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Credit Underwriting and Default Management in Today’s Private Student Loan Environment

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Contact Info:Michial ThompsonManaging Director, Credit Risk ManagementFirst Marblehead617-638-2135mthompson@fmd.com

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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

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PSL Credit, Data and AnalyticsHistorical First Marblehead

Underwriting 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 scorecardsCollections 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

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Credit Policy

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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

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Credit Policy: Skeletons in the Closet

Student FICO on Cosigned (>750) Loans

Cosigner vs. CWS

• 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.

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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

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Credit Policy: Lend to Quality Schools

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2 year school 4 year school K-12 school For profit school

Default Rates by School Type

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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

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FMC Private Student Loan Scorecard: Updated Score Further Separates Risk

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5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100%

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

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Agency Management

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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

Credit Underwriting and Default Management

Data & Analytics

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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

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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

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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

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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

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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

Credit Underwriting and Default Management

Student Loan Idiosyncrasies

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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

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Student Loan Idiosyncrasies: Example Deferment

Credit Underwriting and Default Management

Results: A Case Study

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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.