Credit Underwriting and Default Management in Today’s Private
Student Loan EnvironmentPresented by
Michial ThompsonManaging Director, Credit Risk ManagementFirst 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
Historical First MarbleheadUnderwriting Loans 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 scores Primarily cosigner FICO • FICO of both student and cosigner used, and much higher values required. Many other credit attributes reviewed
• Custom scorecards
Collections Due 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
Data Very 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: Lend to Quality Schools
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Default Rates by School Type
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
FMC Private Student Loan Scorecard: Updated Score Further Separates Risk
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Percent of Applications
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Cosigner FICOFMD Score Version 1FMD Score Version 2 (Extant)
To Remove 75% of Defaults• Eliminate worst 55% using FICO• Eliminate worst 35% using FMD1• Eliminate worst 26% using FMD2
• Worst 15% assigned by FMD Score Version 2 (Extant) captures 58% of defaults• Worst 15% assigned by FMD Score Version 1 captures 50% of defaults• Worst 15% assigned by FICO Score captures 35% of defaults
Aggressive Agency Management Approach
Define StrategyDefine Strategy
Develop NetworkDevelop Network
ManageManage
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