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This presentation cannot be reproduced or shared without FICO’s express consent.© 2009 FICO Corporation. 1
An Introduction to Predictive AnalyticsFor Business Rule Developers
Mac Belniak Principal Sales Consultant, Model Builder
Monday, August 24, 2009
© 2009 FICO Corporation.2 © 2009 FICO Corporation..2
Agenda» Decisions & Uncertainty
» Predictive Analytics
» Analytics & Rules
» Model Builder Demonstration
» Decision Management
» Q&A
© 2009 FICO Corporation.3 © 2009 FICO Corporation..3
Agenda» Decisions & Uncertainty
» Predictive Analytics
» Analytics & Rules
» Model Builder Demonstration
» Decision Management
» Q&A
© 2009 FICO Corporation.4
Uncertainty clouds decision making
Approve/DeclineLoan
DelinquencyHistory
CurrentLimit
CurrentBalance
Profit/Loss
Default?
Dec
isio
n Ti
mel
ine
Odds ofDefault
© 2009 FICO Corporation.5
Predicting if an event will occur parts the clouds
» Binary outcome models» What are the odds an event will occur in a future
time period?» E.g. We can’t know if a borrower will not miss a
payment next year.» But we can predict the odds that they will not miss a
payment next year!» 10:1
» The prediction can help inform future decisions.» Examples
» What are the odds that a prospect will respond to a campaign?
» What is the likelihood that this transaction is fraudulent?» What are the odds that a customer will attrite if I lower
their credit limit?
© 2009 FICO Corporation.6
Predicting amounts brings even more clarity
» Continuous outcome models» What will an amount be in a future time period?» E.g. We can’t know how much we will lose if a
borrower defaults.» But we can predict the amount that will be lost!
» $106,000
» The forecast can help inform future decisions.» Examples
» How many dollars will the prospect spend if they respond to the campaign?
» How much will a cardholder’s utilization change if I increase their credit limit?
© 2009 FICO Corporation.77
Agenda» Decisions & Uncertainty
» Predictive Analytics
» Analytics & Rules
» Model Builder Demonstration
» Decision Management
» Q&A
© 2009 FICO Corporation..
© 2009 FICO Corporation.8
Predictive models have a natural lifecycle
Access Prepare Explore
Data
Discover Approve Deploy
Model
Rapid scorecard
development Streamlined model review
Fast migration into decision application
© 2009 FICO Corporation.9
Historical data is a key input for predictive modelingPe
rfor
man
ceD
evel
opm
ent
3 M
onth
s24
Mon
ths
History Behavior Measurement
Tenure?Months since account opened.
Delinquency?
30 days delinquent in last year.Search for Credit?
# Inquiries last 6 months.Utilization?
Balance / Credit Limit.
Target:Good or bad?
Was the customer 30, 60, or 90 days delinquent or charged off during the
performance period?
2007
2008
2009
© 2009 FICO Corporation.10
Predictive models learn from historical patterns
Target Variable
Custom
er ID
Mon
ths since accoun
t ope
ned
30 days de
linqu
ent in last year
# inqu
iries last 6 mon
ths
Balance / Cred
it Lim
it
Goo
d or Bad
000000001 9 0 1 30% 0000000002 2 0 0 64% 0000000003 11 0 2 100% 0000000004 7 1 1 84% 1000000005 8 0 1 30% 0000000006 17 0 0 37% 0000000007 3 0 1 83% 0000000008 11 0 1 41% 0000000009 23 0 2 73% 0
Input Variables
Historical Data
Scorecard Algorithm
Model Development Predictive Model
© 2009 FICO Corporation.11
For each variable,understand if and how it predicts historical outcomes
Odd
s of
Goo
d
10:1
5:1
1:1
15:1
20:1
Total Portfolio Odds
# inquires in the last 6 months
None 1 2 3 4
© 2009 FICO Corporation.12
Find the most informative set of variablesO
dds
of G
ood
# inquires in the last 6 months
Odd
s of
Goo
d
Years at current address
© 2009 FICO Corporation.13
Variable Weight# inquires last 6 months
None 531 342 – 10 24
# times 30 days delinquent last yearNone 531 242 633 – Max 33
Months since account opened0 – 36 4537 – Max 24
Variable Weight# inquires last 6 months
None 641 552 – 10 48
# times 30 days delinquent last yearNone 741 603 534 – Max 45
Months since account opened0 – 36 5137 – Max 75
Train the predictive model
Target Variable
Custom
er ID
Mon
ths since accoun
t ope
ned
30 days de
linqu
ent in last year
# inqu
iries last 6 mon
ths
Balance / Cred
it Lim
it
Goo
d or Bad
000000001 9 0 1 30% 0000000002 2 0 0 64% 0000000003 11 0 2 100% 0000000004 7 1 1 84% 1000000005 8 0 1 30% 0000000006 17 0 0 37% 0000000007 3 0 1 83% 0000000008 11 0 1 41% 0000000009 23 0 2 73% 0
Input Variables Scorecard Algorithm
© 2009 FICO Corporation.14
Generate scores and rank order the customersCu
stom
er ID
# inqu
iries last 6 mon
ths
30 days de
linqu
ent in last year
Mon
ths since accoun
t ope
ned
000112354 1 0 9…
Input Variables Variable Weight# inquires last 6 months
None 641 552 – 10 48
# times 30 days delinquent last yearNone 741 602 533 – Max 45
Months since account opened0 – 36 5137 – Max 75
55
74
+
51
+
= 180
150 175 200 225
GoodBad
Score
© 2009 FICO Corporation.15
Evaluate the quality of the ranking
Customers Sorted by Scores
10%
20%
% B
ad C
usto
mer
s B
elow
the
Scor
e
30%
40%
50%
60%
70%
80%
90%
100%0%
WorstCustomers
BestCustomers
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
The 20% of customers with the lowest score cover over
70% of the bads!
© 2009 FICO Corporation.1616
Agenda» Decisions & Uncertainty
» Predictive Analytics
» Analytics & Rules
» Model Builder Demonstration
» Decision Management
» Q&A
© 2009 FICO Corporation..
© 2009 FICO Corporation.17
Measuring benefitof analytics with champion/challenger
Champion: The rules and models that currently automate decisions.Challenger: Alternative rules and models that are likely more effective.Champion/Challenger: Running the challenger on a small fraction of the population to verify that it is better.
IF account_number ends in 0-8Champion
IF behavior_score > 650&& utilization > 70%…
THEN up limit by 10%
Champion$ Profit / Account
Challenger$ Profit / Account
ELSEChallenger
IF behavior_score_1 > 750&& utilization > 80%…
THEN up limit by 5%
© 2009 FICO Corporation.1818
Agenda» Decisions & Uncertainty
» Predictive Analytics
» Analytics & Rules
» Model Builder Demonstration
» Decision Management
» Q&A
© 2009 FICO Corporation..
© 2009 FICO Corporation.19
Model Builder supports the entire analytic lifecycle
Access Prepare Explore Model Deploy
» SAS Datasets» Special Values
» Teradata» Oracle» SQL Server» Fixed-Width &
Delimited Text; ASCII & EBCDIC
» MB Native Files
» Replace Missing» Sample
» Stratified» Random
» Partition Train/Test» Sort» Join/Merge» Append» Filter/Where» Variable Creation
» Arrays, Regular Expressions, etc.
» View Table» Statistics
» Summary» Frequency» Correlation» By-Variable
» Dataset Comparison
» Linear Regression» Logistic
Regression» Neural Networks» Reason Codes» K-Means & Bang
Clustering» PCA» Evaluation
Reports» ROC, GINI, KS,
etc.
» Services-Oriented-Architecture» Batch» Transactional
» PMML» Blaze Advisor» Teradata
» Scorecard Tracking» Char. Analysis» Population Stability
© 2009 FICO Corporation.20
Historically, deployment has been expensive
» Traditional technique» Document model» Ask IT to recode» Lengthy testing
» How many € £ $ lost per day?
Time To Deploy Predictive ModelsSelf-Reported Measures, Global Financial Companies
PredictiveModelSpecs
CompiledExecutableIT Software
Development
SQL inDatabase
© 2009 FICO Corporation.21
Quickly deploy via Blaze Advisor
» Imported models are available to rule developers and authorized business users can see and modify them
» PMML integration
ImportDM
RepositoryRule Service
.NET
Rule Service
Java
Code Gen
COBOL
Model Builder
© 2009 FICO Corporation.22
Model Buildersupports scorecard tracking to trigger model refresh
» Easily evaluatepopulation stability
» Track shifts in score distribution
» Analyzecharacteristic-level changes
© 2009 FICO Corporation.23 © 2009 FICO Corporation. 23
DemonstrationBehavior Risk Scorecard
Import Data from SASCheck the DataFind Predictive PatternsSelect Best PredictorsEvaluate Scorecard QualityExport to Blaze Advisor
© 2009 FICO Corporation.2424
Agenda» Decisions & Uncertainty
» Predictive Analytics
» Analytics & Rules
» Model Builder Demonstration
» Decision Management
» Q&A
© 2009 FICO Corporation..
© 2009 FICO Corporation.25
Fast, Fast, Fast
» Stronger predictive models in less time
» Accelerated analytic review & approval
» Rapid deployment into production systems
Access Prepare Explore
Data
Discover Approve Deploy
Model
Minimizing Time to Better Decisions with Model Builder
© 2009 FICO Corporation.26
Customer Lifecycle SolutionsACQUIRE MANAGE PROTECT
Marketing Origination Customer Management
Collections & Recovery
Fraud
Decision Management Applications
Marketing Solutions
Capstone®
Decision ManagerLiquidCredit®
Service
TRIAD™adaptive control
system
Debt ManagerRecovery
Management System
Falcon®
Fraud Manager
Lifecycle Analytics and Services
PreScore®
ServiceOrigination and Small Business
Scores
Transaction Risk Scores
Debt Placement Services
Collection Scores
CardAlert Service
Across the LifecycleAnalytics Industry Standard and Custom
Credit Bureau Scores: FICO® Score Global FICO® Score FICO® Expansion ScoreInsurance Scores FICO Credit Capacity Index™ MyFICO®
Custom Analytics: Predictive Analytics Optimization Portfolio AnalyticsDecision Management Tools
For Application Developers and In-House Analytics TeamsBusiness Rules Management: FICO Blaze Advisor®
Model Development: Model BuilderOptimization: Decision Optimizer Xpress-MP Suite
Professional Services
Analytic ConsultingBusiness and Technology Consulting
FICO address all stages of the customer lifecycle
© 2009 FICO Corporation.27
AnalyticCapabilities
SharedWorkflows
Data
Create value by connecting decisions
Decision ManagementApplication
Smarter DecisionDecisionLogic
Other Applicationsand
Lifecycle Areas
© 2009 FICO Corporation.28
FICO is the leader in Decision Management
“FICO is where the mathematical approach to problem-solving that is inherent in today’s scores and analytics all began.”
— William Blair & Company
PIONEER Introduced many analytic breakthroughs, including credit scoringIntegrated predictive analytics with rules management
LEADER Products that serve whole industries at high scale» 10 billion FICO scores sold a year» Reviewing 20 billion card transactions a year for fraud
PARTNER Expertise on best practices in Decision Management, including operational, legal and regulatory issuesServing many of the world’s top businesses: » 9 of the top 10 Fortune 500 companies» 2/3 of the top 100 banks in world » 2/3 of top US P&C insurers» 1/2 of the top US retailers
© 2009 FICO Corporation.29 © 2009 Fair Isaac Corporation. Confidential.29
To Learn More…Webinar: Enabling COBIT and IT Governance through Business
RulesSeptember 9
FICO Decision Management Tools User GroupSeptember 16-18
Register at: www.fico.com/events
Webinar: Business Change using BPM and BRMSSeptember 23
Or contact us: info@fico.com
+1 888 342 6336+44 (0) 207 940 8718
Visit the Decision Management Community:decisions.fico.com
© 2009 FICO Corporation.3030
Agenda» Decisions & Uncertainty
» Predictive Analytics
» Model Builder Demonstration
» Analytics & Rules
» Decision Management
» Q&A
© 2009 FICO Corporation..
This presentation cannot be reproduced or shared without FICO’s express consent.© 2009 FICO Corporation. 31
THANK YOU
Mac Belniak 847.873.3633MacBelniak@FICO.com
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