect the upcoming changes in risk
» Thus can help:
» Limit losses; Grow portfolio responsibly; Better prepare
for the future; Meet regulatory compliance
» Broadly applicable to and easily integrated with a
variety of score types and uses
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© 2010 Fair Isaac Corporation. Confidential.
Model Impact of Decisions – Decision Models
Issue
» Decisions made by Financial Services company can significantly impact the outcome
» Lend too much, customer may not be able to afford, increasing likelihood of default
» Not lend enough, customer may go to a competitor
Requirements going forward
» Understand the impact of different decisions on customers
» Understand all impacts of different decisions on the components of Profit
» Then identify the Sets of Decisions that bring the most profit but also do not break key
business goals and constraints e.g. Annual Portfolio Losses can not exceed €50m.
» Measure against expectations
FICO has been a pioneer in Decision Modeling & Optimization over the last 10 years
» FICO’s
approach provides an Open Framework &
Methodology for modelling decisions.
»Builds
on existing Scores & Systems
»Focused
»Refined
»Proven
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on Business Goals & Constraints
on over 100+ projects
Business Benefits
© 2010 Fair Isaac Corporation. Confidential.
Post-Crisis Predictive Analytics
Summary
Data
» Utilise all relevant internal and external data
» But be practical – cost v benefit
» Look out for tools and techniques to help:
» Identify the most relevant data
» Operationalize data
» Focus on breadth of Data
» Experimental Design / Learning Strategies
Models
» Model the Decision to optimize performance
» Factor in Macro Economic forecasts
» Simulate Strategies to understand potential
future impacts & set expectations
» Balance Automation with Expertise
» Justify complexity and increase transparency
» Look to understand the 3600 view of the
customer, but from their perspective
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© 2010 Fair Isaac Corporation. Confidential.
Post-Crisis Predictive Analytics
Summary
Some Phrases to consider:
“Don’t’ tell me what you think, but what you know”
“If you can’t measure it, you can’t manage it”
“ All Models are wrong, some are useful”
“Show me the data, explain why I should trust the model and tell me where
its weaknesses are that I need to be aware of”.
“No one doubts that more data and more relevant data leads to better
models. The winners, however, won’t just use that data to build
better models – they will use it to ask better questions.”
Dr Andrew Jennings, Chief Research Officer, FICO
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© 2010 Fair Isaac Corporation. Confidential.
Benefits of Frictionless Model Deployment
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© 2010 Fair Isaac Corporation. Confidential.
Deploying Models Is Not Always Simple
» Traditional technique
» Document model
» Ask IT to recode
» Lengthy testing
Predictive
Model
Specs
IT Software
Development
SQL in
Database
» How many £ lost per day?
Time To Deploy Predictive Models
Self-Reported Measures, Global Financial Companies
FICO Survey 2008
% responders
40
30
20
10
0
1-4 weeks
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1-3 months
Compiled
Executable
3-9 months
9 or more
months
Frictionless Model Deployment
Has Developed Over the Last Few Years
FICO Model Builder
» Imported models are executed as
Java components in the decisioning
service. Rule developers and
business users cannot see nor
modify them
Rule Service
Decision
Management
Repository
Rule Service
» Imported models are available to rule
developers and authorized business
users can see and modify them
» PMML integration
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© 2010 Fair Isaac Corporation. Confidential.
Java
.NET
Code Gen
COBOL
FICO Model Builder
Supports Parallel Development & Deployment Approach
FICO Model Builder
Production
Data
Translate
Metadata
Execute
Variables
Execute
Model
Test
Model
Deployment
Environment
Direct
Deployment
Other Data
Sources
Data Load
&
Transform
Other
Modelers
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© 2010 Fair Isaac Corporation. Confidential.
Create
Variables
Share
Build
Model
Modeling
Repository
Validate
Model
Rebuild
model
Monitor
Validate
Which can be further extended to create the
FICO Analytic Platform
Lowers your lost opportunity and model
management costs
Features
Centralised Model Repository
Across Geographies, Portfolios, Decision Areas
Faster model deployment
Models editable in production
Central Characteristic Library asset
Model lifecycle management
Model monitoring and reporting
Simple reuse, redeployment and refresh
Model and Decision simulation
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© 2010 Fair Isaac Corporation. Confidential.
What is an Analytic Platform?
An integrated solution that can manage the whole predictive model lifecycle.
Data
Preparation
Development
Prioritisation
Model
Realign,
Reweight &
Refresh
Manage
Development
Assets
MODEL
LIFECYCLE
Lifecycle
Management
Model
Monitoring
Manage Use
in Production
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© 2010 Fair Isaac Corporation. Confidential.
Development
Testing,
Validation &
Simulation
Deployment
THANK YOU
Confidential. This presentation is provided for the recipient only and cannot
be reproduced or shared without Fair Isaac Corporation's express consent.
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© 2010 Fair Isaac Corporation.