© 2015 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
An Overview of Scoring
John HadlowSenior Director - Scores
Agenda
© 2015 Fair Isaac Corporation. Confidential.2
► About FICO
► Types of Scoring
► Consumer Access to Scores
► Innovations
4 © 2015 Fair Isaac Corporation. Confidential.
FICO® Score Solution Set
► Critical in billions of decisions each year
► 90 of top 100 largest US financial institutions use FICO® Scores
► Deployed in 20 countries and counting
► Share the FICO® Scores you use with clients
► Strengthen customer relationships by adding value
► Increase profitability and growth
► Consumer Access to their FICO® Score
► Only single source for identity, credit, and FICO®
Score monitoring
Open Access
5 © 2015 Fair Isaac Corporation. Confidential.
FICO® Scores - Expertise
► FICOTM pioneered credit bureau scoring modeling technology
► FICO has partnered with all five major credit bureaus in the United States and Canada
► 150 billion FICO® scores have been sold to date
► Powers 10 billion+ decisions a year
► 70,000+ businesses rely on FICO® scores
► FICO® scores enhance decision-making► Across lenders of all credit industries
► Across various decision points such as approval/denial, marketing and portfolio management
► Use of FICO® scores has expanded into non-traditional markets such as rating agencies – “the FICO Score is an Index”
6 © 2015 Fair Isaac Corporation. Confidential.
UNITED STATES of AMERICA
CANADA
ALASKA (USA)
MEXICO
COLOMBIA
VENEZUELA
BRAZIL
PERU
BOLIVIA
HONDURAS
NICARAGUA
ECUADOR
GUYANA
SURINAMEFRENCH
GUIANA
COSTA RICA
PANAMA
GUATEMALA
CUBA
PARAGUAY
ARGENTINA
URUGUAY
CHILE
GREENLAND
ICELAND
UNITED
KINGDOM
REPULIC OF
IRELAND
NORWAY
SWEDEN
FINLAND
DENMARK
ESTONIA
LATVIA
LITHUANIA
POLANDBELARUS
GERMANY
CZECH
REPUBLIC
NETHERLANDS
BELGIUM
FRANCE
SPAIN
PO
RT
UG
AL
SWITZ.
AUSTRIA
SLOVAKIA
HUNGARY
ROMANIA
BULGARIA
ITALY
UKRAINE
TURKEYGREECE
SYRIA
IRAQ
SAUDI
ARABIA
YEMEN
OMANUAE
EGYPTLIBYA
ALGERIA
MOROCCOTUNISIA
WESTERN SAHARA
MAURITANIAMALI
NIGER CHAD
SUDAN
ETHIOPIA
SOMALIAUGANDA
SENEGAL
GUINEA
LIBERIA
COTE
D’IVOIRE
BURKINA
GHANA
NIGERIA
CAMEROON
CENTRAL
AFRICAN REPUBLIC
GABON CONGODEMOCRATIC
REPUBLIC OF
CONGO
KENYA
TANZANIA
ANGOLA
ZAMBIA
NAMIBIA
BOTSWANA
ZIMBABWE
REPUBLIC
OF SOUTH
AFRICA
MADAGASCAR
RUSSIAN FEDERATION
KAZAKHSTAN
GEORGIA
IRAN
UZBEKISTAN
TURKMENISTAN
AFGHANISTAN
KYRGYZSTAN
TAHKISTAN
PAKISTAN
INDIA
CHINA
NEPAL
MYANMAR
THAILAND
SRI
LANKA
MONGOLIA
NORTH
KOREA
SOUTH
KOREA JAPAN
TAIWAN
CAMBODIA
LAOS
VIETNAM
PHILIPPINES
MALAYSIA
INDONESIA
PAPUA
NEW GUINEA
AUSTRALIA
NEW
ZEALAND
Global FICO® Scores Footprint
* Source: World Bank. 2013. Doing Business 2014: Understanding Regulations for Small and Medium-Size Enterprises. Washington, DC: World Bank Group. DOI: 10.1596/978-0-8213-9984-
2. License: Creative Commons Attribution CC BY 3.0
Credit Information Legend *
Full Data Sharing
Negative-only Data Sharing
No Data Sharing
FICO Score Deployment Legend
FICO® Score Deployed
FICO® Score Available at Credit Bureau
8 © 2015 Fair Isaac Corporation. Confidential.
FICO® Scores, Bureau Scores
► The FICO® Score:►Is a credit score that summarizes a consumer’s or business’s credit risk, based
on a snapshot of their credit report, history at a particular point in time
►Helps lenders and others predict how likely consumers or businesses are to make their credit payments on time
►Affects whether consumers or businesses can access credit and how much they pay for credit cards, auto loans, mortgages and other kinds of credit
►Is the most widely used credit score
► Other Bureau Scores►May include or exclude some of the above
► BI Scores►Either driven primarily by consumer data or balance sheet and other
information: SME is primarily consumer data
9 © 2015 Fair Isaac Corporation. Confidential.
FICO® Score – A Family of Scores
Global FICO Score
FICO Score
Propensity Score
Economic Impact Score
Application Fraud Score
Credit Capacity
Score
Alt-Data Score
Collection Score
SME Score
Household Score (Non
US)
Industry Scores
Invitation to Apply Score
Expansion Score
Insurance Score
Medication Adherence
Score
Revenue Score
Bankruptcy Score
10 © 2015 Fair Isaac Corporation. Confidential.
► Developed using FICO’s proprietary modeling platform and segmented scoring technology
► Scores designed as “second chance” model to address unscoreableapplicants
► Includes FCRA compliant alternative data sources & bureau data
► Same look and feel as the FICO® Score► Scaling
► Reason codes
► Attention to regulatory compliance using palatable predictive data
Alternative Credit Data Score
11 © 2015 Fair Isaac Corporation. Confidential.
US Alternative Data Score
► FICO has undertaken extensive research in the use of alternative credit data for assessing consumer credit risk
► We believe that a combination of three data sources provide best benefit in scoring consumers with sparse or no traditional credit data► Traditional credit data (as available)
► Property and public record data (LexisNexis)
► Cellular and utility billing information (NCTUE managed by Equifax)
► Initial models demonstrate good KS ranges for FICO unscoreables
► FICO and our partners will be conducting a pilot in the near term for card issuers interested in evaluating these scores
► We would like to gauge your interest in pilot participation
12 © 2015 Fair Isaac Corporation. Confidential.
Expert and other Scores- Example: Global FICO® Score
► Global standard for consumer credit risk assessment
► Evaluates consumer credit risk on any form of consumer credit bureau data
► Can be deployed in almost any geography with local credit bureau information or local bank information
► Adopted by global financial institutions to ensure consistent portfolio and risk management assessment across global portfolios
► Compliance► Rating Scale Methodology – Risk calibration across portfolios and countries – enables
capital allocation optimization
► Model Governance (OCC 11/12 Compliant)
► Leverages the fact that a primary driver of score performance is the data
► Has been shown to outperform empirically developed scores
13 © 2015 Fair Isaac Corporation. Confidential.
Application Fraud Score
Provides lenders with the probability that a credit application may be high risk (high probability of
fraud)
Delivered through many platforms and can use data from over 23 bureaus globally
Assesses fraud risk of each credit application by scoring at the time of origination
Solution provides a score from 1-999: Low number is low risk of fraud, high number is higher risk of
fraud
Origination fraud scores have been shown to give 5-30% improvement in
detection performance compared to rules based systems
14 © 2015 Fair Isaac Corporation. Confidential.
Types of Application Fraud Detected
First-party fraud in
credit cards worldwide
cost $18.5BN in 2012
and will rise to
$28.6BN by 2016
Fraudulent obtaining of credit (often by
falsifying information) without intending to
pay it back
FIRST
PARTY
Involves identity theft and fraud that is
committed without the knowledge of a person
whose identity is used to commit the fraud
THIRD
PARTY
© 2013 Fair Isaac Corporation. Confidential.14
15 © 2015 Fair Isaac Corporation. Confidential.
Propensity Scores focus on WHEN and not IF
Traditional Scorecards
A cross-sectional data analysis paradigm
Time-to-Event Scorecards
A longitudinal data analysis paradigm
Focus is on if event will happen, not when
Predictive information is summarized at
observation point.
Fixed scorecard building period
Focus is on when event will happen
Predictive information is exploitable right up to
the time period prior to the target event
Moving time windows
Obs Perf
TimeDiscrete
Time Periods
Target event yes/no? Target event at
time period k
Joe
Mary
No target
event until
censoredMax
Snapshot 1 Snapshot 2 Many snapshots
Predictive information
16 © 2015 Fair Isaac Corporation. Confidential.
FICO SME Score - Overview
► Provides credit grantors with an effective tool for rank-ordering the credit risk of small and medium enterprises, the higher the score the lower the risk.
► Applies to different product types, (loans, leases & trade credit) and can be used throughout the credit lifecycle
► Enables faster, more consistent and profitable credit decisions
► Reduces delinquency and charge-off losses
17 © 2015 Fair Isaac Corporation. Confidential.
FICO® SME Score - Blended Model
FICO® SME Score
“165”
Shareholder 1 Data Application
Consumer CreditBureau Report
Business Data Financials
Business CreditReport
Shareholder 2 Data Application
Consumer CreditBureau Report
FICO® SME Score
“190”
18 © 2015 Fair Isaac Corporation. Confidential.
Importance of Compliance with International Regulation
► Global shift from principle based governance to demonstrable governance
► The additional monetary and other exposures (public and political) new regulatory requirements will impose on our international customers
► Compliance with new and ever-increasing global regulations is time consuming, resource intensive and a management distraction
► New regulations are forcing financial institutions into a higher level of proof of global risk standardization in addition to greater governance proof and reporting than ever before
De-regulation
Re-regulation
Market cycles
Dodd-Frank BASEL III
Risk-based Pricing
Udall
Financial Policy Committee
Durbin
© 2014 Fair Isaac Corporation. Confidential.20
Industry Pressures to Increase Consumer Education &
Transparency Provide Opportunity
Regulatory focus on consumer business practices
Proposals for “free score” legislation and regulation
FICO helps financial services clients address these pressures and capture the opportunity
Appeals for increased consumer education
Initiatives to build customer trust, loyalty & satisfaction
21 © 2015 Fair Isaac Corporation. Confidential.
► Clients (Lenders) make available for FREE for their customers
► Helps enable clients to provide the actual FICO®
Score purchased to make customer decisions
► Helps address issues in an industry facingregulatory and other pressures
► Helps drive increased online engagement, share of wallet, loyalty and customer satisfaction
Example: FICO® Score Open Access—a Valuable
Program that…
Without additional score fees charged by FICO*
*Consumer Reporting Agency may charge a fee
21
22 © 2015 Fair Isaac Corporation. Confidential.
FICO® Score
How FICO® Score Open Access Works
Provide Consumer Empowerment Tools
FICO® Score Meter
12-month Graphical Trend
Two Score Factors (reason codes)
Credit Educational Content
Frequently Asked Questions
23 © 2015 Fair Isaac Corporation. Confidential. Source: Yahoo Finance News, Image – whitehouse.gov
Strong Support from Key Industry Stakeholders
Source: New York Times
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Interesting and Hard Problems to Solve
► Income Estimation
► Big Data and Machine Learning
► Using Unstructured Data
26 © 2015 Fair Isaac Corporation. Confidential.
Income Estimation Score: Development Data Specifics
► FICO undertook a proof of concept to determine whether we could build an empirically derived income estimator based exclusively on credit bureau data
Key Features
Development Data Verified income from mortgage applications
Sample is from loans opened between the years of October 2005
and April 2012
Model Coverage Annual, individual income predicted up to $600,000
Predictive Data Sources Credit Bureau data
Output Point estimate of income
27 © 2015 Fair Isaac Corporation. Confidential.
Model Results
► POC Model results were benchmarked to an existing income estimator
FICO Income EstimatorExisting Income Estimator in
the Market
Output Point estimate of income Range estimate of income
Development data Verified income Aggregated from census data
Model Coverage Up to annual income of $600,000 Up to annual income of $150,000
Predictive Data Sources Credit Bureau Data Demographic Data
R – Squared Value 0.408* 0.160
10% Percent Prediction Error 18.9% 14.1%
* R-Squared value as high as 0.571 depending on data availability – such as using mortgage related variables like “original loan amount”
28 © 2015 Fair Isaac Corporation. Confidential.
Use Case - Reasonability Check at Origination
► Utilize debt to income ratios established by policy to determine initial loan amount.► E.g., $50,000 annual income qualifies for a $10,000 credit card limit.
► Start with a point estimate of income for the applicant.
► Apply an empirically derived confidence interval as a filter to bring the estimate to a specified level of certainty.
► Resulting output for consumer: income is at least $50,000 with 95% confidence.
► Advantage –► Using a confidence level avoids overestimation of income (conservative approach).
► Disadvantage► Confidence level could restrict some borrowers with higher incomes from qualifying for a more
substantial offer of credit.
► However, there is a point where the additional income may make little difference in the amount of credit offered – perhaps, less of a concern for high incomes.
29 © 2015 Fair Isaac Corporation. Confidential.
Practical Scoring Using Big Data
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Big Data creates new opportunities for credit scoring:
Predict consumer behavior more accurately
To harness the value of Big Data for credit decisions requires transparent
analytics: Comprehensible scores, justifiable decisions
Data-
drivenValuable
Decisions
Effective, transparent analytics balances
data with domain expertise
Expert
guided
30 © 2015 Fair Isaac Corporation. Confidential.
Training Data Prediction Function
Predictors
Ou
tco
me
s
Tree 1
Tree 2
Tree 500
Combine predictions from
500 trees
Tree Ensemble – a powerful black box
Predictors
Random Forest
Gradient Boosting
?
Scored!
New case
Raw Machine Learning Approach to Scoring
Sco
re
31 © 2015 Fair Isaac Corporation. Confidential.
CONS
Difficult to incorporate domain expertise into black box
Decisions based on models can sometimes be hard to justify
But handle with care.
Vulnerable to data limitations. Sometimes non-intuitive associations
PROS
Minimal assumptions on customer behavior
Supports discovery of new, unexpected predictive associations
Highly automated, productive data analysis
PROS and CONS: Raw Machine Learning Tools for Credit Scoring
Embrace!
32 © 2015 Fair Isaac Corporation. Confidential.
0 50 100
100
50
0
% B
ad
sR
eje
cte
d
S/c #1 S/c #2 S/c #3 S/c #4 S/c #5
Segmentation derived by Scorecardizer
% Goods Rejected
5-fold cross-validated AUC
Tree Ensemble (not comprehensible) 0.96
Scorecardizer (comprehensible) 0.94
Traditional Scorecard (comprehensible) 0.91
Age of oldest tradeline (mts.)
Value of current property($)
Results: Tradeoff curves (Test set) Home Equity Loan data
33 © 2015 Fair Isaac Corporation. Confidential.
Scoring with Unstructured Data
► Many additional sources of data►Call centres
►Social media
►Comments in bureaus, other data storage
► It works
► It adds value
► In the absence of traditional data it is valuable
34 © 2015 Fair Isaac Corporation. Confidential. 3
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Cumulative Fraction Good
Cum
ula
tive F
raction B
ad
Winner: Model using regular and text-based variables
Close 2’nd: Model based on regular data only
For comparison: Model based on text data only
Predictive Lift From Combining Regular and Text Data
35 © 2015 Fair Isaac Corporation. Confidential.
Unstructured Data Scoring: Main Takeaways
► Embrace the power of new data sources, such as unstructured text
► Powerful analytic approaches such as topic modeling and semantic scorecards allow you to comprehend the value and meaning of text data for predicting consumer behavior
► Existing Structured Data is still the most valuable source-unless you don’t have information about a consumer or business
► The tools are already here to build the scores – the challenge is to operationalize them and make them compliant with legislation
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