Credit Underwriting and Default
Management in Today’s Private
Student Loan Environment
Presented by
Michial Thompson
Managing Director, Credit Risk Management
First Marblehead

To determine how to prevent defaults, let’s look at what the main drivers of default are:
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Credit Policy : Lenders make loans they expect to be paid back
Collection Agency Management : Ensure maximum performance when DQ loans are placed for collections
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Data & Analytics : Performance projections, reporting and collections placement streams driven by data analytics
Student Loan Idiosyncrasies : Deferment, youth, cosigners

Underwriting
Credit scores
Collections
Student loan specific
Data
Historical
Loans to (almost) anyone at
(almost) any school with
(almost) any cosigner.
Primarily cosigner FICO
Due diligence “check the box” style, modeled after federal program. Agencies compensated for carrying out tasks, not for performance.
Not much customization of credit policy or collections
Especially complicated asset class to understand:
Deferment, forbearance, young borrowers. Large unsecured personal loan.
Very few have data needed to understand credit and performance
First Marblehead
• Student and cosigner both evaluated, and much more rigorously
• Quality of school considered
• FICO of both student and cosigner used, and much higher values required. Many other credit attributes reviewed
• Custom scorecards
• 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
• 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
• $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

Appropriate Assessment of Risk at Time of Application
 Beyond just FICO
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More granular credit bureau attributes
Evaluate both student and cosigner
Over-borrowing/loan amounts
School types/programs
Ability to repay

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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.
Cosigner vs. CWS
Student FICO on Cosigned (>750) Loans

•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

Default Rates by School Type
200%
100%
0%
2 year school 4 year school K-12 school For profit school

• 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

100%
90%
To Remove 75% of Defaults
• Eliminate worst 55% using FICO
• Eliminate worst 35% using FMD1
• Eliminate worst 26% using FMD2
80%
70%
60%
50%
40%
30%
• 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
20%
10%
Cosigner FICO
FMD Score Version 1
FMD Score Version 2 (Extant)
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%
Percent of Applications

Define Strategy
 Define the agency type (experience, client base, management, etc)
 Performance drives future volume placements
 Incentive plan must be meaningful to agency to align performance
Develop Network
Manage
 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

NOT one-size-fits-all
 Collectability scorecard
• Origination, monthly performance, refreshed credit bureau data
• Probability of a delinquent loan curing
 Strategies driven by data
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When to place a file vs. leaving it with servicer
Which collection agency to place with
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How long to leave loan at a given collection agency
Which strategies (FB, MGRS, etc) available per customer
Test-and-learn approach

 Agency level
• Daily, weekly, monthly
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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
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Dialer data
• Daily details of every call
Skip-tracing
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Refreshed credit bureau data
Phone, cell phone data
USPS (and others) data to track relocations

• Prevent best performer migration
• Plans for lower performers
• Resulted in 3 better supervisors transferred in
• They know we are watching

Mailing Strategy Test
• Timing of communications strategy
• Borrower vs.
Cosigner
• Delivery / Channel options
• Agency integration
/ talking points
No Cosigner With Cosigner

Deferment does not build a habit of making payments
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Credit policy should encourage cash-flowing loans
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Early Awareness Program
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Reach out to both student and cosigner before repayment
Email, phone, mail package
Most loans need a cosigner—utilize this early and often
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Contact cosigner at any sign of trouble
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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.