LendingClub - Data Driven NYC (27)

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May 20, 2014

PRESENTERS AT MAY’S EDITION OF DATA DRIVEN INCLUDED:

JEFF STEWART: @URGENTSPEED @LENDDOFRIEND

NAVEEN AGNIHOTRI: @NAVAGNI @LENDDOFRIEND

NOAH BRESLOW: @NOAHBRESLOW @ONDECKCAPITAL

ABHRA MITRA:@ONDECKCAPITAL

JAMES GUTIERREZ: INSIKT

RENAUD LAPLANCHE:@LENDINGCLUB

#DataDrivenNYC

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An Online Marketplace

Principal + Interest

Origination Fee Servicing Fee

Funding

All Loans originated and issued by WebBank, a FDIC insured Utah state bank.

InvestorsBorrowers

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Lower Intermediation Cost

Servicing

Origination

Underwriting

Customer Acquisition

Reserve Requirements

Branch Infrastructure

Technology drives cost

down

1. Operating expenses as a percentage of outstanding loan balance • 2. Estimated operating expenses on a “run rate” basis, assuming no growth in monthly rate of origination volumes

Lending ClubOperating Expense2: ~2%

Traditional LenderOperating Expense1: 5–7%

Servicing

Origination

Underwriting

Customer Acquisition

Reserve Requirements

Branch Infrastructure

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LC provides value to both borrowers & investors

0%2%4%6%8%

10%12%14%16%18%

Borrowers' RateInvestors' Rate

Traditional Bank Lenders

LendingClub

12.73%3

7.9%4

16.99%1

0.06%2

16.93%4.83%

1. Average consumer credit card rate for overall market as of May 15, 2014 (Source: indexcreditcards.com). 2. National average APY paid on savings accounts paid by U.S. depository institutions for non-jumbo deposits as of April 7, 2014 (Source: FDIC). 3. Average Interest Rate for 36-month public policy loans in Q1 2014. (Source: Lending Club). 4. Median Adjusted Net Annualized Return for investors with 100+ notes, note concentration of <2.5% of portfolio value, and portfolio age of 12-18 months (Source: Lending Club)

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Consistent controlled growth

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 0

100

200

300

400

500

600

700

800

900

$ M

illi

on

s

$791,348,200

Annual platform issuance run rate:

$3,165,392,800

Q1 2011 – Q1 2014 CAGR:

140%

Multiple ways credit marketplaces use data

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CreditMarketing Fraud Collections

And a variety of others…

Fraud detection is like finding the needle in a haystack

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A day of applications on Lending Club

Privileged and ConfidentialMarch 1, 2014Excludes 49 applications that the geolocator software was not able to place based on ZIP code and excludes Alaska and Hawaii from the viewCreated using Geocommons

Sunday, March 1, 2014Excludes 49 applications that the geolocator software was not able to place based on ZIP code and excludes Alaska and Hawaii from the viewCreated using Geocommons

A day of applications on Lending Club

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Average of days in March 2014

But there are predictors

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0100

0700

1900

1300

Fraud risk by time of day: colored by frequency of fraud attempts and sized by total number of applications

Introducing new data sources to the process

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Credit score

Bank account info

Tax information

Device

Online footprint

Application use

We are utilizing both data sets

Technology company consumer data

Typical bank consumer data

Leveraging device data to predict fraud risk

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PC Mobile0.0%

0.5%

1.0%

1.5%

0%

20%

40%

60%

80%

100%

80%

20%

PC vs Mobile

% of volume Fraud rate

% a

tte

mp

ted

fra

ud

ra

te

% o

f to

tal v

olu

me

Apple Android Other0.0%

0.5%

1.0%

1.5%

2.0%

0%

20%

40%

60%

80%

60%

38%

2%

Mobile OS

% of volume Fraud rate

% a

tte

mp

ted

fra

ud

ra

te

% o

f to

tal v

olu

me

From March 1, 2014 through March 31, 2014

Leveraging device data to predict fraud

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Android Windows 8 Windows 8.1

Apple iOS Mac OS X Windows 7 Windows XP

Windows Vista

0.0%

0.5%

1.0%

1.5%

2.0%

0%

10%

20%

30%

40%

50%

Fraud rate by OSfor OS with 1%+ volume

Volume Fraud rate

% a

tte

mp

ted

fra

ud

rat

e

% o

f to

tal v

olu

me

From March 1, 2014 through March 31, 2014

Leveraging device data to predict fraud

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Android Chrome Firefox Safari IE0.0%

0.5%

1.0%

1.5%

2.0%

0%

20%

40%

60%

Fraud rate by browserfor browsers with 1%+ volume

Volume Fraud rate

% a

tte

mp

ted

frau

d r

ate

% o

f to

tal v

olu

me

From March 1, 2014 through March 31, 2014

Using the data in new ways

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Higher risk of fraudLower risk of fraud

Info provided

Machine / Device

BehaviorInternet footprint

External sources

?

Info provided

Machine / Device

BehaviorInternet footprint

External sources

Example: Inconsistent Location Signals

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Self reported location

IP address locationSocial media presence location

We use machine learning to continually assess the best predictors of fraud

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Info provided

Machine / DeviceBehaviorInternet

footprintExternal sources

Which of the different potential pieces of information we could test are the best predictors

of fraud?

We use ~1,000 different attributes to assess fraud risk

Example: NLP + Performance Measurement

Privileged and ConfidentialAs of Oct. 2013

Job title / loan description score correlation with charge-off rate

Constant iteration of which free form data

fields factor in and how different words within

those fields are weighted in the score

RealtorSergeant

CEOScientist

biologist

Impact of Fraud Detection Efforts

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0%

1%

2%

3%

4%

5%Attempted fraud rate %

Attempted fraud rate is fraudulent loans that get listed that we then identify as fraud and don’t approve as a % of total listings