Transcript Title of Presentation

Fundamentals
Scoring and the Credit Lifecycle
Janice Horan
Senior Director, Fair Isaac Advisors
FICO
© 2014 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.
Agenda
►Credit
and the Credit Lifecycle
►The
Role of Risk Appetite in the Profit and
Loss Dynamic
►Scoring
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and the Credit Lifecycle
© 2014 Fair Isaac Corporation. Confidential.
Credit Defined
► CREDIT:
Main Entry: 1cred·it
Pronunciation: kredt Function: noun
► Etymology:
Middle French, reputation, commercial credit, from Old Italian
credito, from Latin creditum loan, from neuter of creditus, past participle of
credere
►1
a : the balance in a person's favor in an account; also: an amount or limit to
the extent of which a person may receive goods or money for payment in
the future b : an amount or sum placed at a person's disposal by a bank : a loan
of money c : time given for payment for goods or services sold for future
payment <long-term credit>
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© 2014 Fair Isaac Corporation. Confidential.
The Credit Customer Life Cycle
External
Data
Internal
Data
Customer
Marketing
Reactions
Customer
Origination
Client Prospects
Customer
Management
Client Customers
Customer
Collections
Actions
Key Concept: Full impact of credit decisions can only be understood when examined in
light of their impact on the success of the next stage of the credit life cycle
Customer Marketing:
Originations/Underwriting:
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Who is the target customer, and how is interest created in the product?
Once a target/prospect is interested, how are those the organization really
wants selected and booked?
Managing Customers:
Once the customer is in the portfolio, how is the relationship maintained,
controlled, and grown while collecting payment?
Customer Collections:
As customers default on obligations, what treatments should be deployed to
encourage payment and restore customers to non-delinquent status
© 2014 Fair Isaac Corporation. Confidential.
Key Concerns within the Credit Customer
Lifecycle Framework
Data
Data
External
Data
Internal
Data
Customer
Marketing
Customer
Origination
Customer
Management
Location & geographic
footprint
Target prospect/
customer?
Manage marketing
campaigns?
Tailor offer/message/
incentive?
Tier pricing?
Manage promotional
expense and effect?
Timing/ frequency of
campaigns?
Accept/reject?
Deter fraud?
Verify customer ID?
Anti-money laundering?
Affordability/suitability?
Tier pricing?
Initial line?
Loan-to-value?
Collateral value?
Cross-sell?
Upsell/downsell/offer
alternative?
Promote usage?
Obtain payments?
Manage exposure?
Collateral tracking?
Mitigate risk?
Deter fraud?
Marketing
communications?
Adjust pricing?
Service level?
Cross-sell?
Stress testing?
Reactions
© 2014 Fair Isaac Corporation. Confidential.
Obtain payments?
Allocate resource?
Channel & contact
strategy?
Treatment strategy?
Debt placement?
Debt sale?
Agency strategy?
Collector skills?
Legal/insolvent/ repo
accounts?
Workflow? Incentives?
Actions
Client Prospects
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Customer
Collections
Client Customers
Scores and Models and The Credit Lifecycle
CUSTOMER MARKETING &
ORIGINATIONS
CUSTOMER COLLECTIONS
FICO
Applications
CUSTOMER MANAGEMENT
FICO® Falcon® Fraud Manager
FICO® Fraud Resolution Manager
FICO® Insurance Fraud Manager
FICO® Application Fraud Manager
FICO® Retail Fraud Manager
FICO® Merchant Monitoring
FICO® Claims Fraud Manager
FICO® Origination Manager
FICO® LiquidCredit® Service
FICO® Customer Dialogue Manager
FICO® TRIAD® Customer Manager
FICO® Customer Dialogue Manager
FICO® Debt Manager™ solution
FICO® Risk Intervention Manager
FICO® PlacementsPlus® service
FICO® Network
FICO
Analytics
FRAUD MANAGEMENT
Consortium Fraud Models
Custom Fraud Models
Consumer and Small Business
Risk Models
Economic Impact Models
Behavior Scorecards
Transaction Analytics
Targeting Models
Time-to-Event Analytics
Collections Scores
OMNI-CHANNEL COMMUNICATIONS
FICO
Solution Stack
FICO® Customer Communication Services
FICO® Engagement Analyzer
FICO® Analytic Modeler
FICO® Model Builder
FICO® Decision Modeler
FICO® Blaze Advisor®
FICO® Optimization Modeler
FICO® Xpress Optimization Suite
FICO
Scores
FICO® Decision Management Platform
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MODEL MONITORING &
MANAGEMENT
TOOLS
FICO® Score
© 2014 Fair Isaac Corporation. Confidential.
FICO® Identity
Resolution Engine
FICO® Model Central™
Solution
The Risk Reward Trade Off
► Individuals
with higher risk profiles have fewer
options to obtain credit, are willing to pay higher
rates and fees as a result
► Individuals
with lower risk profiles can obtain credit
more easily, are choosy about the credit they seek,
and will expect lower fees and more benefits as a
result
► High
risk customers yield higher rewards—fees,
interest payments—right until they stop paying
altogether
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© 2014 Fair Isaac Corporation. Confidential.
Profitability Is Driven by Risk Appetite
► Inherent
borrower risk
► The
lender selects the risk level comfortable to their
corporate goals
► Then
Risk Happens
► Change
in economic circumstance
► Change in competitive structure
► Regulatory and legislative events
► Operational issues/risks/fraud
► Natural and Unnatural disasters
► Funding and pricing risks
► Technology risk
► Credit
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loss can dominate other profit factors
© 2014 Fair Isaac Corporation. Confidential.
The Interaction of Risk and Growth
Delinquency Rate = Delinquent Balances/Receivables
Loss Rate = Charged-Off Balances/Receivables
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© 2014 Fair Isaac Corporation. Confidential.
Scores and Models
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© 2014 Fair Isaac Corporation. Confidential.
Why Credit Risk Scoring?
► Statistical
► Convert
process
into a numerical score information from:
►
Credit applicants, application forms
► Existing account performance
► External sources like credit reports
► Credit
risk scores measure the likelihood/ probability
of repayment as agreed within a specific time period
Score-driven decisions provide:
 Consistency
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© 2014 Fair Isaac Corporation. Confidential.
and compliance
 Resource
prioritization and allocation
 Predictive
accuracy
All Scores Are Models. Not All Models Are Scores
► Models
may exist for a variety of purposes:
► Description
and segmentation
► Financial forecasting—portfolio models
► Investment models
► Pricing models
► Decision models—operational decision making
► Scores
are models which predict the likelihood of
a specific future behavior by customers
► Credit
risk scores predict the likelihood of payment
as agreed within a specified time period
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© 2014 Fair Isaac Corporation. Confidential.
Key Assumption: The Past Predicts the Future
Data
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© 2014 Fair Isaac Corporation. Confidential.
Model
Outcome
Scoring Development Is Approached Methodically
1.
2.
3.
4.
5.
6.
7.
8.
9.
Decide on decision context and behaviors being measured
Gather or Sample relevant data
Analyze data patterns
Build Model
Scale and validate model
Code for implementation/Deliver executable
Decide cut-offs, other operating considerations
Implement model
Track and monitor model performance
Once in production, monitoring will determine when cut-off changes or
model redevelopments are required
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© 2014 Fair Isaac Corporation. Confidential.
Scores Can Be Developed to Predict Binary or
Continuous Outcomes
Binary outcomes: Account is good or bad
► Odds stated as likelihood of good/bad
outcomes
►
Credit scores for originations evaluation
► Traditional behavior scores
Continuous outcomes: Range of results
►
Odds stated as probability of the outcome
at a specific score range
►
Probability that a transaction is fraudulent
within a certain tolerance
►
Probability of profitability within a certain
tolerance
© 2014 Fair Isaac Corporation. Confidential.
Scoring Susceptible to Legal, Regulatory and
Practical Limits
Data
► Data
may not exist, or may be unusable by law - credit bureau data as primary example
► Reciprocity concepts and permissible purpose may limit access to data
► Certain types of data cannot be used at all (US—gender), or can be used only in limited ways
(US—age, as a splitter but not as a scored characteristic)
► Privacy restrictions may prevent reporting of positive data or use of data or scores in specific
contexts (France: no credit bureau scoring; many countries: negative file only)
Protected Classes
► By
regulation, must not discriminate against individuals above or below
certain age, income or gender lines
Adverse Action
► Must
be able to indicate to an individual where points
were lost if negative action will be taken in response
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© 2014 Fair Isaac Corporation. Confidential.
Adverse Action Codes and Customer Notification
► Adverse Action
occurs when something negative is done to a customer or account
► Declining
an application
► Changing a term or condition to be more expensive or restrictive
► Decreasing a credit line or giving lesser amounts than the customer requested
► When
a score is used as part of the basis for a decision leading to an adverse
action, the US requires notification of the customer
► Generic
notification is permitted (i.e. indication that a score was used, credit report
obtained, and reasons available on request) as a first notice
► If the customer asks for specific reasons behind an adverse action where score was used,
the lender must:
►
Provide explanation of the four top reasons that the customer lost points
► Explain how customer can dispute wrong information
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© 2014 Fair Isaac Corporation. Confidential.
Why Multiple Model Types?
Different lifecycle segments face different issues with different timing implications,
requiring unique predictions
Lifecycle Segment
Issues
Pre-screening
•
(US)
•
•
•
•
Originations
•
© 2014 Fair Isaac Corporation. Confidential.
Recognition of lists or individuals’ records which match criteria
associated with desired account performance
Definition of product offer that will be attractive to desired prospect
Identifying and booking prospects who are good performance risks
Adjusting pricing to match risk
Setting initial credit line or LTV /DSR according to prospect ability to
make payment and/or collateral value (affordability component)
Defining terms and conditions for new accounts to mitigate potential
customer risk
Why Multiple Model Types?
Different lifecycle segments face different issues with different timing implications,
requiring unique predictions
Lifecycle Segment
Non-delinquent
Accounts
Early-Stage
Delinquency
Issues
•
•
•
•
•
•
•
•
•
© 2014 Fair Isaac Corporation. Confidential.
Recognition of potential risk to take mitigating actions
Recognition of potential cross-sell or up-sell opportunities
Spotting potential bankruptcy risks
Appropriateness of exposure management programs (affordability)
Identifying potential skips/ First Party Fraud (no contact accounts)
Distinguishing potential self-cures from potential accelerating
delinquency (roll rate potential)
Identifying First Party Fraud / skips / fraud investigation queue
Addressing bankruptcy risk
Adapting treatment strategy and resource allocation
Why Multiple Model Types?
Different lifecycle segments face different issues with different timing implications,
requiring unique predictions
Lifecycle Segment
Late-Stage
Delinquency
Recovery
Issues
•
•
•
•
•
•
•
© 2014 Fair Isaac Corporation. Confidential.
Identifying payment potential for continued in-house activity
Forecasting potential write-offs
Identifying accounts for assignment/ repo/ legal action
Allocating collections resources & adapting treatment strategy
Identifying and optimizing agency assignments for initial, secondary
and tertiary agency /legal assignment
Working skips, collateral skips (replevin)
Identifying change in customer circumstances that can result in
recovery balances
Scoring Progression
Pre-Screening and Acquisitions
Application and Response
Processing
FICO® Score
Specialty bureau or
custom scores
Other criteria
Pre-Screen
FICO® Score
Specialty bureau or
custom scores
Response models
Other criteria
Origination model
FICO® Score
Policy rules
FICO® Customer Dialogue Manager
Campaign Management System
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FICO® Origination Manager
FICO® LiquidCredit® Service
Primary Decision: Reduce Loss
Specialty Risk Assessment
Secondary Decision: Risk-Related
Revolving Credit: Additional Precision
© 2014 Fair Isaac Corporation. Confidential.
New data: same
criteria for evaluating
pre-screen responses
Fresh data: scores,
policy rules used to
evaluate applications
Scoring Progression
Existing Accounts
Account Status
On-time
Delinquent
Late-stage Collections
Recovery
Behavior score
Custom collection score
FICO® Score
Specialty bureau or
custom scores
Bureau-based
recovery score
Custom recovery score
Transaction score
FICO® TRIAD™ Customer Manager
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FICO® Debt Manager™ solution
Primary Decision: Reduce Loss
Specialty Risk Assessment
Secondary Decision: Risk-Related
Revolving Credit: Additional Precision
© 2014 Fair Isaac Corporation. Confidential.
FICO® Score
A Special Example
► The
“FICO® Score” is a summary of the information on a consumer’s credit file
► Single,
3-digit number between 300 and 850
► Rank-orders consumers according to risk
► Includes 4 explanations of how score could have been higher (adverse action reasons)
► Higher
scores equate to lower future risk of default
► FICO®
Scores are available in the US, Canada, South Africa
► Global
FICO® Score offered in international markets
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© 2014 Fair Isaac Corporation. Confidential.
Partial Sample Credit Bureau Scorecard
Characteristic
Number of bank card trade lines
Attribute
► Answer
Attributes
0
1
2
3
4 or more
0–1
2
3–4
5–7
8+
Below 12
12 to 23
bankcards
24 to 47
90+48day
or more
delinquencies
0 to 5
6 to 11
12 to 17
18 to 23
24 or more
No public Record
0 to 5
6 to 11
12 to 23
24 or more
Points
15
22
30
40
30
65
55
50
40
30
12
35
60
75
20
25
30
38
45
75
10
15
25
45
given by credit bureau report
► Examples:
Number
of trade lines with balance >0 Characteristic
Score
► Number of bankcards = 3
► A question about the credit bureau report
► The sum of points awarded for all
► Number of 90+ days delinquencies = 2
► Examples:
characteristics within the model,
and
►
Number
of
Number ofequated
months
in
tofile
a defined likelihood of a specific
Points:
► Number of
behavior
► Value awarded corresponding to the
Example:
attribute
supplied
for a characteristic
Number►of
months
since most
recent bankcard
► 30+15+20+75= 140
opening
► Examples:
► Below 12 = 12
► 12 to 23 = 35
► since
24 to most
47 = 60
months
recent
► 48 or more = 45
Number of
public record
© 2014 Fair Isaac Corporation. Confidential.
derogatory
Partial Example Credit Bureau Scorecard
Characteristic
Attributes
Points
No public record
0–5
6–11
12–23
24+
75
10
15
25
45
No revolving trades
0
1–99
100–499
500–749
750–999
1000 or more
30
55
65
50
40
25
15
Below 12
12–23
24–47
48 or more
12
35
60
75
Number of inquiries in last 6 months
(Pursuit of new credit)
0
1
2
3
4+
70
60
45
25
20
Number of bankcard trade lines
(Types of credit experience)
0
1
2
3
4+
15
22
30
40
30
Number of months since the most recent
derogatory public record
(Previous credit performance)
Average balance on revolving trades
(Current level of indebtedness)
Number of months in file
(Amount of time credit has been in use)
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© 2014 Fair Isaac Corporation. Confidential.
No Public Record
$600 Average balance
60 months on credit
bureau file
1 inquiry in last six
months
2 bankcard trade lines
Score 280 points
Scores Can Be Calibrated to Performance Odds
280
260
240
220
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© 2014 Fair Isaac Corporation. Confidential.
Odds Or Bad Rate Can Be Used to Decide Cut-offs
Branch Scorecard
Final Score
Approval %
Bad %
100
97.8%
22.0%
200
90.6%
18.0%
300
79.5%
12.5%
400
65.4%
7.6%
500
49.8%
4.0%
600
32.1%
1.4%
700+
46.5%
0.5%
Cut-offs reflect risk appetite
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© 2014 Fair Isaac Corporation. Confidential.
Making Scores Actionable
► Scores
are made actionable when they are implemented in combination with
other criteria and decision rules through a manual or automated process
► Decision
► Making
Management systems can incorporate analytics including scores
a specific scoring model actionable should include review of:
► Data
accessibility and stability
► Palatability of adverse action explanations
► Ease of incorporating model into production systems
► Validation/simulation to assess operating volume concerns
► Credit policy concepts that will be applied
► Training and education needed for operating staff
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© 2014 Fair Isaac Corporation. Confidential.
Combining Scores: Behavior Score and FICO® Score
Bad Rates and Example Strategic Use
Bad rate = 28% - HIGH risk – consider active mitigation
1% < Bad rate <20% MEDIUM risk – no action or monitor
Bad rate <1% - LOW risk – consider positive action
FICO® Score (provided by TransUnion)
BEH Score
LOW– 600
500–660
661–680
681–700
701–720
721–740
741–760
761+
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© 2014 Fair Isaac Corporation. Confidential.
601–680
681–700
701–720
721–740
741–760
761+
Decision Trees Enable Lenders to Make Scores
Actionable and Coordinate Use of Multiple Scores
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© 2014 Fair Isaac Corporation. Confidential.
FICO® Model Builder for Decision Trees
Segment Detail
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© 2014 Fair Isaac Corporation. Confidential.
The Score-Odds Relationship Can Change
Through Time, Making Validation Necessary
► Because
the development population may become less and less like the
through-the-door population as time passes, scorecard validation should be
regularly performed
► Odds
to score can change for many reasons:
► Competitive
changes attract a different customer type
► Economic conditions create behavior extremes or shift concepts of “normal” behavior
► Sudden events can alter behavior—Hurricane Katrina, Boxing Day Tsunami, etc.
► New
scorecards should be validated before implementation in production
► Monitoring
and tracking reports should be run annually in smaller operations,
quarterly in larger ones
► Regulatory
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examinations in the US will absolutely require evidence of validation
© 2014 Fair Isaac Corporation. Confidential.
Scorecard Monitoring and Tracking
► In
US, use of scoring system requires performance of standardized monitoring
and tracking
► FICO
has long recommended creation and use of 8 standard reports:
1. Population Stability Report
2. Characteristic Analysis Report
3. Final Score Distribution
4. Override Tracking Report
5. Detail Delinquency Report—Maximum Delinquency (incidence and balances)
6. Detail Delinquency Report—Current Delinquency (incidence and balances)
7. Vintage Analysis Table and Graph
8. Chronology Log
► Details
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available in Scorecard Management Guide
© 2014 Fair Isaac Corporation. Confidential.
Model Governance, Affordability and Stress Testing
Challenge Lenders
► Globally,
regulators have indicated they will pay more attention to model governance
►
The design, type, use and results of scoring models is coming under greater scrutiny
► Performance tracking, scorecard performance monitoring increasingly required
► Results of validations being given greater review
► Model governance an increasingly important compliance objective
► A wide
range of countries have called for review of consumer affordability before the
issuance of new credit or further extension of existing credit
►
Methods for assessing affordability include income estimation models, debt to income calculations/debt
burden calculations, and asset or relationship value measurement
► Methods for assessing suitability include calculating whether new payments can be addressed along
with existing obligations
► Stress
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Testing includes mandated parameters; quantitative and qualitative aspects
© 2014 Fair Isaac Corporation. Confidential.
Scores Influence Decisions Across the
Customer Lifecycle
External
Data
Internal
Data
Customer
Marketing
Reactions
Customer
Origination
Client Prospects
Customer
Management
Client Customers
Customer
Collections
Actions
Key Concept: Full impact of credit decisions can only be understood when examined in
light of their impact on the success of the next stage of the credit life cycle
Customer Marketing:
Originations/Underwriting:
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Response models, profitability models
Credit risk models, bankruptcy models, profitability models
Managing Customers:
Behavior risk models, profitability models, attrition models, response models
Customer Collections:
Payment projection models, roll-rate models
© 2014 Fair Isaac Corporation. Confidential.
Thank You!
Janice Horan
[email protected]
+1-856-562-3776
© 2014 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.
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Janice Horan
[email protected]
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© 2014 Fair Isaac Corporation. Confidential.