Presentation on theme: "Credit Underwriting and Default Management in Today’s Private Student Loan Environment Presented by Michial Thompson Managing Director, Credit Risk Management."— Presentation transcript:
Credit Underwriting and Default Management in Today’s Private Student Loan Environment Presented by Michial Thompson Managing Director, Credit Risk Management First Marblehead
How to Avoid Student Loan Defaults To determine how to prevent defaults, let’s look at what the main drivers of default are: Credit Policy: Lenders make loans they expect to be paid back Collection Agency Management: Ensure maximum performance when DQ loans are placed for collections Data & Analytics: Performance projections, reporting and collections placement streams driven by data analytics Student Loan Idiosyncrasies: Deferment, youth, cosigners
PSL Credit, Data and Analytics HistoricalFirst Marblehead UnderwritingLoans to (almost) anyone at (almost) any school with (almost) any cosigner. Student and cosigner both evaluated, and much more rigorously Quality of school considered Credit scoresPrimarily cosigner FICOFICO of both student and cosigner used, and much higher values required. Many other credit attributes reviewed Custom scorecards CollectionsDue diligence “check the box” style, modeled after federal program. Agencies compensated for carrying out tasks, not for performance. Driven by data and analytics Custom treatment streams driven by credit risk Similar to other asset classes—credit cards Rigorous (micro)management of agencies and performance Student loan specific Not much customization of credit policy or collections Especially complicated asset class to understand: Deferment, forbearance, young borrowers. Large unsecured personal loan. Products custom designed with credit and portfolio management in mind Data, analytics, reporting and collections are custom designed to deal with student loan idiosyncrasies Analytical techniques specifically tailored for PSL’s DataVery few have data needed to understand credit and performance $17B in originations and performance data over 20+ years Comprehensive and frequent credit bureau refreshes Robust data set of loans across multiple lenders, marketers, school lists, economic periods and credit policies
Credit Policy Appropriate Assessment of Risk at Time of Application Beyond just FICO More granular credit bureau attributes Evaluate both student and cosigner Over-borrowing/loan amounts School types/programs Ability to repay
Credit Policy: Skeletons in the Closet Cosigner vs. CWS Student FICO on Cosigned (>750) Loans All of these are cosigned loans with cosigner FICO > 750. The bars show what happens to defaults when we further segment these by student FICO. The student (skeleton in the closet) weighs heavily on the performance of the loan. Overall cosigned loans with cosigner FICO > 750 default at a higher rate than non-cosigned loans with student FICO > 750.
Credit Policy: Lend to Quality Schools Dropouts are twice as likely to default as graduates School, school type, and program of study are strong predictors of graduation rates Clearly graduates are more likely to get a higher paying job that will allow them to pay back the loan
Credit Policy: Control Over-Borrowing School certification greatly reduces over-borrowing compared to DTC Loan amount requested should be considered in credit decision Capacity metrics (such as DTI) further assess ability to repay and prevent excessive loan amounts
Aggressive Agency Management Approach Define Strategy Develop Network Manage Define the agency type (experience, client base, management, etc) Performance drives future volume placements Incentive plan must be meaningful to agency to align performance Optimizing number of agencies per segment to foster competition Continuous refresh of agencies based on results Robust bullpen for quick change-out for performance or client need Goals and volume forecasts clearly communicated Monitoring in place for outcomes; activity monitoring progressing Mutual transparency into operations Deep dives on root causes of performance gaps Volume shift algorithms for Recovery agencies Agencies now know they are being watched
Data and Analytics NOT one-size-fits-all Collectability scorecard Origination, monthly performance, refreshed credit bureau data Probability of a delinquent loan curing Strategies driven by data When to place a file vs. leaving it with servicer Which collection agency to place with How long to leave loan at a given collection agency Which strategies (FB, MGRS, etc) available per customer Test-and-learn approach
Data and Analytics Agency level Daily, weekly, monthly Performance by batch, by risk segment, by placement stream/strategy Transparent view of competition Agent level Daily, weekly, monthly Keep track of what happens to top performers
Data and Analytics Data Dialer data Daily details of every call Skip-tracing Refreshed credit bureau data Phone, cell phone data USPS (and others) data to track relocations
Data and Analytics: Example Agent level reporting Prevent best performer migration Plans for lower performers Resulted in 3 better supervisors transferred in They know we are watching
Data and Analytics: Example Test-and-Learn Mailing Strategy Test Timing of communications strategy Borrower vs. Cosigner Delivery / Channel options Agency integration / talking points No Cosigner With Cosigner
Student Loan Idiosyncrasies Deferment does not build a habit of making payments Credit policy should encourage cash-flowing loans Early Awareness Program Reach out to both student and cosigner before repayment Email, phone, mail package Most loans need a cosigner—utilize this early and often Contact cosigner at any sign of trouble Include cosigner in all communications Require cosigner participation in FB or similar decisions
Case Study: FMD reduced delinquencies and defaults for one major bank’s PSL portfolio by 50% After taking over, delinquencies immediately improved. Within 6 months, annualized monthly charge-off rates were cut in half.