Credit Risk Modeling for Alternative Lending
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Transcript of Credit Risk Modeling for Alternative Lending
Keith Shields: Chief Analytics Officer - MagnifyBruce Lund, Ph.D.: Director, Thought Leadership - Magnify
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Credit Risk Modeling for Alternative Lending
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
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Credit Risk Modeling for Alternative Lending
Speaker Intro
Keith Shields 20+ years in financial services, developing and deploying statistical models to predict
credit risk across multiple asset classes
CAO Magnify & SVP Loan Science: 7 years of Analytic Services and Solutions
14 years at Ford Motor Credit, Director of Global Analytics
2+ years at Credit Acceptance Corp, VP Portfolio Management
Dr. Bruce Lund 22 years at Ford Marketing: created the Customer Knowledge System, a DW that
houses customer data, predicts customer behavior, and steers 1-1 marketing efforts for 30+ million Ford customers
Former Math professor
Analytics “Godfather” of Magnify
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
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Credit Risk Modeling for Alternative Lending
Tying to the Abstract…
Key Elements: Credit Risk ModelingScorecards and Cutoff ScoresAlternative Lending, Marketplace Lending, and FintechCommon predictive modeling conventionsRegulatory PressureThe Demand for Creativity and DisruptionWhat Makes “Alternative Lending Analytics” Different from “Banking Analytics”?The Continued Relevance of Logistic Regression“It is not the model fitting techniques that need to change, as much as it is the treatment of samples, potential predictors, and model refits”.
3Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
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Credit Risk Modeling for Alternative Lending
Definition and Function A credit risk model is, usually, a linear statistical model that uses combination
of credit, contract, and personal attributes to predict the likelihood that a loan applicant will default (fail to pay back the loan).
“Attributes” = “Characteristics” = Independent variables Credit Score Loan-to-Value Ratio (LTV)…insert diatribe on 2008 mortgage disaster Payment-to-Income Ratio (PTI) Revolving Utilization Recent Credit Inquiries
Integral part of how lending is done. PD’s, Basel III, SR 11-7 Is lending ever done without models? The Big Short…
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Credit Risk Modeling
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Credit Risk Modeling for Alternative Lending
History Bill Fair, Earl Isaac…”FICO” Credit Scoring System: Credit Risk Models are converted to
“scorecards” by creating a point system whereby the parameter estimates of the credit risk model are multiplied by the possible values of the independent variables to create points.
Example: Y = b0 + b1*x1. Suppose x1 can take on a value of 1, 2, or 3. Scorecard:
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Credit Risk Modeling
X1 Points1 B1 * 1 * 1002 B1 * 2 * 1003 B1 * 3 * 100
Note: Typically we transform X1 first
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Credit Risk Modeling for Alternative Lending
Tying to the Abstract…
Key Elements: Credit Risk ModelingScorecards and Cutoff ScoresAlternative Lending, Marketplace Lending, and FintechCommon predictive modeling conventionsRegulatory PressureThe Demand for Creativity and DisruptionWhat Makes “Alternative Lending Analytics” Different from “Banking Analytics”?The Continued Relevance of Logistic Regression“It is not the model fitting techniques that need to change, as much as it is the treatment of samples, potential predictors, and model refits”.
6Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
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Credit Risk Modeling for Alternative Lending
Example: Assume we make $1,000 profit on “good” loans and suffer a $10,000 loss on “bad” loans:
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Scorecards and Cutoff Scores
Credit Score% of
Applicants Good Rate Loss (Bad) RateExpectedNet Profit
Cumulative Approval
RateCumulative Loss Rate
500 40% 99.5% 0.5% $945 40% 0.5%400 20% 96.0% 4.0% $560 60% 1.7%300 15% 92.0% 8.0% $120 75% 2.9%200 15% 87.0% 13.0% -$430 90% 4.6%100 10% 75.0% 25.0% -$1,750 100% 6.7%
• Cutoff score = 300. We lose money on borrowers who score below 300.• We will decline roughly 25% of our applicants.• Our loss rate will be 2.9% (as opposed to 6.7% if we approved everyone).• Our profit per contract is $677 (as opposed to $268 if we approved everything).
Some simple math on each credit tranche leads us to very useful conclusions:
(40%*.5%+20%*4%+15%*8%)/75%
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Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Credit Risk Modeling for Alternative Lending
Tying to the Abstract…
Key Elements:
Credit Risk ModelingScorecards and Cutoff ScoresAlternative Lending, Marketplace Lending, and FintechCommon predictive modeling conventionsRegulatory PressureThe Demand for Creativity and DisruptionWhat Makes “Alternative Lending Analytics” Different from “Banking Analytics”?The Continued Relevance of Logistic Regression“It is not the model fitting techniques that need to change, as much as it is the treatment of samples, potential predictors, and model refits”.
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Credit Risk Modeling for Alternative Lending
Definition and Players The simplest definition is that an alternative lender is any lender that
isn’t a bank (or captive, in my opinion) . Historically the customers of alternative lenders are people, or small
businesses, who are unable to secure a bank loan. This is still largely the case, but not nearly to the extent it once was.
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Alternative Lending
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Credit Risk Modeling for Alternative Lending
Modern Nuances and New Entrants Recently many start-up alternative lenders have distinguished themselves
from banks based on heavier use of technology, removal of traditional loan structures, and tapping into overlooked sources of collateral and non-traditional funding sources: Technology: Fintech platforms Removal of traditional repayment plans: Merchant Cash Advance, Payday
Loans, and Uber Overlooked sources of collateral: merchant invoices Non-traditional funding sources: Peer-to-Peer (P2P) or “Marketplace”
Lending
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Alternative Lending
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Credit Risk Modeling for Alternative Lending
Is there a place for us (statisticians, data scientists, analysts)? In a big way yes:
Huge expenditures on alternative data (Yodlee, Clarity, LendDo, Yelp) 100% technology-enabled, model-driven, loan adjudication and pricing Loan acquisition strategies driven completely from customer behavior and
“in-market” models
But there are also minefields: Small samples and the “overfitting contest” Biased samples Relentless pressure to grow (before regulation) and strong appetite from
secondary markets. The Big Short?
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Alternative Lending
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Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Credit Risk Modeling for Alternative Lending
Tying to the Abstract…
Key Elements: Credit Risk ModelingScorecards and Cutoff ScoresAlternative Lending, Marketplace Lending, and FintechCommon predictive modeling conventionsRegulatory PressureThe Demand for Creativity and DisruptionWhat Makes “Alternative Lending Analytics” Different from “Banking Analytics”?The Continued Relevance of Logistic Regression“It is not the model fitting techniques that need to change, as much as it is the treatment of samples, potential predictors, and model refits”.
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Credit Risk Modeling for Alternative Lending
Logistic regression offers the right construct for estimating risk… Let PD = “Probability of Default”. Let Y=1 be default and Y=0 be
paid as agreed PD is related to attributes Xk’s by taking a weighted sum of the
attributes called “xbeta” where xbeta = β0 + β1X1 + … + βnXn and substituting xbeta into the logistic function
PD = P(Y=1|Xk) = … the logistic function
We need to find the ’s which best connect the Xk’s to the Y’s If Y=1, want PD big If Y=0, want 1-PD big Done by maximizing the likelihood function “L” as a function
of ’s
Max [ L( X) = (1 - )1- ]Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
The Continued Relevance of Logistic Regression
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Credit Risk Modeling for Alternative Lending
Logistic regression models are at the root of most credit scores Per Wiki: Although logistic (or non-linear) probability
modelling is still the most popular means by which to develop scorecards, various other methods offer powerful alternatives, including MARS, CART, CHAID, and random forests.
Machine learning has become really popular lately, particularly among alternative lenders. The overfitting contest continues (next slide)…
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
The Continued Relevance of Logistic Regression … But
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Credit Risk Modeling for Alternative Lending
Comparing a Logistic Regression Model with SVM ModelThe sample was split 50/50 (sample1 / sample2).The following steps were performed for both the logistic regression model and SVM model: Sample1 was used to fit a model that was subsequently used to score sample2 Sample2 was used to fit a model that was subsequently used to score sample1
00%-37%
37%-45%
45%-52%
52%-60%
60%-67%
67%-72%
72%+0%
10%20%30%40%50%60%70%80%90%
100%
0%
5%
10%
15%
20%
25%
30%
38.1% 38.5% 40.3%
58.3%66.7%
73.1% 70.3%
Validation of Machine Learning Model: Test Samples
% of Funded Actual Predicted
SVM Prediction
Rene
wal
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lihoo
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% o
f Fun
ded
Cont
ract
s
00%-37%
37%-45%
45%-52%
52%-60%
60%-67%
67%-72%
72%+0%
10%20%30%40%50%60%70%80%90%
0%
5%
10%
15%
20%
25%
37.3% 38.1%
66.7%
47.4%
68.9% 67.9% 68.5%
Validation of Logistic Regression Model: Test Samples
% of Funded Actual Predicted
Logistic Regression Prediction
Rene
wal
Like
lihoo
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% o
f Acti
ve C
ontr
acts
KS=28.0
KS=24.4
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Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Credit Risk Modeling for Alternative Lending
Tying to the Abstract…
Key Elements: Credit Risk ModelingScorecards and Cutoff ScoresAlternative Lending, Marketplace Lending, and FintechCommon predictive modeling conventionsRegulatory PressureThe Demand for Creativity and DisruptionWhat Makes “Alternative Lending Analytics” Different from “Banking Analytics”?The Continued Relevance of Logistic Regression“It is not the model fitting techniques that need to change, as much as it is the treatment of samples, potential predictors, and model refits”.
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Credit Risk Modeling for Alternative Lending
Building predictive models has become a canned process for many banks – why?
Reg B: Empirically-derived, statistically sound ECOA OCC 11-7: Supervisory Guidelines on Model Risk Management CFPB and lenders’ lack of transparency Credit Ratings and Secondary Markets Banks shrinking desire to do retail banking. The alternative?
Commercial lines-of-credit for alternative lenders Millennials, millennials, millennials…
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Regulatory and Societal Pressures
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Credit Risk Modeling for Alternative Lending
But we still have to find ways to accurately assess risk and price loans…
Big Data can’t be just a buzzword
Unstructured data has to be structured in an intelligent way
Personally-verifiable, or volunteered, data
Technology devoted to collecting unstructured data and structuring it
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The Demand For Creativity and Disruption
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Credit Risk Modeling for Alternative Lending
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Analytics: Banking vs. Alternative LendingIssue Banking Alternative
LendersHow do alternative lenders handle the modeling problems?
Sample Sizes
Large Small Strong priorsFix parameter estimates“Wider” samples, not longer
History Spans multiple economic cycles
1-3 years, these are mostly start-up companies
“Look-alike samples”: BUY DATARefit and relearnIntroduce products that control risk without having to model it (e.g. SoFi)Change the target event to something that happens quickly (e.g. Loan Science)
Regulation Onerous Light Actively explore social media (Yelp)Huge investments in data that banks and captives don’t think they can deploy
Legacy code
Lots of old SAS processes
No legacy processes
Technology “free for all”: R processes are daisy-chained with SAS, then connected to Python. “The IT nightmare”: 100% deployment flexibility
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Credit Risk Modeling for Alternative Lending
“It is not the model fitting techniques that need to change, as much as it is the treatment of samples, potential predictors, and model refits”.
The dependent variable can be redefined to something that is more frequently occurring and ultimately worth predicting.
Unstructured data can be structured as x’s and multiplied by betas to create expanded credit scores. For example: Yelp: # times the word “terrible” is mentioned Student lending: # times “broke” is in the same tweet as “student
loans”
Rapid refit and relearn processes are relatively easy to run routinely when data are appropriately structured.
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How Does Alternative Lending Change What We Do?
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Credit Risk Modeling for Alternative Lending
Thank you for your time.Follow-up questions and requests: [email protected]; [email protected] [email protected]
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
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