1. © 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. Settlement Optimization Maximizing Returns in Late Stage Collections Paul Robinson Associate Vice President Canadian Tire Bank Matt LaHood Sr. Director FICO

2. Agenda © 2014 Fair Isaac Corporation. Confidential. ► Canadian Tire Overview ► Optimization Process Overview ► Settlement Optimization Analytics ► CTB Experience and Early Results ► Key Learnings 2

3. © 2014 Fair Isaac Corporation. Confidential. 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 Overview Canadian Tire Corporation (CTC) Glacier Credit Card Trust Canadian Tire Retail Segment Financial Services Segment Canadian Tire REIT Segment

4. © 2014 Fair Isaac Corporation. Confidential. Canadian Tire operates over 1,700 stores and its Family of Companies employ over 85,000 Canadians. Canadian Tire Overview 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) CTC Quick Facts

5. © 2014 Fair Isaac Corporation. Confidential. CTB’s Optimization Journey Optimization Strategy Complexity 2007 2008 2009 2010 2011 2012 2013 2014 Credit Limit Management V1 Credit Limit Management V2 Late Stage Collections V1 Early Stage Collections V1 Settlement Optimization V2 Authorizations Optimization V1 Settlement Optimization V1 Initial Credit Limit Assignment V1 Credit Limit Management V3 Credit Limit Management V4

6. © 2014 Fair Isaac Corporation. Confidential. Optimization Process Overview 6

7. © 2014 Fair Isaac Corporation. Confidential. Four Key Steps Unlocking Value from Optimization Decision Modeling ► Establish mathematical relationships between customer treatment options, their reactions and profitability Simulation and Optimization ► Identify optimal strategy scenarios subject to your multiple goals, business constraints and forecasts for the future Implementation & Tracking Interpretation ► Gain insight into key profit drivers and opportunity pockets through diagnostics and final strategy engineering Accelerated Learning ► Test efficiently to learn beyond historical operating regions to further increase future performance Design Framework Optimization Software (Decision Optimizer) Reporting & Analysis 7

8. © 2014 Fair Isaac Corporation. Confidential. Step 1: Model Customers’ Reactions to your Actions CustomerActionReaction 80% Settlement E(Pay) = 35% E(Loss) = 65% E(NPV) = -$3,750 Offer 1 E(Pay) = 50% E(Loss) = 50% E(NPV) = -$2,000 60% Settlement Offer 2 40% Settlement E(Pay) = 70% E(Loss) = 30% E(NPV) = -$200 Offer 3 Risk score = 680 Rev Balance = $10,000 Rev Util = 61% Time in File = 132 Segment = A As Action Effects built into Decision Model it also allows you to run thousands of scenarios very quickly 8

9. © 2014 Fair Isaac Corporation. Confidential. Step 2: Consider Scenarios and Select Operating Point Scenario G Increase profitability without incurring additional losses Current Operating Point Where you are today Scenario B Maintain profitability per account and decrease settlements Total Dollars Settled Projected Profit $ $$ $$$ $$$$ $$$$$ $$$$$$ $$ $$$$$$$$$$$$$ Efficient Frontier Choose the optimal operating point from multiple choices Efficient Frontier I H G F E D C B J A 9 FICO Optimization helps you understand all options

10. © 2014 Fair Isaac Corporation. Confidential. ► 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 visibility into strategies and pockets of opportunity Step 3: Operationalize Benefits 10

11. © 2014 Fair Isaac Corporation. Confidential. Step 4: Accelerate Learnings 11

12. © 2014 Fair Isaac Corporation. Confidential. Settlement Optimization Analytics 12

13. © 2014 Fair Isaac Corporation. Confidential. AreaPredictionExample Objective Pre-delinquency Potential for customer to become delinquent Minimize collection expense Early stage collectionsPriority and treatment of accountsMaximize profit Mortgage workoutsRestructure terms Maximize NPV Minimize loss Credit card settlementsWhich accounts to settle pre-charge off Minimize loss 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 optimizationWhat is the overall potential impact Minimizing costs Maximizing return Examples of Where FICO Has Applied Decision Modeling and Optimization in Collections 13

14. © 2014 Fair Isaac Corporation. Confidential. ► 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 Settlement Offers Are a Powerful, Yet Underutilized, Arrow in the Collection Manager’s Quiver 14

15. © 2014 Fair Isaac Corporation. Confidential. Key Collections Decisions 15 Which accounts to contact How to contact the accounts When to contact the accounts 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) Charge-off Early Stage Collections Late Stage Collections Recovery 120150180PrimarySecondary Decisions 306090

16. © 2014 Fair Isaac Corporation. Confidential. How Does Your Institution See the Problem? 16 But much of this potential is lost when settlement policies rely on a simple rules based approach… ► Profit ► Loss Rates ► Payment Program Loss Rates ► Revenue ► Revenue “Give-back” ► Collection Costs

17. © 2014 Fair Isaac Corporation. Confidential. With Optimization, Conflicting Goals Are Understood and Balanced 17

18. © 2014 Fair Isaac Corporation. Confidential. Illustrative Example of Action-effect Model for a Settlement Plan With Consumer Response Included 18 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

19. © 2014 Fair Isaac Corporation. Confidential. Settlement Optimization Influence Diagram 19 Settlement offer decisions range from 100% (No Offer) to 20% of balances outstanding, with impact on Net $Paid captured by action-effect models Offer No Call Offer Call Settlement Net $ Paid No Contact P(Charge-off) Not Paid Collector (In-house, Agency)

20. © 2014 Fair Isaac Corporation. Confidential. CTB Experience and Early Results 20

21. © 2014 Fair Isaac Corporation. Confidential. ► 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 CTB Settlement Optimization Objectives 21

22. © 2014 Fair Isaac Corporation. Confidential. ► 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 Learning ► Closely monitor the results to ensure enough successful settlements for future model development CTB Settlement Optimization: Getting Started 22

23. © 2014 Fair Isaac Corporation. Confidential. CTFS 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 (cure): ► Attempt to cure the account using normal practices ► Write offs and recoveries will come over a longer period in the future CTB Settlement Optimization Testing Framework 23

24. © 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 24

25. © 2014 Fair Isaac Corporation. Confidential. ► Contact tactics play an important role in obtaining a Settlement arrangement ► Dialing campaigns varied; outbound, managed dial, limited skip etc. campaigns and follow up calls ► Concurrently to outbound calling, tested three types of letter contact: ► 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 CTB Settlement Optimization: Contact Strategy 25

26. © 2014 Fair Isaac Corporation. Confidential. CTFS Settlement Optimization – Valuation ► The ‘cash flow’ dynamics of the Action group are fundamentally different vs. the Control group, leading to negative valuation in the short term CTB Settlement Optimization: Valuation 26 Value Metric Timing Dynamic ActionControl Write OffsBrought ForwardEventually catch up? (Less) RecoveriesBrought ForwardEventually catch up? (Less) Agency FeesBrought ForwardEventually catch up? /Divided by Receivables = Equals Net Loss RatioHigher in Short TermHigher in Long? Term Write Offs – Recoveries – Agency Fees Receivables = Net Loss Ratio

27. © 2014 Fair Isaac Corporation. Confidential. CTFS Settlement 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 CTB Settlement Optimization: Contingent Liability 27

28. © 2014 Fair Isaac Corporation. Confidential. Key Learnings 28

29. © 2014 Fair Isaac Corporation. Confidential. CTFS 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 CTB Settlement Optimization: Key Learnings 29

30. © 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. Thank You! Matt LaHood mlahood@fico.com 415.690.0768 Paul Robinson Paul.Robinson@ctfs.com

31. © 2014 Fair Isaac Corporation. Confidential. Learn More at FICO World Related Sessions ► Collection Platform Success in a Transitioning Economy ► The New Normal: Adopting and Adapting to Drive Sustainable, Profitable Growth Products in Solution Center ► FICO ® Decision Optimizer ► FICO ® Debt Manager ► FICO ® Customer Communication Services: Collections Experts at FICO World ► Cheryl Miller ► Matt Stanley ► Ana Marcos ► Matt LaHood White Papers Online ► Five Imperatives in a Shifting Collections Landscape ► Harnessing the Speech Analytics Advantage Blogs ► www.fico.com/blog 31

32. © 2014 Fair Isaac Corporation. Confidential. Please rate this session in the FICO World App! Matt LaHood mlahood@fico.com 32 Paul Robinson Paul.Robinson@ctfs.com