vision 2014 driving-student-loan-rehab-collection-efforts-with-scorecards

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Driving student loan rehab collection efforts with scorecards

Christopher Magnotti Experian

Alex Siotos Experian

2 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.

Would your business be interested in knowing which student loans are likely to go on a repayment program?

Our session will focus on how predictive, for recovery, a collection scorecard is at determining a student loan account’s ability to go into a repayment program

We will focus on how predictive daily event based triggers are at determining a student loan account’s ability to go into a repayment program

We will also look at use cases for the collection treatment strategies

Objective / purpose


Biographies

Chris Magnotti joined Experian in

2009. Chris’s experiences include

full life cycle risk management, P&L

forecasting, stress testing, portfolio

acquisition, international due

diligence, and staff management

and development. Chris has 18

years of experience in risk

management and related analytics.

Chris’s past experiences include

working for a Top-5 portfolio in each

of the following industries; bankcard,

subprime bankcard and private label.

Chris holds a Bachelor degree in

Environmental Science from the

University of Delaware.

Chris Magnotti

Experian

Consultant Lead

Alex Siotos

Experian

Consultancy Director

Alex Siotos is a Business Consultant

for Collections who works with clients

in the financial services industry to

integrate the right solution into their

collection process with the goal of

improving operational efficiency and

shortening collection time.

An expert in account segmentation,

contact strategy, analytics scoring and

operational efficiencies, Siotos has

been entrenched in the collections

market landscape for more than the

past 10 years.


Quote for today

The growing number

of students who have

defaulted on their federal

student loans is troubling.

The Department will

continue to work with

institutions and borrowers

to ensure that student

debt is affordable.

“

” – Arne Duncan

Secretary of Education


Headline news stories

Student loans continue to be a ticking time bomb

NEXT BAILOUT: Student loans?

The number of borrowers defaulting two years into repayment has increased six years in a row


What are the students saying?


Nearly 20 million Americans attend college each year

60% of them borrow annually

There are 37 million student loan borrowers with student loan balances

There is somewhere between $902 billion and $1.2 trillion in outstanding loans

► $864 billion is federal student loan debt

► $150 billion is in private student loans

Student loan statistics


Average loan balance is $24,301

► 10% owe more than $54,000

► 3% owe more than $100,000

Approximately 5 million federal loan borrowers are in default

Approximately 850,000 private loans are in default

► The price tag – $67 billion

Student loan statistics


Student loan balances by age and delinquencies

Age group Loan balance Delinquencies

Under 30 33.9% 25.0%

30-39 32.8% 34.2%

40-49 16.4% 23.1%

50-59 11.3% 12.1%

60+ 4.2% 4.8%

Not Known 1.4% 0.8%


Lowest Delinquency Rates

► Utah

► North Dakota

► Minnesota

► Iowa

► Pennsylvania

Highest Delinquency Rates

► Nevada

► New Mexico

► Oklahoma

► Texas

► South Carolina

► Mississippi

► Florida

What states are best and worst?

Unemployment is lower than national average (< -10%)

Unemployment is higher than national average (> 10%)

Unemployment by State as of Decemeber-2013

Highest/Lowest Student Loan Delinquencies by State

Lowest Delinquency Rates

Highest Delinquency Rates


Comparing delinquency rates

0%

2%

4%

6%

8%% of loan balances that are 90+ days delinquent

Auto Loan & Lease Bank Card Mortgage Student Loan

Student loan delinquencies are up 30% from 2006

Other loan delinquencies are down from 2006


Student loan analytics

Christopher Magnotti


Background Analysis design and timing

Starting population – all placements for eight monthly snap shots

Aug 2012 /

Sep 2012 /

Oct 2012

Three-month performance windows

Nov 2012

to Jan 2013

Feb 2013

to Mar 2013

Apr 2013

to Jun 2013

Performance periods

Nov 2012 /

Dec 2012

Jan 2013 /

Feb 2013 /

Mar 2013

Sep 2012 Credit archive

Nov 2012 Credit archive

Feb 2013 Credit archive

Performance

definitions:

Non-Recovered Account did not enter a

repayment program

Recovered Account did enter a repayment

program


Performance measured over three-month measurement window

Captures attribute at two different time frames and compares to impute a trigger

Background Analysis design and timing

3-month measurement window

June 2013

March 2013

Triggers

calculated


Scorecard models



The majority of payments are collected from a relatively small percentage of consumers

Know which consumers have the ability

to pay before applying collection strategies


Too many consumers, limited collection resources

Working every account the same … is not efficient!


38.3%

13.7%

0.0%

10.0%

20.0%

30.0%

40.0%

PriorityScore Traditional Recovery Score

KS

%

Score Card

KS Values

KS values

180% lift


99.3%

89.8%

70%

80%

90%

100%


% S

core

ab

le

Score Card

% Scoreable Records

Scoreable percentage

10% more

scoreable accounts


0%

20%

40%

60%

80%

100%

Cu

mu

lati

ve

% o

f A

cco

un

ts E

nte

rin

g R

ep

ay

me

nt

Cumulative % of Accounts

Cumulative % of Accounts Entering Repayment Program


Cumulative recovery rate

Top 50%, 81% repayments captured

30% lift Top 50%, 63% repayments captured


0%

10%

20%

30%

40%

50%

60%

70%

Cu

mu

lati

ve %

of

Acc

ou

nts

En

teri

ng

Re

pa

ym

en

t


Best Scoring - % of Accounts Entering Repayment Program


Best scoring capture rates

Note: It is preferred to have more #s

recovered in the best scoring populations

94% lift in accounts entering into a repayment program

baseline

baseline

baseline


0%

10%

20%

30%

Cu

mu

lati

ve %

of

Acc

ou

nts

En

teri

ng

Re

pa

yme

nt


Worst Scoring - % of Accounts Entering Repayment Program


Worst scoring capture rates

Note: It is preferred to have fewer #s

recovered in the worst scoring populations

baseline

baseline

baseline 8% of the accounts entering into a repayment program are left


0%

4%

8%

12%

16%

20%

% o

f A

cco

un

ts E

nte

rin

g R

ep

ay

me

nt

Twentiles (Score Intervals)

Interval % of Accounts Entering Repayment Program


Interval recovery rate

110% lift in accounts entering repayment


$0

$5,000

$10,000

$15,000

$20,000

$25,000

$30,000

Ba

lan

ce


Average Balance on Accounts Entering Repayment Program


Average balance

$25,986 average

$972 average


0%

20%

40%

60%

80%

100%

Cu

mu

lati

ve %

of

Re

ven

ue

Do

lla

rs


Cumulative % of Revenue $ Dollars (revenue = (rehab balance * 70%) * 11%)


Cumulative revenue rate

92% revenue dollars captured

68% revenue dollars captured

% of repayments that complete the 9 month payment program

% of repayments balances that turns into client revenue

35% lift in revenue captured


0%

8%

16%

24%

32%

% o

f R

ev

en

ue

Do

lla

rs


Interval % of Revenue $ Dollars (revenue = (rehab balance * 70%) * 11%)


Interval revenue rate

183% lift in revenue at top 5%


Daily event-based triggers



Background Consumer’s life cycle

No ability to pay

Diminishing ability to pay Full capacity to pay

A consumer’s cycle of delinquency

Consumer

Overextended

Health issues

Job loss Bankruptcy Employment

Paying off

debts

Shopping for

credit

New

credit line

Be notified of these events and then

determine the right time to collect


Daily event-based triggers

7.3%

11.8%

15.1%

0.0%

4.0%

8.0%

12.0%

16.0%

Total Population Total Trigger Population Top Triggers

% Accounts Entering Repayment Program

107% lift in account recovery

10% of total

population 3% of total

population


Daily event-based triggers Top selected triggers

13.7%

12.9%

14.3%

13.7%

18.8%

7.3%

0.0% 4.0% 8.0% 12.0% 16.0% 20.0%

Credit Available

Inquiry

Lien/Civil Action

New Trade

Positive Improvement

Total Population

Avg % Accounts Entering Repayment by Trigger Group for Top Triggers

157% lift

87% lift


Daily event-based triggers Top selected triggers

Trigger Group – Trigger Description Account Entering Repayment %

Positive improvement – current was foreclosure 40.0%

Positive improvement – current was 120 30.3%

Positive improvement – paid was 120 25.0%

Inquiry – business loan inquiry 23.1%

Positive improvement – paid charge-off 22.2%



Positive improvement – settled dispute 20.5%



Lien / civil action – civil action: satisfied, suit dismissed,

wage released or vacated 20.0%

Positive improvement – paid recreational merchandise 20.0%

Positive improvement – current 30 was 120-180 20.0%




Conclusion



Our analysis shows that the scorecard PriorityScoreSM and daily event based triggers are both very predictive in determining which student loan accounts will enter a repayment program

Scorecards alone cannot achieve the highest levels of recovery, incorporating daily event based triggers in with your collection strategies would make recovery more effective and valuable

These are tools that can be used for any student loans types like department of education, private, or state issued loans

Conclusion

If you are looking for 100+% lift in top-performing accounts

entering a repayment program, then PriorityScoreSM and

Collections TriggersSM are the tools you need to consider

for your collection strategies


For additional information, please contact:

[email protected]

[email protected]

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month measurement

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positive improvement

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