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Scoring and the Credit Lifecycle

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Presentation on theme: "Scoring and the Credit Lifecycle"— Presentation transcript:

1 Scoring and the Credit Lifecycle
Fundamentals Scoring and the Credit Lifecycle Janice Horan Senior Director, Fair Isaac Advisors FICO

2 Credit and the Credit Lifecycle
The Role of Risk Appetite in the Profit and Loss Dynamic Scoring and the Credit Lifecycle

3 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>

4 The Credit Customer Life Cycle
External Data Customer Marketing Customer Origination Customer Management Customer Collections Internal Data Reactions Client Prospects Client Customers 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: 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

5 Key Concerns within the Credit Customer Lifecycle Framework
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? Initial line? Loan-to-value? Collateral value? Cross-sell? Upsell/downsell/offer alternative? Promote usage? Obtain payments? Manage exposure? Collateral tracking? Mitigate risk? Marketing communications? Adjust pricing? Service level? Stress testing? Data Client Prospects Client Customers Actions Reactions Customer Marketing Customer Origination Customer Management Customer Collections External Data Internal Allocate resource? Channel & contact strategy? Treatment strategy? Debt placement? Debt sale? Agency strategy? Collector skills? Legal/insolvent/ repo accounts? Workflow? Incentives?

6 Scores and Models and The Credit Lifecycle
FICO Applications Fraud Management Customer Marketing & Originations Customer MANAGEMENT CUSTOMER 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® 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 Services FICO® 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

7 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

8 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 loss can dominate other profit factors

9 The Interaction of Risk and Growth
Delinquency Rate = Delinquent Balances/Receivables Loss Rate = Charged-Off Balances/Receivables

10 Scores and Models Page 10

11 Why Credit Risk Scoring?
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 Score-driven decisions provide: Consistency and compliance Resource prioritization and allocation Predictive accuracy

12 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

13 Key Assumption: The Past Predicts the Future
Data Model Outcome

14 Scoring Development Is Approached Methodically
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

15 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

16 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

17 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

18 Why Multiple Model Types?
Different lifecycle segments face different issues with different timing implications, requiring unique predictions 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

19 Why Multiple Model Types?
Different lifecycle segments face different issues with different timing implications, requiring unique predictions 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

20 Why Multiple Model Types?
Different lifecycle segments face different issues with different timing implications, requiring unique predictions 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

21 Scoring Progression Pre-Screening and Acquisitions Pre-Screen
Application and Response Processing FICO® Score FICO® Score New data: same criteria for evaluating pre-screen responses Specialty bureau or custom scores Specialty bureau or custom scores Response models Other criteria Other criteria Origination model Fresh data: scores, policy rules used to evaluate applications FICO® Score Policy rules FICO® Customer Dialogue Manager Campaign Management System FICO® Origination Manager FICO® LiquidCredit® Service Revolving Credit: Additional Precision Specialty Risk Assessment Secondary Decision: Risk-Related Primary Decision: Reduce Loss

22 Scoring Progression Existing Accounts Account Status On-time
Delinquent Late-stage Collections Recovery Behavior score Bureau-based recovery score Custom collection score Custom recovery score FICO® Score Specialty bureau or custom scores Transaction score FICO® TRIAD™ Customer Manager FICO® Debt Manager™ solution Revolving Credit: Additional Precision Specialty Risk Assessment Secondary Decision: Risk-Related Primary Decision: Reduce Loss

23 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

24 Partial Sample Credit Bureau Scorecard
Characteristic Attributes Points Number of bank card trade lines 15 1 22 2 30 3 40 4 or more Number of trade lines with balance >0 0–1 65 55 3–4 50 5–7 8+ Number of months in file Below 12 12 12 to 23 35 24 to 47 60 48 or more 75 Number of months since most recent bankcard opening 0 to 5 20 6 to 11 25 12 to 17 18 to 23 38 24 or more 45 Number of months since most recent derogatory public record No public Record 10 Attribute Answer given by credit bureau report Examples: Number of bankcards = 3 Number of 90+ days delinquencies = 2 Score The sum of points awarded for all characteristics within the model, and equated to a defined likelihood of a specific behavior Example: = 140 Characteristic A question about the credit bureau report Examples: Number of bankcards Number of 90+ day delinquencies 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

25 Partial Example Credit Bureau Scorecard
Characteristic Attributes Points Number of months since the most recent derogatory public record (Previous credit performance) No public record 0–5 6–11 12–23 24+ 75 10 15 25 45 Average balance on revolving trades (Current level of indebtedness) No revolving trades 0 1–99 100– – –999 1000 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) 0 1 Number of bankcard trade lines (Types of credit experience) 15 22 30 40 30 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

26 Scores Can Be Calibrated to Performance Odds
280 260 240 220

27 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

28 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 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

29 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– 600 601–680 681–700 701–720 721–740 741–760 761+ 500–660 661–680

30 Decision Trees Enable Lenders to Make Scores Actionable and Coordinate Use of Multiple Scores

31 FICO® Model Builder for Decision Trees
Segment Detail

32 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 examinations in the US will absolutely require evidence of validation

33 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: Population Stability Report Characteristic Analysis Report Final Score Distribution Override Tracking Report Detail Delinquency Report—Maximum Delinquency (incidence and balances) Detail Delinquency Report—Current Delinquency (incidence and balances) Vintage Analysis Table and Graph Chronology Log Details available in Scorecard Management Guide

34 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 Testing includes mandated parameters; quantitative and qualitative aspects

35 Scores Influence Decisions Across the Customer Lifecycle
External Data Customer Marketing Customer Origination Customer Management Customer Collections Internal Data Reactions Client Prospects Client Customers 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: 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

36 Thank You! Janice Horan

37 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

38 Please rate this session online
Janice Horan

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