Forecasting peer to_peer_lending_risk

Forecasting

Peer-to-Peer

Lending Risk

Archange Giscard Destine

Steven Lerner

Erblin Mehmetaj

Hetal Shah

September 10, 2016

Forbes

Peer-to-Peer Lending

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• Investors and borrowers are linked by online service providers

Investors Borrowers

• Growing rapidly – $5.5B in the U.S. in 2014

– Over 100% annual growth rate today

– Expected to be a major player in consumer financing – over $150B by 2025

– Lending Club is the clear market leader

How Does It Work?

Borrowers

• Unsecured loan

• Rates often below credit cards

• Done online – quick and easy

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Investors

• Higher rates, from 4 to 25+%

• Ability to spread risk – invest as little as $25 per loan

Lending Club

• Collect ~ 5% fee up front

• Collect ~ 1% on all loan payments

• Pursue collections

But, roughly 14% of loans end in default and

All risk is assumed by the investor

Objectives

Current

Develop a tool to help investors avoid loans likely to default

A model to forecast probability of default, given loan information …

emphasize default recall versus precision

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Future Work For investors interested in taking more risk, develop a tool to determine effective interest rate

A model forecasting impact of default (x, fraction of loan value)

Effective interest rate (z) =

n√[(1+i)n - p*x] where i = original interest n = loan duration, yrs p = probability of default

12%

0% Over 36 quarters

Unemployment rate

Charge-off rate

What’s Different Than Prior Work

• Lending Club’s new historical data set increases modeling difficulty

• Other studies ignored macroeconomic features … which are important

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Unsecured Personal Loan Delinquencies,2Q16 Unemployment Rate and Charge Off Rate

1.3% 7.7% TransUnion

Data Selection

• Loan data on completed loans from the Lending Club website

• Macroeconomic data

6

Measure State Fed. Value Slope* Reflection of:

Unemployment X X X Job loss & replacement difficulty

GDP X X X Overall economic activity

Disposable income X X X Cost/wage pressure

10-yr to 3-m T-bill spread X X Future economic growth

3-yr T-bill rate X X Short term inflation

Credit card rate (average) X X Alternative borrowing costs

* Slope is for 12 months prior, based on expert input

Data Ingestion: Sources

• Loan data: Lending Club website – 111 features for each loan

– Historical data since June 2007

• Macroeconomic data – Federal Reserve

– Bureau of Economic Analysis

– Bureau of Labor Statistics

– Cardhub

– National Conference of State Legislatures

• Collected data stored in data archive (PostgreSQL DB)

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Data

Ingestion Wrangling Computation / Analysis Modeling

Reporting / Visualization

• Initial data reduction – 111 historical features 29 features provided to investors

– Date range reduction to completed loans

• Data verification and cleanup – Verify loan uniqueness

– Eliminate redundant data

– Eliminate non-informative features

(URL’s, free form, extremely sparse data, etc.)

– Trim entries: “months”, “%”, “+”, “years”, etc.

– Verify geographic scope

– Select uniform date structure for analysis and merging

– Address data that is both numeric and categorical

Data Wrangling … a big time consumer

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Data Ingestion Wrangling Computation / Analysis Modeling Reporting / Visualization

220K instances 111 features

• Address all NaN entries

• Analyze outliers

• Economic calculations

– Least square slopes

– Interpolating for quarterly and annual data

• Wrangle economic data: trimming entries and using consistent format

• Merge economic and loan data

Data Wrangling (cont’d)

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Categorical and

numerical wrangled

data frames

Surprise learning: LC only verifies data for 31% of loans!

Data Ingestion Wrangling Computation / Analysis Modeling Reporting / Visualization

84K instances 30 features - 21 loan - 9 economic

Data Analysis

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• Initial data analysis shows little separation based on features

• What separation there is, appears to be driven by macroeconomic variables

Data Ingestion Wrangling Data Analysis Modeling Reporting / Visualization

Paid

Default

Data Analysis (cont’d)

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Features initially deemed important, showed little differentiation


Default Paid Overlap

Modeling

• Tested several modeling algorithms – Logistical Regression

– Random Forest

– Naïve Bayes (Bernoulli, Gaussian, Multinomial)

– K-Nearest Neighbors

– Gradient Boosting

– Voting Classifier

• Manual feature exploration

• Created pipeline – Standardization

– Feature reduction via PCA and LDA

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Best recall was 0.58 to 0.62 …

was imbalanced data the issue?

Modeling (cont’d)

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-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Annual income

Feature importance for random forest


Feature importance for logistic regression

Annual income

Modeling (cont’d.)

• Balanced data set via undersampling paid loans – Little improvement

– Losing lots of instances

• Added hyper-parameter tuning using GridSearch … little improvement

• Balanced data via oversampling defaulted loans – Extracted representative data sample (85/15, paid/default)

– Multiply remaining defaults 6X

– Train model using 80/20 split

– Final test versus extracted (unseen) data

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De minimis improvements

Modeling (cont’d)

• Sought expert advice – Financial experts

– Modeling experts

• Adjusted feature set – More responsive economic input

• 36/60 month lagging slopes 12 month leading slopes

• 36/60 month averages point values

– Added critical ratios and indices to expand feature set

• Tested binary encoding

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De minimis improvements

Made a strategic decision to modify class weight to enhance default recall at the expense of default precision

Modeling: Metrics

Targeted 90+% default recall and 90+% paid precision

• Default recall Defaults identified / total defaults

• Paid precision Paids identified correctly / total instances identified as paid

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Modeling (cont’d)

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Logistic Regression Precision Recall F1 Score Support

Default (weight = 0.7) 0.52 0.94 0.67 13,568

Paid (weight = 0.3) 0.77 0.20 0.31 14,547

Unseen / Imbalanced Results

Default 0.16 0.97 0.20 115

Paid 0.97 0.18 0.30 734

Random Forest

Default (weight = 0.6) 0.53 0.92 0.68 13,568

Paid (weight = 0.4) 0.77 0.25 0.38 14,547

Unseen / Imbalanced Results

Default 0.16 0.95 0.28 115

Paid 0.97 0.24 0.39 734

What does

default recall = 0.97

and

default precision = 0.16

look like?


Reporting

• Tool (online) to predict loan status and probability of default – Investor enters loan info

– Tool fetches macroeconomic data

– Above data is passed to webservice, which executes model and returns predicted loan status and probability

• Tool developed using – Flask interface with machine learning model as a RESTful webservice

– Jinja2 template

– HTML/CSS

– Javascript

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Data Ingestion Wrangling Data Analysis Modeling Reporting

Demo

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Conclusions

• Model effectively sequesters loans likely to default (97% default recall)

• Model cherry-picks loans not likely to default (97% paid precision)

• Achieving the above required class weighting which drives default recall at the expense of default precision

… potentially good loans are misclassified as default

• Root causes appear to be lack of data separation, lack of feature relevancy and imbalanced data

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Future Work

Project specific

• Can we maintain recall and drive up precision by using logistic regression on the total dataset followed by random forest on potential defaults?

• Can we identify or create more relevant features?

• Can we develop a tool for aggressive investors, providing impact of default?

General opportunity space around highly imbalanced data

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Logistic Regression Random Forest

The authors would like to recognize the open source software that made this work possible

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Questions? Archange Giscard Destine [email protected] Steven Lerner [email protected]

Erblin Mehmetaj [email protected] Hetal Shah [email protected]

Date post:	21-Apr-2017
Category:	Economy & Finance
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Forecasting peer to_peer_lending_risk

Economy & Finance