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© 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. Fundamentals Scoring and the Credit Lifecycle Janice Horan Senior Director, Fair Isaac Advisors FICO
Agenda © 2014 Fair Isaac Corporation. Confidential. ► Credit and the Credit Lifecycle ► The Role of Risk Appetite in the Profit and Loss Dynamic ► Scoring and the Credit Lifecycle 2
© 2014 Fair Isaac Corporation. Confidential. ► 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 Credit Defined 3
© 2014 Fair Isaac Corporation. Confidential. The Credit Customer Life Cycle Customer Marketing:Who is the target customer, and how is interest created in the product? Originations/Underwriting: 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 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 Customer Origination Customer Management Customer Collections Client ProspectsClient Customers ActionsReactions External Data Internal Data 4
© 2014 Fair Isaac Corporation. Confidential. Key Concerns within the Credit Customer Lifecycle Framework 5 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? Data Client ProspectsClient Customers ActionsReactions Customer Marketing Customer Origination Customer Management Customer Collections External Data Internal Data Obtain payments? Allocate resource? Channel & contact strategy? Treatment strategy? Debt placement? Debt sale? Agency strategy? Collector skills? Legal/insolvent/ repo accounts? Workflow? Incentives?
© 2014 Fair Isaac Corporation. Confidential. Scores and Models and The Credit Lifecycle FICO Applications FRAUD MANAGEMENT CUSTOMER MARKETING & ORIGINATIONS CUSTOMER MANAGEMENTCUSTOMER COLLECTIONS 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 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 FICO Solution Stack OMNI-CHANNEL COMMUNICATIONS FICO ® Customer Communication ServicesFICO ® Engagement Analyzer TOOLS MODEL MONITORING & MANAGEMENT FICO ® Analytic Modeler FICO ® Model Builder FICO ® Decision Modeler FICO ® Blaze Advisor ® FICO ® Optimization Modeler FICO ® Xpress Optimization Suite FICO ® Identity Resolution Engine FICO ® Model Central™ Solution FICO ® Decision Management Platform FICO Scores FICO ® Score 6
© 2014 Fair Isaac Corporation. Confidential. ► 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 The Risk Reward Trade Off 7
© 2014 Fair Isaac Corporation. Confidential. ► 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 loss can dominate other profit factors Profitability Is Driven by Risk Appetite 8
© 2014 Fair Isaac Corporation. Confidential. The Interaction of Risk and Growth Delinquency Rate = Delinquent Balances/Receivables Loss Rate = Charged-Off Balances/Receivables 9
© 2014 Fair Isaac Corporation. Confidential. Scores and Models 10
© 2014 Fair Isaac Corporation. Confidential. Score-driven decisions provide: Consistency and compliance Resource prioritization and allocation Predictive accuracy Why Credit Risk Scoring? 11 ► Statistical process ► Convert 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
© 2014 Fair Isaac Corporation. Confidential. ► 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 All Scores Are Models. Not All Models Are Scores 12
© 2014 Fair Isaac Corporation. Confidential. Data Model Outcome Key Assumption: The Past Predicts the Future 13
© 2014 Fair Isaac Corporation. Confidential. 1.Decide on decision context and behaviors being measured 2.Gather or Sample relevant data 3.Analyze data patterns 4.Build Model 5.Scale and validate model 6.Code for implementation/Deliver executable 7.Decide cut-offs, other operating considerations 8.Implement model 9.Track and monitor model performance Scoring Development Is Approached Methodically 14 Once in production, monitoring will determine when cut-off changes or model redevelopments are required
© 2014 Fair Isaac Corporation. Confidential. Binary outcomes: Account is good or bad ► Odds stated as likelihood of good/bad outcomes ► Credit scores for originations evaluation ► Traditional behavior scores Scores Can Be Developed to Predict Binary or Continuous Outcomes 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. 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 Scoring Susceptible to Legal, Regulatory and Practical Limits 16
© 2014 Fair Isaac Corporation. Confidential. ► 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 Adverse Action Codes and Customer Notification 17
© 2014 Fair Isaac Corporation. Confidential. Why Multiple Model Types? Lifecycle Segment Issues Pre-screening (US) 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 Originations 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 Different lifecycle segments face different issues with different timing implications, requiring unique predictions
© 2014 Fair Isaac Corporation. Confidential. Why Multiple Model Types? Lifecycle Segment Issues Non-delinquent Accounts 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) Early-Stage Delinquency 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 Different lifecycle segments face different issues with different timing implications, requiring unique predictions
© 2014 Fair Isaac Corporation. Confidential. Why Multiple Model Types? Lifecycle Segment Issues Late-Stage Delinquency 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 Recovery 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 Different lifecycle segments face different issues with different timing implications, requiring unique predictions
© 2014 Fair Isaac Corporation. Confidential. FICO ® Origination Manager FICO ® LiquidCredit ® Service FICO ® Customer Dialogue Manager Campaign Management System Application and Response Processing Pre-Screen Pre-Screening and Acquisitions Scoring Progression 21 Specialty bureau or custom scores FICO ® Score Response models Other criteria Specialty bureau or custom scores FICO ® Score New data: same criteria for evaluating pre-screen responses Origination model FICO ® Score Policy rules Fresh data: scores, policy rules used to evaluate applications Other criteria Revolving Credit: Additional Precision Specialty Risk Assessment Secondary Decision: Risk-Related Primary Decision: Reduce Loss
© 2014 Fair Isaac Corporation. Confidential. Existing Accounts Scoring Progression 22 RecoveryLate-stage CollectionsDelinquentOn-time Bureau-based recovery score Custom collection score Behavior score Transaction score Account Status Specialty bureau or custom scores FICO ® Score Custom recovery score Revolving Credit: Additional Precision Specialty Risk Assessment Secondary Decision: Risk-Related Primary Decision: Reduce Loss FICO® TRIAD™ Customer Manager FICO® Debt Manager™ solution
© 2014 Fair Isaac Corporation. Confidential. 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 FICO ® Score 23
© 2014 Fair Isaac Corporation. Confidential. CharacteristicAttributesPoints Number of bank card trade lines or more30 Number of trade lines with balance >0 0– –450 5– Number of months in file Below to to or more75 Number of months since most recent bankcard opening 0 to to to to or more45 Number of months since most recent derogatory public record No public Record75 0 to to to or more45 Partial Sample Credit Bureau Scorecard Characteristic ► A question about the credit bureau report ► Examples: ► Number of bankcards ► Number of 90+ day delinquencies Attribute ► Answer given by credit bureau report ► Examples: ► Number of bankcards = 3 ► Number of 90+ days delinquencies = 2 Points: ► Value awarded corresponding to the attribute supplied for a characteristic ► Examples: ► Below 12 = 12 ► 12 to 23 = 35 ► 24 to 47 = 60 ► 48 or more = 45 Score ► The sum of points awarded for all characteristics within the model, and equated to a defined likelihood of a specific behavior ► Example: ► = 140
© 2014 Fair Isaac Corporation. Confidential. Partial Example Credit Bureau Scorecard 25 CharacteristicAttributesPoints Number of months since the most recent derogatory public record (Previous credit performance) No public record 0–5 6–11 12– Average balance on revolving trades (Current level of indebtedness) No revolving trades 0 1–99 100– – – or more Number of months in file (Amount of time credit has been in use) Below 12 12–23 24–47 48 or more Number of inquiries in last 6 months (Pursuit of new credit) Number of bankcard trade lines (Types of credit experience) Score 280 points No Public Record $600 Average balance 60 months on credit bureau file 1 inquiry in last six months 2 bankcard trade lines
© 2014 Fair Isaac Corporation. Confidential. Scores Can Be Calibrated to Performance Odds
© 2014 Fair Isaac Corporation. Confidential. Branch Scorecard Odds Or Bad Rate Can Be Used to Decide Cut-offs 27 Cut-offs reflect risk appetite Final ScoreApproval %Bad % %22.0% %18.0% %12.5% %7.6% %4.0% %1.4% %0.5%
© 2014 Fair Isaac Corporation. Confidential. ► Scores are made actionable when they are implemented in combination with other criteria and decision rules through a manual or automated process ► Decision Management systems can incorporate analytics including scores ► Making 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 Making Scores Actionable 28
© 2014 Fair Isaac Corporation. Confidential. Bad Rates and Example Strategic Use Combining Scores: Behavior Score and FICO ® Score 29 FICO ® Score (provided by TransUnion) BEH ScoreLOW– –680681–700701–720721–740741– – – – – – – 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
© 2014 Fair Isaac Corporation. Confidential. Decision Trees Enable Lenders to Make Scores Actionable and Coordinate Use of Multiple Scores 30
© 2014 Fair Isaac Corporation. Confidential. Segment Detail FICO ® Model Builder for Decision Trees 31
© 2014 Fair Isaac Corporation. Confidential. ► 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 examinations in the US will absolutely require evidence of validation The Score-Odds Relationship Can Change Through Time, Making Validation Necessary 32
© 2014 Fair Isaac Corporation. Confidential. ► 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 available in Scorecard Management Guide Scorecard Monitoring and Tracking 33
© 2014 Fair Isaac Corporation. Confidential. ► 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 Testing includes mandated parameters; quantitative and qualitative aspects Model Governance, Affordability and Stress Testing Challenge Lenders 34
© 2014 Fair Isaac Corporation. Confidential. Scores Influence Decisions Across the Customer Lifecycle 35 Customer Marketing:Response models, profitability models Originations/Underwriting: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 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 Customer Origination Customer Management Customer Collections Client ProspectsClient Customers ActionsReactions External Data Internal Data
© 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. Janice Horan Thank You!
© 2014 Fair Isaac Corporation. Confidential. Learn More at FICO World Related Sessions ► Credit Boot Camp: Fundamentals - Origination ► Credit Boot Camp: Fundamentals - Customer Management ► Credit Boot Camp: Fundamentals - Collections ► Credit Boot Camp: Game On! How to Make Banking Training Fun Products in Solution Center ► FICO ® Origination Manager ► FICO ® TRIAD ® Customer Manager ► FICO ® Debt Manager™ solution ► Consumer & Small Business Risk models ► P&L Insight Service Experts at FICO World ► Daniel Melo ► Sarah Murphy ► Liz Ruddick ► Alecia Jacobs ► Mary Dupont ► Bruce Curry ► Miguel Cabezas ► Alissa McCarthy White Papers Online ► Scoring your customers: how often is often enough? ► Managing Credit Line Increase Strategies with Opt-in Requirements Blogs ►
© 2014 Fair Isaac Corporation. Confidential. Please rate this session online Janice Horan 38
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