The changing face of installment lending

Transcription

1 The changing face of installment lending Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc. Other product and company names mentioned herein are the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian.

2 Introducing: Sharat Shankar LendingPoint Allison Saffran Experian Crissy Wallace Experian

3 Agenda Market landscape Consumer profiling Take action Bringing it all together 3

4 Market landscape Sharat Shankar 4

5 Traditional Finance Digital finance eco-system is exploding Active Investors in Digital Finance Digital Finance Commercial Consumer Financial Technology 5

6 Significant high profile venture capital and PE activity in 2015 Date Target Industry Description Investor Investment 12/15* Online Consumer Finance $300 million 11/15 Online Consumer Finance $75 million 11/15 Online Consumer Finance $14.3 million 10/15 Online Consumer Finance $77 million 10/15 Online Commercial Finance $135 million 9/15 Online Consumer Finance $325 million 9/15 Online Consumer Finance- Marketplace Lending $17.5 million 8/15 Online Consumer Finance $1 billion 7/15 Specialty Renewable Energy Finance (Online, Instant Origination) 7/15 Online Consumer Finance- Marketplace Lending Undisclosed $35 million 5/15 Online Consumer Finance $275 million, undisclosed equity portion 4/15 Online Consumer Finance $400 million 4/15 Online Consumer Finance Marketplace Lending $165 million 3/15 Online Commercial Finance $80 million 1/15 Online Healthcare Patient Consumer Finance Undisclosed Source: Press Releases and Capital IQ. * Denotes pending transactions. 6

7 LendingPoint is seeing significant demand Up 54% Q Q

8 Consumer profiling: Who takes personal installment loans? Crissy Wallace 8

9 Analysis Originated in Q Archive file Dec 2015 Originated in Q Archive file Dec 2010 Originated in Q Archive file Dec

10 Consumer demographics Secured loan consumers Generation gap Average: 47 years old Marital status 21% 8% Silent Baby Boomer 37% Gen X 34% Millennial Academic collateral US Secured 29% 17% Gender bias? 59% 15% 26% 39% 27% 4% 30% 10

11 Consumer demographics Unsecured loan consumers Generation gap Average: 46 years old Marital status 7% 24% Silent Baby Boomer 35% Gen X 34% Millennial Academic collateral US Unsecured 29% 18% Gender bias? 53% 20% 27% 38% 35% 6% 21% 11

12 Consumer demographics Student loan consumers Generation gap Academic collateral 0% 11% Average: 32 years old Marital status Silent Baby Boomer 25% Gen X 64% Millennial US Student 29% 19% Gender bias? 38% 26% 36% 29% 47% 17% 7% 12

13 VantageScore 3.0 Model overview A = Super-prime B = Prime C =Near prime D = Sub-prime F = Deep sub-prime

14 Annual risk profile of borrowers VantageScore at origination year over year Bankcard Personal installment 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 40% 86% 50% 40% 30% 30% 62% 20% 20% 10% 10% 0% % A B C D F 14

15 Annual risk profile of borrowers VantageScore at origination limited to analyzed windows 100% 80% 60% 40% 20% 0% Bankcard Q Q Q % 100% 80% 60% 40% 20% 0% Personal unsecured Q Q Q % 100% Personal secured 100% Student 80% 80% 60% 60% 40% 40% 20% 0% Q Q Q % 20% 0% Q Q Q % A B C D F 15

16 Evolution of year one risk over time 12% Personal secured 10% 8% Since % +74% 7.4% +95% 6% 4% 5.1% 3.8% 5.6% 4.3% 4.8% +144% 2% 2.0% 2.5% 0% Originated Q Originated Q Originated Q DPD 90 DPD Charged off Average line 2014: $13,300 16

17 Evolution of year one risk over time 12% 10% 8% 10.1% 8.3% Personal unsecured 8.3% 6.8% Since % +8.6% 9.8% +17.3% 7.7% +61.5% 6% 4% 4.8% 3.9% 2% 0% Originated Q Originated Q Originated Q DPD 90 DPD Charged off Average line 2014: $8,400 17

18 VantageScore migration Origination vs. 12 months later % % % Personal Secured Personal Unsecured Student VS3 Q Origination VS3 as of Dec

19 Percent of file in each VS3 band Where are the bad accounts Q Originations Ever 60DPD in the first 12 months 70% 60% 50% 40% 60% 54% 30% 20% 10% 0% 21% 16% 18% 11% 5% 6% 6% 3% 0% 0% Secured Unsecured Super Prime Prime Near Prime Subprime Deep Subprime Missing 19

20 Lenders continuously look to re-up their existing customers balances 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 57.3% 42.7% Personal Secured Percent of renewed loans 69.8% 30.2% Personal Unsecured Percent non-renewed loans 20

21 Thousands Consumers claim to use personal loans to pay off existing debt mo average balance trend Average balance change from Sept 2014 Mar 2015 Retail + 10% Auto + 6% Student Loan +5% Home Equity 0% Sep-14 Mar-15 Bank/charge -7% 21

22 Imbalanced borrowing Personal loans and revolving debt Average Unsecured Loan: $11,895 Retail: $1,194 HELOC: $1,967 Bank/Charge: $4,183 TTL Revolving: $7,343 22

23 Take action Allison Saffran 23

24 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing 24

25 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing 6% of the file, 0.6% bad rate, 0% of bads 33% of the file, 1.95% bad rate, 6% of bads Typically VantageScore can easily differentiate the highest and lowest scoring population 4% of the file, 48% bad rate, 18% of bads 3% of the file, 19% bad rate, 6% of bads 25

26 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing Differentiation gets harder in the middle, that s where overlaying scores and Premiers through a chisquare can really assist lenders 28% of the file, 7% bad rate, 16% of bads Look at the income 14% <$50K 8.44% bad rate 14% >$50K 5% bad rate Income <$50,000 ever % trades 8.25% bad rate >75% trades 18% bad rate 26

27 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime 26% of the file, 23% bad rate, 54% of bads Deep subprime Missing 27

28 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing 26% of the file, 23% bad rate, 54% of bads Percent trades ever <25% 12% bad 28

29 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing 26% of the file, 23% bad rate, 54% of bads Percent trades ever <25% 12% bad 0 18 mo 19% bad 18+mo 9% bad Average life span of trades 29

30 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing 26% of the file, 23% bad rate, 54% of bads Percent trades ever % 16% bad 30

31 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing 26% of the file, 23% bad rate, 54% of bads Percent trades ever % 16% bad 0 36mo 18% bad 36+mo 13% bad Average life span of trades 31

32 Predicting risk Probability 60+DPD Super prime Prime Near prime Subprime Deep subprime Missing 26% of the file, 23% bad rate, 54% of bads Percent trades ever >50% 32% bad 32

33 Bringing it all together Allison Saffran 33

34 Summary Challenges More lenders are flooding the personal loan market Personal loan credit quality is deteriorating Opportunities Be CLEAR on target market and value proposition KNOW your customer Leverage enhanced SEGMENTATION STRATEGIES to manage risk Improve lending decisions by developing INDEPENDANT individual borrower and joint borrower scorecards 34

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