LendingClub - Data Driven NYC (27)
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Transcript of LendingClub - Data Driven NYC (27)
Privileged and Confidential
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
Privileged and Confidential 3
An Online Marketplace
Principal + Interest
Origination Fee Servicing Fee
Funding
All Loans originated and issued by WebBank, a FDIC insured Utah state bank.
InvestorsBorrowers
Privileged and Confidential 4
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
Privileged and Confidential 5
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)
Privileged and Confidential 6
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
Privileged and Confidential
CreditMarketing Fraud Collections
And a variety of others…
Fraud detection is like finding the needle in a haystack
Privileged and Confidential
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
Privileged and Confidential
Average of days in March 2014
But there are predictors
Privileged and Confidential
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
Privileged and Confidential
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
Privileged and Confidential
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
Privileged and Confidential
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
Privileged and Confidential
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
Privileged and Confidential
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
Privileged and Confidential
Self reported location
IP address locationSocial media presence location
We use machine learning to continually assess the best predictors of fraud
Privileged and Confidential
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
Privileged and Confidential
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