The nature of sales in retail banking has changed dramatically. While there is a renewed pressure to grow accounts, the techniques banks have traditionally used to acquire new accounts have become less effective. As consumer preferences continue to shift and non-traditional competitors continue to disrupt the market, the ROI of acquisition techniques like batch mail and branch cross-sell will continue to decline. In order to thrive, banks need to leverage the tremendous amount of data they have on each of their customers to drive more profitable and satisfying customer interactions across all of their channels. This presentation will: • Identify the market trends impacting banks’ growth strategies. • Explore the role of marketing and risk analytics in making better acquisition decisions. • Introduce best practices for implementing a more holistic approach to account acquisition.

1. Death of a Salesman:
Account Acquisition in a New Environment
April 2, 2013
Zoot Enterprises, Inc. Proprietary & Confidential Information.

2. Ed O’Brien
Director Banking Channels
Mercator Advisory Group
Keith Shields
Chief Analytics Officer, Magnify
Chief Credit Officer, Loan Science
Tom Johnson
Vice President, Strategic Alliances
Zoot Enterprises
2

3. AGENDA
• Industry Overview
• More Intelligent Decisions through Analytics
• Next Generation Account Acquisition
• Q&A
3

4. CHANGING MARKET
CONDITIONS SIGNIFICANTLY
IMPACT FI GROWTH STRATEGIES
• Financial Institutions are under intense pressure to
perform, even though the business fundamentals
are challenging
• Reduced fee income
• Increased costs
• Reduced revenues, net interest income, and profitability
• FIs are facing intense pressure to increase their
financial performance throughout their LOBs and
throughout their portfolios
• They need to find ways to profitably grow their
portfolios in new and creative ways
4

5. CONSUMERS ARE
CONSOLIDATING THE NUMBER
OF INSTITUTIONS THEY USE
Mean Number of Financial Institutions Used by Households by Type
(Base = Those with FI relationships by type of FI)
2012 (4.9 mean of all financial institutions) 4.8
2011 (4.9 mean of all financial institutions)
2010 (6.3 mean of all financial institutions)
2.6 2.7 2.7
2.5
2.3
2.1
2 2.1
1.6 1.6 1.6 1.6
1.4 1.3 1.4 1.4 1.4 1.4 1.4
1.2 1.2 1.2 1.2 1.2 1.3 1.2
Full service banks Credit card banks Mortage lenders Credit unions Auto lenders Brokerage firms Online only bank Online Other
brokerage/investment
5

6. CONSUMERS ARE MORE
LOYAL TO THEIR PRIMARY FI
Have You Changed Your Primary Financial Institution in the Past Two Years?
(Base = All)
88% 90%
86%
2010
2011
2012
14% 12% 10%
Yes No
6

7. MOST PREFER IN-PERSON
COMMUNICATION WHEN LEARNING
ABOUT NEW FINANCIAL PRODUCTS
Preferred Method for Becoming Aware of New Financial Products and Services
(Base = All)
In person with an account specialist 28%
In person with teller or greeter 23%
Electronically at ATM or kiosk 7%
Telephone call with account specialist 6%
Chat online at FI website 6%
Teller-assisted videoconference 2%
Other 19%
None of the above 8%
7

8. INDUSTRY OVERVIEW:
FI PROFITABILITY SOLUTION
POSITIONING
Data Cleansing Customer Business
and Quality CRM Analytics Predictive Intelligence
Analytics
Integration Layer
Channel
Systems
Core Banking System Layer
Underlying FI
Infrastructure Application Server Layer
Database Layer
8

9. INDUSTRY OVERVIEW:
VARYING PROFITABILITY
Database/ PERSPECTIVES
Data LOB and
Consulting Strategy Warehouses FI-Centric
Legacy Approaches
Partners Consulting
Systems
Best
Financials
Practices
Systems
Reviews
Profitability Operations
FI Profitability
Analytics Systems
Customer Channels
Analytics Systems
ISV Products FI/ISV
and BI, Reports, Data Partnerships
Consulting KPIs, and Cleansing
Marketing
Services Dashboards and Quality
and CRM
Systems
9

10. INDUSTRY OVERVIEW:
COMMON CATEGORIES OF
ANALYTICS SYSTEMS
Business
Systems Channel
Data Customer Decisioning
and Mgmt
Mgmt Insight Models
Data Systems
Sources
• Databases • Metadata • Predictive analytics • Real-time • Branch
• Customer decisioning • ATM
• Data warehouses • Master data
management experience • Content • Online
• Data marts
• Profitability models management
• Core systems • Data modeling • Mobile
• Risk and compliance • Campaign
• CRM • Business • Call centers
models management
intelligence
• Web • Network analytics • Event • Multichannel
• Dashboards
• Social media management
• Visualization
• Reporting tools
• Querying
capabilities
10

11. ANALYTICS-DRIVEN DECISIONS
• Why do banks (or any lender) invest in analytics?
• Applying analytical techniques, particularly predictive modeling, to
customer data gives forward-looking insight into customer behavior.
• Understanding future customer behavior is integral to making
better decisions and driving lender profitability from two primary
perspectives:
1. Marketing / Pricing – What loan parameters (APR in particular) acquire
the customer’s business?
2. Credit Risk Management – Will the customer default on the loan? Is his
business worth having?
• Death of a Salesman? Possibly.
• The renewed appetite for profitable growth (note Ed’s
presentation), combined with the explosion of available customer
data, make the time right for automatic, realtime, analytically-
informed lending to customers.
11

12. MARKETING AND CREDIT RISK
APPLICATIONS
• The need for analytics within the Marketing and Credit Risk
Management disciplines is pervasive.
• A recent survey of business technology professionals (see below)
indicates that much of the interest in Big Data and Analytics is driven
by (or at least correlated with) Marketing or Risk Management needs.
MARKETING CREDIT RISK
NEEDS NEEDS
Data: Information Week Analytics, Business Intelligence and Information Management
Survey of 417 business technology professionals at companies using or planning to
deploy data analytics, BI or statistical analysis software, October 2012
12

13. MARKETING ANALYTICS &
CREDIT RISK ANALYTICS
• So lenders can make better decisions and drive profitability
through “Credit Risk Analytics” and “Marketing Analytics” (not
exclusively of course).
• Let’s define these terms that we’ll use colloquially throughout the
presentation:
• Credit Risk Analytics: empirically-based quantitative
techniques (e.g. statistical models) aimed at understanding,
predicting, and controlling the level of credit risk associated
with a consumer loan applicant and/or portfolio
• Marketing Analytics: empirically-based quantitative and
qualitative techniques (e.g. statistical models, segmentation)
aimed at understanding, predicting, and classifying the likely
purchase behavior of a consumer or group of consumers
13

14. THE IMPORTANCE OF
CREDIT RISK ANALYTICS
• Let’s show the importance Credit Risk Analytics with an example:
• If a lender makes a $100 profit on a paying loan and loses $400 on a
defaulting loan, then it has to book 4 paying loans for every defaulting
loan just to break even.
• Another way to state the above bullet is this: a loan applicant should
have at least an 80% chance (4:1) of paying as agreed to be
considered for approval.
• How do we determine if an applicant has at least an 80% chance of
paying as agreed? Empirically-derived, demonstrably and statistically
sound models of course. Almost all lenders use these in some form…
• Generic credit bureau scores -and/or-
• Custom scores derived from contract attributes (LTV, PTI) and credit bureau
attribute libraries (from Zoot of course)
14

15. RISK MODELS
• Continuing with the example…the importance of robust,
predictive “risk models”:
• So what if a lender is drawing from a population that is inherently 85%
good (85% will pay off a standard loan) and 15% bad (15% will default
on a standard loan)? Shouldn’t that lender always be profitable?
• It is crucial that the statistical model (or something equivalent) used by
the lender to predict the likelihood of default be able to SEPARATE the
good from the bad. Example below:
• If the model is incapable of any separation whatsoever, it will issue a 15%
probability of default (PD…in bank terminology) for every proposed
contract. This is less than a 20% cutoff, so we approve everything.
• Thus the lender’s profitability is easy to calculate…suppose 1,000 contracts
will be booked: P = 850*$100 – 150*$400 = $25,000 … is it that easy to make
money? By always saying yes to loan applications? That would be Death
of a Credit Analyst. But it’s not that easy…
15

16. “GOOD AND BAD I DEFINE THESE TERMS, QUITE
CLEAR, NO DOUBT, SOMEHOW” -- BOB DYLAN
• Risk models must, first and foremost, distinguish between good and
bad (i.e. rank order the risk):
• “All models are wrong, but some are useful”. -- George E.P. Box
• No individual customer has a 15% chance of default. All individual
customers effectively have a 0% chance or 100% chance of default (they
either do or they don’t).
• Profitability is far greater if the model is able to issue higher PD predictions for
defaults than for payers. This is what happens when a model is able to RANK
ORDER the risk. See the next bullet.
• Back to our example: Suppose the model predicts a PD of 25% for half
the defaults, and 10% for the other half. In turn it issues a PD prediction
of 25% for ¼ of the payers, and 10% for ¾ of the payers.
• Before we calculate profitability we’ll note that the profitability on all
applicants with a predicted PD of greater than 20% is $0. They are declined
based on the breakeven calculation on the previous slide. So…
• P = [850*.25*$0 + 850*.75*$100] – [150*.5*$0 + 150*.5*$400] = $33,750
• This is a 35% increase in profitability due to having a better predictive model.
16

17. MARKETING ANALYTICS
• We’ll continue the discussion with Marketing Analytics, which has become a
staple in the retail industry (Target for example)…
• The analytical techniques used to predict how “in-market” a customer
is for clothes, diapers, etc… can be the same ones used to predict how
“in-market” a customer is for a loan.
• The advantage of using predictive analytics to identify the customers
most likely to take up a loan is that it “expands the base of
incrementality” associated with a loan offer.
• In other words, identifying the groups of customers that are most likely to buy
(take up a loan) is tantamount to identifying the groups that contain the
majority of the “incremental” sales (contracts).
• If I can get the most of the incremental sales by making an offer to only a
small fraction of the population, then I can squeeze most of the benefit from
the offer at a fraction of the cost. Example next slide.
17

18. ADDING BUSINESS VALUE THROUGH
MARKETING ANALYTICS: AN EXAMPLE
• Intelligent use of Marketing Analytics enables lenders to generate
incremental loans cost-effectively and efficiently. An example with
assumptions:
• An untargeted (no model), incentivized loan offer to 100,000 customers increases
the “take rate” by 10%.
• The revenue per incremental loan is $250.
• The cost of communicating the offer to 100,000 consumers is $20,000.
• The cost of the incentive is $20 per loan.
• When we apply a predictive model, we split the population into 4 groups (1=most
likely to take…4=least likely to take)
Organic Offer Incremental Incremental Incremental Incremental
No Model Population
Take Rate Take Rate “Takers” Revenue Cost Profit
Profit = $10,000 100,000 10% 11% 1,000 $250,000 $240,000 $10,000
Model Organic Take Offer Incremental Incremental Incremental Incremental
Population
Rank Rate Take Rate “Takers” Revenue Cost Profit
Use a Model 1 25,000 18% 19.80% 450 $112,500 $104,000 $8,500
2 25,000 14% 15.40% 350 $87,500 $82,000 $5,500
Profit = $14,000
3 25,000 6% 6.60% 150 $37,500 $38,000 ($500)
40% improvement
4 25,000 2% 2.20% 50 $12,500 $16,000 ($3,500)
18

19. THE INTERSECTION OF
MARKETING AND CREDIT RISK
• Practically we should not deploy Marketing Analytics in a lending
environment without doing sound Credit Risk Analytics at the same time.
• Marketing analytic efforts are typically aimed at increasing response (and thus
sales). Doing so can also increase credit risk, which means credit losses can easily
wipe out the gains had by improvements in targeted marketing efforts.
• The right loan offer needs to be defined as the one that maximizes
incremental profit…after incremental credit losses are factored in.
• Making the right loan offer is an analytical exercise that requires the intersection of
Marketing and Credit Risk Analytics. Through the Magnify-Loan Science
partnership, we specialize in this type of exercise…and we deploy through Zoot.
• We see pre-approval models as being the perfect example of this
intersection. And we will show the work we’ve done in auto…
• Common with credit cards…interest rates and credit limits are tested to determine
the impact on response and yearly interest revenue.
• Pre-approval models for auto are more complex, because the presence of
collateral means we have to solve for very important variables like loan-to-value
and term.
19

20. DEATH OF REDEFINING A SALESMAN:
USE ANALYTICS TO TARGET OFFERS
• The tyranny with almost all pre-approval programs is that the customers who
respond to them are the ones you least want to give credit to.
• Customers with low credit bureau scores are generally the ones that
respond to pre-approval offers, and the more exposure the lender is willing
to take, the better they respond.
• See the example below from an auto captive…the data are doctored somewhat
but not to the point where the message is changed:
Population: Existing Customers and Prospects
FICO Score and
Control Target Buy
Pre-Approval Amount Buy Rate Rate “Incrementality” Lift
FICO <= 680 and Pre-Approval >= $30,000 1.93% 2.17% 0.23% 12.14%
FICO <= 680 and Pre-Approval < $30,000 0.93% 1.52% 0.60% 64.27%
FICO > 680 and Pre-Approval < $30,000 2.34% 2.32% -0.01% -0.46%
FICO > 680 and Pre-Approval >= $30,000 2.64% 2.56% -0.08% -2.85%
20

21. MAKE THE RIGHT OFFER
RESPONSIBLY…
• Can we have the high lift associated with high-risk customers AND control the
risk of the pre-approved portfolio? Yes, probably so. Consider turning the
traditional PD (probability of default) model on its head:
• Traditional: PD = f(credit score, LTV, PTI, term,…)
• Pre-approval: LTV = f(PD, credit score, PTI, term,…)
• See the auto captive example below, where we control for PD and solve for LTV
Will yield Tier B performance. Will yield Tier C performance. Will yield Tier D performance.
Tier B rate can be guaranteed in Tier C rate can be guaranteed in Tier D rate can be guaranteed in
the pre-approval offer. the pre-approval offer. the pre-approval offer.
Credit Score PD Term LTV Limit Credit Score PD Term LTV Limit Credit Score PD Term LTV Limit
581-600 4.0% 60 60% 581-600 8.0% 60 75% 581-600 15.0% 60 90%
601-620 4.0% 60 75% 601-620 8.0% 60 85% 601-620 15.0% 60 98%
621-640 4.0% 60 85% 621-640 8.0% 60 94% 621-640 15.0% 60 105%
641-660 4.0% 60 95% 641-660 8.0% 60 100% 641-660 15.0% 60 112%
661-680 4.0% 60 100% 661-680 8.0% 60 105% 661-680 15.0% 60 120%
21

22. REDEFINING SALES OCCURS WHEN THE
BENEFITS OF ANALYTICS AND TECHNOLOGY
ARE TANGIBLE…
• In the example on the previous slide we achieved two important outcomes:
1. We confined our targeting to the customers who, according to our best Marketing
Analytics, would respond to our offer.
2. We confined our offers to those that, according to our best Credit Risk Analytics,
would be profitable at a controlled level of risk and price.
• We also achieved a third very important outcome, which I’ll offer as a
conclusion: we generated incremental loans that subsequently contributed
an additional $8 mils profit per year.
• But what is missing from this good story? DEPLOYMENT. Analytic tools and
technologies must be made available to operational systems when:
• Credit decisions are made, or when
• A list of targeted customers is generated, or when
• The parameters of pre-approval offers are specified
• ... and this is where Zoot fits in: realtime deployment of analytic tools so that
interactions with the customer are informed, targeted, and profitable…
22

23. NEXT GENERATION
ACCOUNT ACQUISITION
23

24. CONSISTENT EXPERIENCE
ACROSS CHANNELS
24

25. TYING IT ALL TOGETHER:
OFFERS REPOSITORY
25

26. OPTIMIZED OFFERS
26

27. BETTER DATA, BETTER DECISIONS
27

28. QUICKER TIME TO MARKET
28

29. SUMMARY
• Sales in retail banking isn’t
dead, but it has changed.
• New channels and more “The only thing you got in this
interactions across all channels. world is what you can sell.
And the funny thing is that
you're a salesman, and
• Analytics available to make you don't know that.”
more intelligent decisions. ~Arthur Miller
Death of a Salesman
• Underlying technology must
support next generation
account acquisition techniques.
29

30. QUESTIONS ?
Ed O’Brien, Director Banking Channels, Mercator Advisory Group
eobrien@mercatoradvisorygroup.com
Keith Shields, Chief Analytics Officer at Magnify Analytic Solutions and
Chief Credit Officer at Loan Science
kshields@MarketingAssociates.com
Tom Johnson, Vice President, Strategic Alliances, Zoot Enterprises
tom@zootweb.com
30