This document discusses past, present, and emerging paradigms in risk modeling and pricing for lending. It covers: 1) Generic credit bureau scores have long been used for lending decisions but custom models using lenders' own data can better rank risk. Custom scores are now widely used, incorporating more contract data. 2) An emerging paradigm fixes a target probability of default and solves for loan terms (amount, APR, term) that meet that risk level rather than basing pricing on credit score alone. This allows matching customers to any vehicle, like a peer-to-peer lending platform for autos. 3) The "Big Data" revolution enables capturing more applicant information to further refine risk assessments and

1. Risk Modeling and Pricing Paradigms:
Past, Present, and Emerging
A Consultant’s View
Keith Shields
SVP, Analytic Services – Loan Science
Chief Analytics Officer – Magnify Analytic Solutions

2. Introduction and Background
 Who are we and why this topic? And do we know anything?
 Is credit scoring dead?
– Uber?
– P2P?
 In exploring past and present paradigms we remember how
useful credit scoring is.
 We also see how the “Big Data Revolution” allows us to get so
much more out of it.
 But everything boils down to DEPLOYMENT.

3. A Past and Present Paradigm: Generic Scores
 Generic scores / Bureau Scores
– Have been, and still are, the basis for many credit decisions
– Very useful properties
• Rank order the risk - below left
• Quantifiable relationship with the probability of default - below right
• Facile deployment
• Pervasive…they exist in almost everyone’s vernacular
Worst 40% => 70% of
defaults

4. A Past and Present Paradigm:
Pricing with an LTV-dependent matrix to control the contract…
 The Bureau Score-LTV matrix
– The pricing problem is more or less solved within the grid
– Important factors like PTI considered during underwriting
– The importance of facile deployment
Can we price for a
22.2% default rate?
If so, then aren’t
these approvable?
Default Rate
Credit Score 80% 90% 100% 110% 120% 130%
540 22.2% 31.9% 36.6% 40.0% 41.5% 43.0%
560 19.1% 25.7% 27.3% 31.6% 32.5% 33.6%
600 17.2% 24.9% 26.4% 28.8% 30.9% 28.4%
640 10.9% 16.7% 19.0% 23.2% 25.2% 27.0%
680 6.3% 6.7% 9.1% 11.8% 14.5% 13.8%
720 2.7% 3.7% 4.7% 5.7% 8.4% 8.5%
760 0.9% 3.4% 3.7% 5.2% 5.5% 5.6%
800 0.5% 1.2% 1.8% 2.9% 5.1% 3.5%
840 0.2% 0.4% 0.8% 1.5% 2.0% 2.6%
LTV

5. Moving On From The Past…
 Why stay with the generic
scores?
 Can we get a better ranking with
our own data?
 Won’t a better risk ranking
enable more approvals and a
reduction in defaults? See graph
right.
 Things to consider:
– Capability and Scale
– Business and process
nuances
– Deployment
– The Credit Score-LTV
matrix is essentially a
custom model.
– Validation and CFPB
Worst 40% => 75% of
defaults

6. A Present Paradigm: The Pervasiveness of Custom Scores
 Driving the need to improve over the generic scores…
– “Big Data”: Data retention and scalable data platforms (data warehouses)
• The more we store, and the more we analyze, the more we know
– Modeling exercises begin as an effort to understand and evolve into an
effort to predict.
– Evolution and availability of nuanced and sometimes esoteric credit
attributes (which can improve segmentation as well as models)
• Bureaus have made these easily deployable as well
– Cost of funds disadvantage for some lenders creates a need to find “650s
that perform like 700s”. When there is a cost of funds disadvantage, the
lender can’t afford to make the same decisions as the competition.
– Subprime lending – success very much depends on our ability to get good
ranking between 500 and 600…which results in our having to differentiate
between a “good 550” and a “bad 550”.

7. The Custom Score and Pricing
 To date custom scores have offered improved risk assessment
largely through the incorporation of contract data.
– A customized score-to-odds:
• ln(p/(1-p)) = A + B*BUREAU_SCORE
– The basis for the Bureau Score-LTV matrix is, as previously
mentioned, a custom model:
• ln(p/(1-p)) = A + B*BUREAU_SCORE + C*LTV +
D*BUREAU_SCORE*LTV

8. The Custom Score and Pricing
 Our desire to price based on the custom score creates a
deployment problem.
– Pricing decisions are no longer based just on LTV and credit score:
Nuanced credit attributes, PTI, Term, Vehicle Age, or perhaps even
the presence of a SID
– Contracts have to be entered before a score can be generated, and a
score must be generated before a price can be determined, but a
price must be assumed to generate a contract.
– The vicious circle:
Gotta know the
contract to get a
score
Gotta know the
score to get an
APR
Gotta know the
APR to get a
contract

9. An Emerging Paradigm:
Making the Most of the Custom Score “Math”
 Probability of Default = f(Custom Score)
 Custom Score = f(Credit attributes, Applicant Info, LTV, PTI, Term)
– Credit, applicant and income are all fixed
– Monthly payment = f(Loan Amount, APR, Term)
– What if PD is also fixed? What if we have a “target PD”?
 Target PD = f(Fixed values, Loan Amount, Vehicle Value, APR, Term)
 => APR = F(Target PD, Loan Amount, Vehicle Value, Term, Fixed
Values)
 This question:
– What probability of default is created by the proposed contract?
 Becomes:
– What is the APR, Loan Amount, and Term that creates a desired
probability of default (one that we are priced for)?
 And the best contract needn’t be on the proposed vehicle. Can we marry
the customer to any vehicle on the lot?
 Lenders already do this on a limited scale. But why limit the scale?

10. An Emerging Paradigm: Matching a Customer to a Vehicle
 LTV = Loan Amount / Vehicle Value
– LTV has a lot of leverage on the probability of default and loss given
default (a level of complexity not discussed on the previous slide)
– It can be reduced through the numerator (additional cash in a deal is
logical and popular condition), but it can also be reduced though the
denominator.
– The range of the denominator is dependent upon the vehicles we can
offer.
 APR = F(Target PD, Loan Amount, Term, Vehicle Value, Fixed
Values)
– The mathematical expression above allows us to solve for a loan
amount and APR for any vehicle-customer combination.
– What about a virtual vehicle lot (like Ebay Motors)? The math allows
us to generate thousands, perhaps millions, of potential contracts on
a given customer.
– This becomes similar to a P2P lending paradigm for autos.

11. The Auto-Lending “Platform”
 Three questions we force upon ourselves when
evaluating emerging paradigms:
1. Is our industry ripe for disruption?
2. Is there is “Big Data” play driving the disruption?
3. What’s the plan?

12. Questions
Questions?
Thank you for your time and attention.
www.loanscience.com
www.magnifyas.com