peer to peer lending analysis conclusions

NETWORK ECONOMIES

peer-to-peer lending Quantitative Analysis Review

Pattern RecognitionMIT Media Lab Class

January 16th 2009

contact: Ray [email protected]

Copyright 2009

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Synopsis of Research Results

• Predictive Accuracy

• Predictable with 80% accuracy Loan conversion and defaults

• Able to detect borrowers financial health by payment record

• Social Factors

• Increase odds of getting a loan when financial features are similar

• Evidence of preferential attachment with threshold number of bids

• Probable lender biases

• Demonstrated the textual information influences a loan

• Shows that images posted by borrower matter

OVERVIEW

http://www.media.mit.edu/

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Business Implications

• Tools may need to be provided

• For borrowers who need help to increase their odds of getting a loan

• For lenders to detect pending defaults.

• For P2P to manage risk

• Existing Social Networks should be exploited

• As loan volume increases the likelihood of similar financials becomes greater therefore the social aspects of assessing the quality of the borrower becomes more important.

• User may be reluctant to build a social network on a P2P site when they have already done so elsewhere

• Social networks may have a natural affinity for lending therefore increasing loan volumes.

OVERVIEW


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Recommendations

• Continue academic research with peer review publication for validation

• Complete a statistical profiling of full set of p2p data (not sampling)

• Use existing text, social, images, to determine qualitative voice of customer

• Analysis tools needed before having a secondary market of bundled loans

• Develop game model of p2p lending using info-economics theory

• Simulate social interaction networks impact on p2p lending model

• Test predictive model simulated against similar data samples

OVERVIEW


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Pattern Recognition Class

• MIT ML Professor Roz Picard Phd. teaching the course

• Teaching Assistant, Dawei Shen (Phd. candidate in Viral Communications)

• CFB Events invite Prosper.com and Virgin Money to present at MIT

• MIT Faculty and students take an interest in Peer to Peer Lending

• CFB (Ray Garcia) presents P2P Lending to the Pattern Recognition class.

• 7 researchers (students in class) interested in Peer to Peer Lending

• 2 Teams research P2P Lending using Pattern Recognition

INTRODUCTION


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Research Teams analyzing P2P Lending

• Lending Activity Team

• Coco Krumme

• Charlie DeTar

• Matt Aldrich

• Ernesto Martinez-Villalpando

INTRODUCTION

Social Capital Impact Team

• Sergio Herrero

• Rahul Bhattacharyya

• Aithne Sheng-Ying Pao


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Outline of Analysis

•Research Inquiry

•Review of prior P2P analysis using descriptive statistics

•Analytical results: classification, feature selection, neural networks

•Suggested tools / applications for borrowers, lenders, P2P vendor

•Conclusions

INTRODUCTION


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Analysis from several perspectives

• Borrowers: how to improve chances of getting a loan?

• Lenders: how to maximize returns by choosing the best loans?

• Social Interaction: what are the implications of the network relationships

• P2P Vendor:

• increase loan conversion,

• create tools to help borrowers and lenders

• identify loans before default

• P2P stakeholders & competitors:

• what borrower profiles are best served by P2P versus a traditional lender?

RESEARCH INQUIRY Analysis of P2P Lending Activity


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Data Used in the Analysis

P2P RESEARCH Analysis of P2P Lending Activity


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Descriptive Statistics

• Data from past 3 years: 340K listings, 29K loans

• Distribution of credit scores, loan status

P2P RESEARCH Analysis of P2P Lending ActivityP2P RESEARCH Analysis of P2P Lending Activity


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Descriptive Statistics

• Geographical distribution of members


Darker green indicates more members


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Previous Research Findings by Stanford GSB

• Research used Regression analysis of financial and social factors

• Group membership and endorsement increases loan funding significantly

• Credit Score and Verified Bank Account are financial factors most correlated with high funding rate

• Reference: http://www.prosper.com/Downloads/Research/Prosper_Regression_Project-Fundability_Study.pdf

CURRENT analysis is multi-factor,utilizes advanced feature selection,considers unequal prior probabilities

and a variety of data models



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Data reveals patterns

ANALYTICAL RESULTS Analysis of P2P Lending Activity


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Loan conversion/default predicted ~80% accuracy

• Predict loan conversion, default with ~80% accuracy (neural net not shown)



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Optimal feature set to predict conversion/default

• Identified 96 features (including text, social metrics)

• Ranked using floating feature selection

Top 8 features:

- Amount Delinquent - Open Credit Lines- Amount Requested - Borrower’s Max Rate - Credit Grade - Debt to Income Ratio - Funding Option - Endorsement



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The crucial 20%: human judgment & text analysis

• Human judgment is key in loan funding - factors such as description, image

• # prior bids counts (75% threshold): we want to fund already-supported listings



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Decision Tree Analysishelp borrowers get loans (increase loan conversion)

• Decision tree predicts loan conversions and defaults

• Borrower can control requested amount and interest rate

• Interactive tool to help borrower set optimal amount, rate

TOOLS Analysis of P2P Lending Activity


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Decision Tree to identifyprobability of loan defaulting (before lending)

• Before lending, use decision trees to identify risky borrowers

• Tool for default insurance, securitization of loans



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Identify loans pre-default using Hidden Markov Model

• 3-state “financial health” model

• Prosper could offer support to borrowers before default

• prediction error decreases with longer observation series



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Research Inquiry on Social Capital

•How social capital influence peer-to-peer lending?

• “Friends”: Direct relationship. intends to motivate other lenders to bid on second degree friends based on indirect trust.

• “Endorsements”: Feedback on previous transactions with other users.

• “Groups”: Users are allowed to form communities. Group members help each other and the group rating depends on their performance. Peer pressure

Analysis of P2P Social Impact


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Social versus Financial Components

• Group Leader Reward Rate

• Endorsement Number

• First Degree Friend Number

• Second Degree Friend Number

• Group Rating

• Group Size

• Borrower Maximum Rate

• Credit Grade

• Debt To Income Ratio

• Amount Requested

• Is Borrower Homeowner

Social Profile Financial Profile

Analysis of P2P Social ImpactFEATURE SELECTION


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Importance of Social Features

• Most important features

• Credit Grade

• 3 Bidding Forces

• Least important Social Capital features:

• Group Leader Reward Rate

• Group Size

• Number of first degree Friends

Analysis of P2P Social ImpactFEATURE SELECTION

3 bidding forces involving “social interaction” behavior• Bids from First Friends• Bids from Second Friends• Bids from Group Members


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Cluster Analysis of Bids



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Cluster Analysis of Group Rating



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Conclusions from Lending Activity Analysis1. Data is separable and has identifiable patterns

2. Non-obvious features do play a significant role

3. Factoring Human judgment is important consideration

Recommendation:Create useful tools for borrowers and lenders to help foster P2P activity.These tools should be based on decision tree and HMM models.Development of risk models should be explored using these techniques.

Team: Coco Krumme, Charlie deTar, Matt Aldrich, Ernesto Martinez-Villalpando

Analysis of P2P Lending Activity


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Conclusion from Social Impact Analysis

“Social features” do not replace “financial features”. But….they are the best complement for differentiation when

comparing similar financial profiles.

Users do not have time to maintain many social profiles

Recommendation:

P2P lending should use existing social networks as a foundation, instead of building their own.


Team: Aithne Sheng-Ying Pao, Sergio Herrero, Rahul Bhattacharyya


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Analysis Method

IntroductionDescriptivestatistics

graphical models

separation methods

feature selection

greedy

floating

PrincipalComponent

Analysis

NeuralNets

LinearDiscriminant

Analysis

experimental methods

SupportVector

Machine

Hidden Markov Model

loan performancegroup

performance

mechanical turk

decision trees

bayesian networks

SuggestedTOOLS

for Peer to Peer

Lending

loan or no loan?default or pay?



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Summary of Pattern Recognition Models Applied

Conclusions remain consistent across different Models• Descriptive Statistics

• Linear Regression

• Principal Component Analysis

• Support Vector Machine

• Artificial Neural Network

• Linear Discriminant Analysis

• K-Nearest Neighbor

• Fisher Algorithm

• Pudil’s Algorithm

• Bayesian Nets

• Decision Trees

• Hidden Markov Model

• Human Qualitative Classification



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References for Further Study

• MIT Pattern Recognition Course Information:

• http://courses.media.mit.edu/2008fall/mas622j/

• Complete MIT Pattern Recognition Study:

• http://courses.media.mit.edu/2008fall/mas622j/Projects/CharlieCocoErnestoMatt/#contents

• http://courses.media.mit.edu/2008fall/mas622j/Projects/SergioAithneRahul/SocialInteractionsInP2PLending.pdf

• Prior Research

• Prosper.com for information on p2p lending

• Stanford Business School regression analysis of prosper.com data

• http://www.prosper.com/Downloads/Research/Prosper_Regression_Project-Fundability_Study.pdf

• Stanford Podcast by Chris Larsen CEO of Prosper.comhttp://ecdev.stanford.edu/authorMaterialInfo.html?mid=1576

• Books:

• The Complete Guide To Prosper.com by Sean Bauer

• Happy About People-to-People Lending With Prosper.com by Roger Steciak

• Competitor list:

• VirginMoneyUS.com, Zopa.com, LendingClub.com, Loanio.com, Circlelending.com, FundMyNotes.com


peer to peer lending analysis conclusions

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