Settlement Optimization
Maximizing Returns in Late Stage Collections
Paul Robinson
Associate Vice President
Canadian Tire Bank
Matt LaHood
Sr. Director
FICO
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Agenda
►Canadian
Tire Overview
►Optimization
►Settlement
►CTB
►Key
2
Process Overview
Optimization Analytics
Experience and Early Results
Learnings
© 2014 Fair Isaac Corporation. Confidential.
Canadian Tire Overview
Canadian Tire is one of the most recognized brands in Canada comprised of a
diversified retail segment and a robust financial services business.
Canadian Tire Corporation (CTC)
Canadian Tire Retail
Segment
Canadian Tire REIT
Segment
Financial Services
Segment
Glacier Credit
Card Trust
© 2014 Fair Isaac Corporation. Confidential.
Canadian Tire Overview
Canadian Tire operates over 1,700 stores and its Family of Companies employ over
85,000 Canadians.
CTC Quick Facts
 Founded in 1922 by the Billes family who still own a
controlling stake
 Diversified retail banners including Canadian Tire Retail,
PartSource, Sport Chek, Sports Experts, Pro Hockey Life
and Mark’s
 90% of Canadians are within 15 minutes of a CTR store;
60% of Canadians visit a CTR store each month
 31 million square feet of retail space under multiple banners
 Market leadership in Automotive, Fixing, Living and Playing
categories, as well as Men’s Industrial and Casual Apparel
 Named Marketer of the Year (2013)
 Named one of North America’s Top 50 Brands (2014)
 One of Canada’s Best Managed Companies (2013)
 Canada’s Second Most-Trusted and Loved Brand (Ipsos)
© 2014 Fair Isaac Corporation. Confidential.
Optimization Strategy Complexity
CTB’s Optimization Journey
Authorizations
Optimization V1
Settlement Optimization V1
Initial Credit Limit Assignment V1
Settlement
Optimization V2
Credit Limit Management V3
Early Stage
Collections V1
Late Stage
Collections V1
Credit Limit
Management V1
2007
2008
© 2014 Fair Isaac Corporation. Confidential.
Credit Limit
Management V4
Credit Limit
Management V2
2009
2010
2011
2012
2013
2014
Optimization Process Overview
6
© 2014 Fair Isaac Corporation. Confidential.
Unlocking Value from Optimization
Four Key Steps
Accelerated Learning
►
Test efficiently to learn
beyond historical operating
regions to further increase
future performance
Design
Framework
Implementation
& Tracking
►
Gain insight into key profit
drivers and opportunity pockets
through diagnostics and final
strategy engineering
© 2014 Fair Isaac Corporation. Confidential.
Establish mathematical
relationships between customer
treatment options, their reactions
and profitability
Simulation and Optimization
Reporting &
Analysis
7
►
Optimization Software
(Decision Optimizer)
Interpretation
►
Decision Modeling
Identify optimal strategy
scenarios subject to your
multiple goals, business
constraints and forecasts for
the future
Step 1: Model Customers’ Reactions to your Actions
Customer
Risk score = 680
Rev Balance = $10,000
Rev Util = 61%
Time in File = 132
Segment = A
Action
Offer
80% Settlement
1
E(Pay) = 35%
E(Loss) = 65%
E(NPV) = -$3,750
Offer
60% Settlement
2
E(Pay) = 50%
E(Loss) = 50%
E(NPV) = -$2,000
Offer
40% Settlement
3
E(Pay) = 70%
E(Loss) = 30%
E(NPV) = -$200
As Action Effects built into Decision Model it also allows
you to run thousands of scenarios very quickly
8
© 2014 Fair Isaac Corporation. Confidential.
Reaction
Step 2: Consider Scenarios and Select Operating Point
Efficient Frontier
Choose the optimal operating point from multiple choices
Projected Profit
$$$$$$ Scenario G
Increase profitability without
incurring additional losses
$$$$$
F
G
H
I
Efficient
Frontier
J
E
D
Current Operating Point
Where you are today
$$$$
C
$$$
B
FICO Optimization
helps you
understand all
options
Scenario B
Maintain profitability per account
and decrease settlements
$$
A
$
$
$$
$$$
Total Dollars Settled
9
© 2014 Fair Isaac Corporation. Confidential.
$$$$
$$$$$
Step 3: Operationalize Benefits
► Optimized
treatments can be converted into decision trees and loaded
into collection system in order to execute consistent decisions every
month, week, day, etc.
► Trees
should have interpretability, robustness and ease
of implementation
► Provide
10
visibility into strategies and pockets of opportunity
© 2014 Fair Isaac Corporation. Confidential.
Step 4: Accelerate Learnings
11
© 2014 Fair Isaac Corporation. Confidential.
Settlement Optimization Analytics
12
© 2014 Fair Isaac Corporation. Confidential.
Examples of Where FICO Has Applied Decision Modeling
and Optimization in Collections
Area
Prediction
Example Objective
Pre-delinquency
Potential for customer to become
delinquent
Minimize collection expense
Early stage collections
Priority and treatment of accounts
Maximize profit
Mortgage workouts
Restructure terms
Maximize NPV
Minimize loss
Credit card settlements
Which accounts to settle pre-charge off
Settlement after charge-off
Who is the most likely candidate for a
settlement and for what $ amount
Maximize money collected
Maximize profit
Agency placement
Which accounts to place with which
agency
Maximize profit
Maximize money collected
Collections optimization
What is the overall potential impact
Minimizing costs
Maximizing return
13
© 2014 Fair Isaac Corporation. Confidential.
Minimize loss
Settlement Offers Are a Powerful, Yet Underutilized,
Arrow in the Collection Manager’s Quiver
► Too
much time, effort, and resource is spent pursuing borrowers who won’t
fully pay
► Settling
with the right people for the right amount can accelerate dollars
collected while freeing resource to work accounts where their efforts make
a difference
► Identifying
the right settlement offer for the right borrower at the right time
is immensely difficult
14
© 2014 Fair Isaac Corporation. Confidential.
Key Collections Decisions
Early Stage
Collections
30
60
Late Stage
Collections
90
120
150
Recovery
180
Primary
Secondary
Decisions
Charge-off
15
Which accounts to contact
How to contact the accounts
When to contact the accounts
© 2014 Fair Isaac Corporation. Confidential.
How to work out
the accounts (settlement,
payment plan options)
Which accounts to send to
early outs
Where to place the account
(which agency or attorney)
Which accounts to sell (for how
much)
How Does Your Institution See the Problem?
►
Profit
►
Revenue
►
Loss Rates
►
Revenue “Give-back”
►
Payment Program Loss
Rates
►
Collection Costs
But much of this potential is lost when settlement policies
rely on a simple rules based approach…
16
© 2014 Fair Isaac Corporation. Confidential.
With Optimization, Conflicting Goals Are Understood
and Balanced
17
© 2014 Fair Isaac Corporation. Confidential.
With Consumer Response Included
Illustrative Example of Action-effect Model for a Settlement Plan
In this oversimplified example for Segment 2, settlement
percent of 40% has a higher NPV
E(NPV | 70%) = 70% * $10,000 * 41%-$10,000 * (1–41%)
= -$3,030
E(NPV | 40%) = 40% * $10,000 * 70%-$10,000 * (1–70%)
= -$200
While optimization is running extremely sophisticated
mathematics behind the scenes; it is NOT a black box
18
© 2014 Fair Isaac Corporation. Confidential.
Settlement Optimization Influence Diagram
Settlement offer decisions range from 100% (No Offer) to 20% of
balances outstanding, with impact on Net $Paid captured by action-effect models
Offer
Not Paid
Call
Net $
Paid
No Contact
Offer
No Call
19
Settlement
© 2014 Fair Isaac Corporation. Confidential.
Collector
(In-house,
Agency)
P(Charge-off)
CTB Experience and Early Results
20
© 2014 Fair Isaac Corporation. Confidential.
CTB Settlement Optimization Objectives
► Premise:
Circumvent future collections efforts and agency recovery fees
through proactive settlement on impaired accounts across all balance
ranges and past due statuses
► Objectives:
► Gather
Data: Target actions to past due accounts that align with the model
development population (high charge-off rates and probability of listing), but
collect response information to enable improved future model precision
► Execute Well: Leverage offer response information to improve future settlement
offer positioning and perfect operational execution
► Repeat: Create a sustainable business based on experiences from pilot and
revised models
21
© 2014 Fair Isaac Corporation. Confidential.
CTB Settlement Optimization: Getting Started
► Decision
Modeling
►
CTB had little experience with proactive, in house settlement offers
► Used limited agency data to help build models
► Simulation
and Optimization
►
Target accounts with high probability of charge off and listing
► Action space: 20% to 90% of balance
► Interpretation
►
Test vs. Control framework leverages existing TRIAD implementation to select accounts for Settlement
Optimization (Action) and business as usual (Control)
► Created a dedicated outbound/inbound collections team from scratch
► Accelerated
►
22
Learning
Closely monitor the results to ensure enough successful settlements for future model development
© 2014 Fair Isaac Corporation. Confidential.
CTFS Settlement
Optimization
Testing Framework
CTB
Settlement
Optimization
Testing Framework
► NOT
business as usual as there are inherent differences in tactic execution
► Action
►
►
(liquidate):
Attempt to sell settlement, if no success list to agency for full balance
The lifetime stream of payments has been brought forward to a shorter window
► Control
►
►
23
(cure):
Attempt to cure the account using normal practices
Write offs and recoveries will come over a longer period in the future
© 2014 Fair Isaac Corporation. Confidential.
CTB Settlement Optimization: Early Results
► The Action
Group clearly brings write offs
forward in time when compared to the
Control group…
► …and
the Action Group has higher overall
payments (internal and Agency) when
compared to the Control Group
Cumulative % of Balance Paid by Month queued,
Monthly Cohort Average
Cumulative % of Balance Written-off by Month
queued, Monthly Cohort Average
1
2
3
4
5
6
7
8
Action
24
© 2014 Fair Isaac Corporation. Confidential.
9
10
Control
11
12
13
14
15
1
2
3
4
5
6
7
Action
8
9
10
Control
11
12
13
14
15
CTB Settlement Optimization: Contact Strategy
► Contact
tactics play an important role in
obtaining a Settlement arrangement
Contact Rate By Letter Type By Cohort
35%
► Dialing
campaigns varied; outbound,
managed dial, limited skip etc. campaigns
and follow up calls
to outbound calling, tested
three types of letter contact:
25%
►
20%
► Concurrently
Specified Settlement Amount vs.
► Generic ‘Helpful Solutions’ vs.
► No letter
► Ongoing
concern that a Settlement offer in
writing may impact value on future sale of
receivables
25
30%
© 2014 Fair Isaac Corporation. Confidential.
No Letter
Specific Letter
Generic Letter
CTFS Settlement
Settlement Optimization
– Valuation Valuation
CTB
Optimization:
►
Timing Dynamic
Value Metric
Write Offs
Action
Control
Brought Forward
Eventually catch up?
The ‘cash flow’ dynamics of the Action group are
fundamentally different vs. the Control group,
leading to negative valuation in the short term
Average Difference in Cumulative Net Loss
Ratio (Action - Control)
(Less)
Recoveries
Brought Forward
Eventually catch up?
16%
(Less)
Brought Forward
Eventually catch up?
/Divided by
Receivables
= Equals
Net Loss Ratio
Higher in Short Term
Higher in Long? Term
Write Offs – Recoveries – Agency Fees
= Net Loss Ratio
Receivables
Cumulative Net Loss Ratio Difference
Agency Fees
14%
12%
10%
8%
6%
4%
2%
0%
-2%
1
2
3
4
5
6
7
8
9
Months after Queued
26
© 2014 Fair Isaac Corporation. Confidential.
10
11
12
13
14
CTFS Settlement
Settlement Optimization
– Contingent
Liability
CTB
Optimization:
Contingent
Liability
► The
valuation method does not entirely capture the reduction in high risk
contingent liability
► The
‘remaining balance at risk’ reflects the proportion of the original balance that
has yet to be paid, recovered or charged off—the Control group has much larger
contingent liability vs. the Action group
Action Receivables
100%
100%
80%
80%
60%
60%
40%
40%
20%
20%
0%
0%
1
27
Control Receivables
2
3
Receivables at Risk
© 2014 Fair Isaac Corporation. Confidential.
4
Total Losses
5
6
7
8
Total Payments & Recoveries
1
2
3
Receivables at Risk
4
Total Losses
5
6
7
8
Total Payments & Recoveries
Key Learnings
28
© 2014 Fair Isaac Corporation. Confidential.
CTFS Settlement
Optimization
– Key Learnings
CTB
Settlement
Optimization:
Key Learnings
► CTB
can find customers willing to accept and pay a Settlement offer, using good modeling
techniques and effective operational execution
► Settlements
generates significant improvements in overall recoveries, for both in-house and
agency collections
► Earlier
►
actions drive earlier listings and write-offs
Timing of actions has potential to significantly disrupt normal portfolio cash-flows
► Test/Learn/Rollout
►
approach
Start with existing response information, and plan data collection up front to enable better models and
coverage for the future
► Early
and clear communication with internal stakeholders regarding expected impact of new
settlement strategy on both recoveries and write-offs is key to ongoing program success
29
© 2014 Fair Isaac Corporation. Confidential.
Thank You!
Paul Robinson
Paul.Robinson@ctfs.com
Matt LaHood
mlahood@fico.com
415.690.0768
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
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© 2014 Fair Isaac Corporation. Confidential.
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Paul Robinson
Paul.Robinson@ctfs.com
32
© 2014 Fair Isaac Corporation. Confidential.
Matt LaHood
mlahood@fico.com