Stress Testing - Orchard Platform

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Stress Testing

Marketplace Lending in a Rapidly Changing Environment By David Snitkof, Larry Haertel Jr., Raymond Han and Michael Toth Orchard Platform

INTRODUCTION

CONTENTS Introduction

1

A Brief History of Marketplace Lending

4

Methodology

6

Stress Testing

9

Analysis of Historical Originations

12

Benchmarking Against Other Asset Classes

14

Management of Risk in Anticipation of Stress

16

Conclusion

20

In an era of Federal Reserve near-zero interest rate policy and with

The question that seems to be on everyone’s mind is, what happens

bond markets near historically low yields, marketplace lending

then? How might higher unemployment levels, falling home

has emerged as a new asset class with attractive returns and low

prices, and credit deterioration affect the returns of loan portfolios

volatility. Over the last five years, we’ve seen strong and consistent

originated under the marketplace model? How much stress would

returns from portfolios of marketplace loans. The Orchard

be needed to realize negative returns? What can we expect? In

Consumer Marketplace Lending Index presents a compelling

this paper, we will define a framework for stress testing, simulate a

picture of the potential returns of this new asset class, with 2015

range of outcomes, and provide expectations and guidance on the

returns that have clearly outperformed stalwarts such as the

expected returns of marketplace loans through a series of stressors.

S&P 500 and the Barclays Aggregate Bond Index. While falling unemployment rates and strong domestic growth have provided the ideal opportunity for this new asset class to flourish, we know that these favorable conditions cannot last forever.

105%

100%

95%

AGG: ISHARES BARCLAYS AGG. BOND FUND HYG: ISHARES HIGH YIELD CORP BOND ETF ORCHARD CONSUMER LENDING INDEX SPY: SPDR S&P 500 TRUST ETF

90% JAN 2015

FEB 2015

MAR 2015

APR 2015

MAY 2015

JUN 2015

JUL 2015

AUG 2015

SEP 2015

NOV 2015

INTRODUCTION

Two predominant business models prevail: non-bank balance-sheet

Fundamentally speaking, a marketplace-funded loan is no different structurally from a loan funded through other means. All other things being equal, a borrower making a loan payment wouldn’t care whether that loan is held on a lender’s balance sheet or if it is sold to a hedge fund, business development company, or family office. And yet, the question of how “marketplace” loans will perform in various economic environments persists. There are multiple reasons for this, which span the areas of industry dynamics, operational capacity, and consumer psychology.

A Brief History of Marketplace Lending

lenders and marketplace lenders. On the one hand, balance-sheet lenders behave a lot like traditional lenders, funding loans using their own balance sheets, but they use elements of novel technology to make faster and better credit decisions, offer a more seamless borrower experience, or integrate deeper with their customers’ financial lives. On the other hand, marketplace lenders use a digital marketplace to match borrowers and lenders, assuming little to no balance-sheet risk. Given that both models have merit and have experienced success, some platforms now employ a hybrid model. While borrowers created the need for these platforms, it has really been the investors - those funding the loans - who have propelled the industry forward. Marketplace lending, originally

First, the originators and servicers (often the same entity) engaged in

disadvantage as against an issuer with multiple product lines. For

this new world of marketplace lending are relatively new and may

instance, borrowers may be more reluctant to miss a payment on

not have the same track record or recession-hardened perspective

a loan from an institution where they have additional financial

held by more traditional lenders. Some industry participants may

products, such as a deposit account or credit card.

worry about how the underwriting, servicing, and collections practices of these newer entities would hold up in a challenging

Finally, it is necessary to consider the consequences of defaulting

environment, with increased pressure on their operational capacity.

on different loan obligations. With marketplace loans, the main

There is also some concern that originators chasing increasingly

consequence is credit score deterioration. For secured loans such as

ambitious growth targets would be hesitant to recalibrate risk

a mortgage or an auto loan, a default can result in the loss of the

policies in a way that would put that growth story in jeopardy.

underlying collateral. In the case of credit cards, the most common

In an environment with a higher rate of missed payments and

form of unsecured lending, although there is no pledged collateral,

financial difficulty for borrowers, organizations would also need

there is what can be thought of as an opportunity cost of default. If

to scale up their servicing and collections capabilities, which have

forced to choose between making a payment on a term loan or on

largely been built in a lower-stress environment and not subject to

a revolving line of credit, a stressed borrower may opt to keep the

major capacity challenges.

revolving line current in order to preserve future purchasing power.

Next, the new breed of marketplace lender tends to be a monoline

As marketplace lending continues to grow and encompass a greater

issuer, and the most prevalent product offered today is the fixed-rate

share of financial activity in the U.S. and around the world, these

amortizing term loan. Both of these attributes present challenges

questions will become increasingly important. More time and

to what is known as the payment hierarchy—the order in which

experience in diverse market conditions will provide more of the

a borrower decides to pay his or her debt obligations in a time of

data needed to answer them.

financial stress. A monoline issuer, in this case, is at a theoretical

3

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Marketplace lending has come to encompass a number of different

labeled “peer-to-peer”, or “P2P,” lending, consisted of groups of

business models, but at its most basic, it involves the investment

individual retail investors lending to individual borrowers. However,

of capital via data-driven online platforms to lend directly and

this symbiotic relationship has experienced a pronounced and

indirectly to borrowers. A marketplace-lending loan is generally

seemingly permanent shift as institutional investor interest in the

a short-duration, high-yielding, fixed-income instrument. The

space has ballooned in recent years. This trend may be attributed

marketplace-lending loans available today are predominantly

to to a variety of factors, including a low-interest-rate environment,

unsecured consumer loans and secured and unsecured small-

increased transparency and availability of data, and attractive risk-

business loans, though we are seeing growth across a wide array

adjusted returns.

of asset classes including student, mortgage, auto, and invoicefactoring loans.

Meanwhile, banks, which had for the most part dismissed marketplace lending as a transient movement, now view the industry

Marketplace lending largely began in the United Kingdom with

as scalable and enduring and are racing to leverage the technology

Zopa in 2005 and arrived in the United States shortly thereafter

of its leading platforms. One prominent example is the recent

with Prosper Marketplace and Lending Club. The idea behind

partnership between JPMorgan Chase & Co. and OnDeck Capital.1

these early platforms was novel: Reimagine the business of credit by

In other cases, banks such as Goldman Sachs are attempting to

cutting out the middlemen and letting individuals lend to each other

build their own technology-enabled consumer and small-business

through more efficient online markets, thus providing lower rates

lending platforms.2 Banks are also helping to facilitate leverage and

to borrowers than offered by traditional banks and better returns to

securitizations for some lending platforms, fueling further growth

investors. Many of these platforms emerged from the turmoil of the

for the industry. In addition, we recently witnessed a number of

2008 financial crisis, extending much needed funding for consumers

platforms execute successful IPOs, such as Lending Club and

and small businesses who had been almost completely shut off from

Ondeck, the former having conducted the fourth largest IPO in

traditional lending channels. Since then, a multitude of platforms

U.S. Internet history. Several other platforms have announced their

has sprouted up, offering a wide range of products and solutions to

intentions to issue public offerings and are currently in the queue

an even more diverse group of borrowers.

waiting for adverse market conditions to subside.

Stress Testing

4

METHODOLOGY

2005

MILESTONES

MARCH 2005 Zopa launches in U.K.

IN MARKETPLACE LENDING

Recently, regulators have expressed heightened interest in marketplace lending, as it has become an increasingly

FEB 2006 - Prosper launches in U.S. MAY 2006 - OnDeck launches in U.S.

meaningful part of the financial services landscape. Last summer, the U.S. Department of Treasury issued a

2007 AUG 2007 - Lending Club launches as Facebook app OCT 2007 - Dow Jones Industrial Average peaks

Request for Information seeking input from leading industry participants and stakeholders. Policymakers are using this information to study the growing number of business models and products

SEP 2008 - Lehman Brothers files for bankruptcy

offered by marketplace lenders, the potential for online marketplace lending

2009

to expand access to credit to historically MAR 2009 - Dow Jones Industrial Average bottoms out

underserved borrowers, and how the financial regulatory framework should

JUL 2010 - Dodd Frank signed into legislation AUG 2010 - Funding Circle launches small business lending in U.K. DEC 2010 - Prosper drops variable rate model

2011

evolve to support the safe growth of this burgeoning industry.3 As marketplace lending evolves, its potential to transform the global

NOV 2012 - Lending Club surpassed $1B in originations DEC 2012 - Avant Launches

allocation of capital has captured the imagination of an industry. Technology and data are rapidly changing the way lending is done in the U.S. and around the world. While marketplace lending still only accounts for a narrow portion of the

2013 SEP 2013 - JOBS Act

OCT 2013 - Funding Circle launches in U.S. joining forces w/ Endurance Lending Network

DEC 2013 - Orchard Platform launches

S&P and “A2” from Moody’s

/ Eaglewood Capital deliver first p2p securitization

DEC 2014 - Lending Club IPO raises $1B

2015 DEC 2014 - OnDeck MAY 2015 - Avant reaches $1B in loan originations since launching in January 2013

5

insignificant. The future of marketplace be exciting to see what comes next.

NOV 2014 - SoFi achieves ‘A” rating from

OCT 2013 - Lending Club

IPO raises $320M

global credit market, its progress is not lending is sure to be very bright and it will

signed into legislation

Methodology

1. Son, H. “JPMorgan Working With On Deck to Speed SmallBusiness Loans.” Bloomberg, News, Web. 1 Dec. 2015 2. Corkery, M. and Popper, N. “Goldman Sachs Plans to Offer Consumer Loans Online, Adopting Start-Ups’ Tactics.” New York Times, Deal Book, Web. 15 Jun. 2015

To analyze the effects of market stresses on a loan portfolio, it is important to decide upon an agreeable model for projecting future cash flows on loans.

We decided to turn to the standard prepayment-default-recovery model that has been in use for many years in the mortgage and asset-backed securities markets. Next, we established a base case for expected cash flows in a non-stressed scenario. Finally, we stressed our default assumptions in order to get a sense for what we can expect in a stressed scenario. Our cash-flow model has three key inputs: CPR (Constant Prepayment Rate), CPR =1-(1- Prepayment Amount / (Beginning Balance-Default Amount - Scheduled Principal))^12 CDR (Constant Default Rate CDR =1-(1- Default Amount / Beginning Balance)^12 Recovery rate (Rec = Recoveries / Default Amount) Each of these input assumptions plays a role in the calculation of monthly cash flows. At the beginning of each projected payment month, a percentage of the pool (corresponding to the supplied CDR) defaults. After that, all survivors in the pool make their scheduled monthly payments. Next, a percentage of the remaining survivors (corresponding to the supplied CPR) prepay their loans in full. Finally, we also receive recovery amounts equal to the supplied Recovery Rate times the dollar amount of defaults this period. We repeat the above process until every loan in our portfolio has fully matured.

3. Treasury Seeks Public Comments on Marketplace Lenders.” U.S Department of Treasury, Press Center, Web. 16 Jul. 2015

Orchard Platform

Stress Testing

6

METHODOLOGY

1. Beginning Balance = $10,000

For our base-case CPR

flows for one month on a hypothetical $10,000 loan pool with a weighted average interest rate of 16% and a remaining term of 36 months. For this example, we’ll use 5% CDR, 15% CPR, 3% Recovery. In summary, under these assumptions, in this

2. CDR = .05 = 1 − 1 − �



3. .05 = 1 − 1 − �



Beginning Balance

Default Amount 10,000

$124.47 in interest payments, $351.68 in principal payments ($124.47 scheduled principal and $220.71 prepaid principal), and $1.28 in recoveries.

we assume a constant 14.5%



based on historical loan data across the consumer unsecured space. Orchard Curves attempt to capture the observed loan seasoning effect whereby charge-offs in static pools ramp

Looking historically, the

4. Default Amount = $42.65

model over historical realized charge-offs

5. Scheduled Payment = Balance *

much less pronounced than it

Interest Rate * (1+Interest Rate)Term

is for CDR, and defaults have

(1+Interest Rate)Term− 1

6. Scheduled Payment = (10000 − 42.65) *

in the consumer lending space from 2010 to the present. After the initial fitting of the curves, they are scaled to align with recent performance. (The detailed mathematics behind the generation of these curves are beyond the scope of this paper but may be discussed further in future research products.)

7

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15%

17.5%

7.5%

a significantly larger effect

.16 .16 36 (1 + ) 12  *      12

(1 +



10%

on portfolio yield relative to

.16 36     12 ) -1

5%

prepayments. For Recovery rate, we use the

0

observed average of 3%.

4

8

12

16

9. Scheduled Interest = (10000 −42.65) *

.16 12

= $124.47

20

24

28

32

36

Months on Book

8. Scheduled Interest = (Beginning Balance − Default Amount) * Interest Rate 60 Months Loans Model CDR Interest Rates

10. Scheduled Principal = Scheduled Payment − Scheduled Interest

12.5%

>= 20%

= 20%

over our analysis period.

For our base-case CDR (charge-off) curves (hence referred to as Orchard Curves)

< 7.5%

20%

observed long-term average



7. Scheduled Payment = $345.17 expectations, we have generated charge-off

Interest Rates

CPR for simplicity. This is the





month, $42.65 of our portfolio would have charged off, and we would have received

12

(prepayment) expectations,

12

Default Amount

36 Months Loans Model CDR

CDR

Let’s look at an example of how to project cash

10%



14. Prepay Amount = $130.98 15. Recovery Amount = Recovery * Default Bal 16. Recovery Amount = .03 * 42.65 = $1.28

5%

0 4

8

12

16

20

24

28

32

36

40

44

48

52

56

60

Months on Book

Stress Testing

8

STRESS TESTING

Stress Testing Now that our model and default assumptions have been established, we will combine

With our base case assumptions, our static market portfolio is expected to realize a 6.7%

them and generate a set of hypothetical base-case future cash flows. The portfolio

yield, a duration of .88 years and a weighted average life of 1.62 years. All calculations

of loans for which we are projecting cash flows comprises all loans in our data set

above are based on a purchase price of par (or face value). What happens if, over the life

originated in Q3 2015. These cash flows will then be analyzed to develop a set of static

of the portfolio, the realized defaults are double the expected defaults? In that case, our

pool performance metrics. The quarterly cash flows are as follows:

cash flows will be as follows:

Period

BegBalance

ChargeOffs

Recoveries

Interest

Serv_Fee

Sched_Principal Unsched_Principal EndBalance

Period

BegBalance

ChargeOffs

Recoveries

Interest

Serv_Fee

Sched_Principal Unsched_Principal EndBalance

1

$1,000,000.00

$-

$-

$31,792.71

$2,422.81

$54,729.49

$36,997.70

$908,272.82

1

$1,000,000.00

$-

$-

$31,792.71

$2,422.81

$54,729.49

$36,997.70

$908,272.82

2

$908,272.82

$4,124.25

$123.73

$28,847.67

$2,190.79

$54,116.99

$33,394.66

$816,636.92

2

$908,272.82

$8,226.61

$246.80

$28,746.10

$2,184.76

$53,984.91

$33,302.51

$812,758.79

3

$816,636.92

$7,450.10

$223.50

$25,870.47

$1,959.62

$53,268.73

$29,807.89

$726,110.21

3

$812,758.79

$14,704.66

$441.14

$25,523.09

$1,938.85

$52,765.03

$29,491.07

$715,798.02

4

$726,110.21

$9,689.86

$290.70

$22,931.62

$1,733.16

$52,210.65

$26,297.05

$637,912.65

4

$715,798.02

$18,806.00

$564.18

$22,277.80

$1,693.64

$51,136.19

$25,695.82

$620,160.02

5

$637,912.65

$10,878.75

$326.36

$20,082.90

$1,514.36

$50,968.27

$22,907.76

$553,157.87

5

$620,160.02

$20,661.36

$619.84

$19,123.99

$1,455.74

$49,167.59

$22,018.83

$528,312.24

6

$553,157.87

$11,131.10

$333.93

$17,366.33

$1,305.75

$49,578.51

$19,679.08

$472,769.18

6

$528,312.24

$20,614.30

$618.43

$16,149.89

$1,230.64

$46,949.62

$18,544.19

$442,204.13

7

$472,769.18

$10,613.37

$318.40

$14,812.78

$1,109.31

$48,086.67

$16,641.19

$397,427.96

7

$442,204.13

$19,116.84

$573.51

$13,415.41

$1,022.32

$44,583.65

$15,332.74

$363,170.90

8

$397,427.96

$9,521.69

$285.65

$12,441.78

$926.37

$46,543.75

$13,813.95

$327,548.57

8

$363,170.90

$16,651.21

$499.54

$10,953.42

$833.21

$42,173.72

$12,420.69

$291,925.27

9

$327,548.57

$8,063.90

$241.92

$10,261.94

$757.55

$45,004.08

$11,206.56

$263,274.03

9

$291,925.27

$13,676.07

$410.28

$8,773.31

$664.20

$39,820.36

$9,820.88

$228,607.96

10

$263,274.03

$6,446.23

$193.39

$8,272.05

$602.83

$43,523.63

$8,817.75

$204,486.42

10

$228,607.96

$10,594.40

$317.83

$6,865.58

$514.90

$37,616.90

$7,525.76

$172,870.91

11

$204,486.42

$4,865.08

$145.95

$6,462.17

$461.54

$42,159.05

$6,636.45

$150,825.84

11

$172,870.91

$7,741.45

$232.24

$5,206.44

$383.80

$35,648.67

$5,510.91

$123,969.88

12

$150,825.84

$3,504.93

$105.15

$4,814.87

$332.44

$40,967.81

$4,642.60

$101,710.51

12

$123,969.88

$5,391.32

$161.74

$3,761.69

$268.54

$33,995.21

$3,738.52

$80,844.83

13

$101,710.51

$2,597.83

$77.93

$3,574.35

$237.71

$10,777.92

$3,559.88

$84,774.88

13

$80,844.83

$3,865.96

$115.98

$2,702.77

$185.92

$8,467.13

$2,783.81

$65,727.92

14

$84,774.88

$2,049.04

$61.47

$2,957.81

$197.09

$10,479.17

$2,931.44

$69,315.22

14

$65,727.92

$2,966.56

$89.00

$2,177.49

$150.49

$8,034.24

$2,237.96

$52,489.16

15

$69,315.22

$1,553.99

$46.62

$2,397.28

$159.99

$10,203.73

$2,357.74

$55,199.77

15

$52,489.16

$2,193.37

$65.80

$1,721.23

$119.45

$7,645.15

$1,759.88

$40,890.76

16

$55,199.77

$1,120.89

$33.63

$1,886.94

$126.10

$9,953.94

$1,833.43

$42,291.51

16

$40,890.76

$1,545.98

$46.38

$1,324.05

$92.21

$7,300.48

$1,340.41

$30,703.89

17

$42,291.51

$754.48

$22.63

$1,420.96

$95.05

$9,731.72

$1,353.08

$30,452.22

17

$30,703.89

$1,019.38

$30.58

$976.69

$68.22

$7,000.27

$970.89

$21,713.35

18

$30,452.22

$456.99

$13.71

$993.57

$66.50

$9,538.64

$911.27

$19,545.32

18

$21,713.35

$606.38

$18.19

$670.70

$46.95

$6,744.23

$643.23

$13,719.51

19

$19,545.32

$229.03

$6.87

$599.17

$40.11

$9,376.02

$502.70

$9,437.57

19

$13,719.51

$299.18

$8.98

$398.38

$27.93

$6,532.04

$349.99

$6,538.31

20

$9,437.57

$70.27

$2.11

$232.41

$15.56

$9,245.06

$122.24

$-

20

$6,538.31

$90.57

$2.72

$152.72

$10.72

$6,363.47

$84.27

$-

9

Orchard Platform

Stress Testing

10

STRESS TESTING

Charge-Off Rate Charge-Off Rate

positive at 1.01%. In fact,

10% 10%

rates in order for a loss to be realized. For reference, default rates on credit card receivables peaked in Q2 2010 at 10.97%, which is 2.23 times the average credit card default rate throughout 2000–2007 (4.92%).

Charge-Off Rate On Credit Card Loans Charge-Off Rate On Credit Card Loans

realized defaults would have

Of Historical Originations

8% 8%

6% 6%

In the graph to the right, we

4% 4%

rates of marketplace loans by

2% 2%

annual vintage. We see that 2006, 2007, and 2008 vintages

00

Below is a yield table of

1986 1986 1988 1988 1990 1990 1992 1992 1994 1994 1996 1996 1998 1998 2000 2000 2002 2002 2004 2004 2006 2006 2008 2008 2010 2010 2012 2012 2014 2014

CDR Multipliers vs. CPR

Observation Dates Observation Dates

all experienced substantially higher charge-off rates than

and the resulting yields

those in recent years, which

of each scenario. Red

are clustered together near the

highlighted scenarios represent negative yield.

CPR

1

1.25

1.5

1.75

2

2.25

2.5

2.75

3

12

6.63%

5.21%

3.82%

2.45%

1.11%

-0.21%

-1.51%

-2.79%

-4.04%

12.5

6.64%

5.23%

3.84%

2.48%

1.14%

-0.17%

-1.47%

-2.74%

-3.99%

13

6.66%

5.25%

3.87%

2.51%

1.17%

-0.14%

-1.43%

-2.70%

-3.94%

13.5

6.68%

5.27%

3.89%

2.54%

1.21%

-0.10%

-1.39%

-2.65%

-3.89%

14

4. Source: https://research.stlouisfed.org/ fred2/series/CORCCACBS

Cumulative Rate of Charge-Offs by Vintage for 36 Month Loans 30%

plot the cumulative charge-off

6.69%

5.29%

3.92%

2.57%

1.24%

-0.06%

-1.35%

-2.61%

-3.85%

14.5

6.71%

5.31%

3.94%

2.60%

1.27%

-0.03%

-1.30%

-2.56%

-3.80%

15

6.72%

5.33%

3.97%

2.63%

1.31%

0.01%

-1.26%

-2.51%

-3.75%

15.5

6.74%

5.35%

3.99%

2.66%

1.34%

0.05%

-1.22%

-2.47%

-3.70%

16

6.76%

5.38%

4.02%

2.69%

1.38%

0.09%

-1.18%

-2.42%

-3.65%

16.5

6.77%

5.40%

4.04%

2.71%

1.41%

0.13%

-1.14%

-2.38%

-3.60%

17

6.79%

5.42%

4.07%

2.74%

1.44%

0.16%

-1.09%

-2.33%

-3.55%

17.5

6.81%

5.44%

4.09%

2.77%

1.48%

0.20%

-1.05%

-2.28%

-3.50%

18

6.82%

5.46%

4.12%

2.80%

1.51%

0.24%

-1.01%

-2.24%

-3.45%

bottom of the graph. To provide additional detail on

% of%Vintage of Vintage Charged Charged Off by OffBalance by Balance

undoubtedly lower but still

to be 2.2 times projected

Analysis

Charge-Off Rate On Credit Card Loans, All Commercial Banks, January 1985 to October 2015 4

30%

2006 Vintage

25%

2007 2006 2008 2007

25%

2009 2008

20%

2010 2009

20%

2011 2010

15%

2012 2011

15%

2013 2012

10%

2014 2013

10%

2015 2014

5%

2015

5% 0

vintages, we’ve also created the

4

8

12

16

4

8

12

Months Since 16 20 Origination 24

20

24

28

32

36

28

32

36

Months Since Origination

chart below, which removes the 2006–2008 data. Here, we

Vintage

0

charge-off performance in later

Cumulative Rate of Charge-Offs by Vintage for 36 Month Loans, 2009 - Present

see steady improvement, with

10%

Vintage

vintages 2013–2015 showing

10%

2009 Vintage

the lowest cumulative charge-off rates to-date of all vintages.

% of%Vintage of Vintage Charged Charged Off by OffBalance by Balance

Our yield in this scenario is

ANALYSIS OF HISTORICAL ORIGINATIONS

2010 2009 2011 2010

7.5%

2012 2011

7.5%

2013 2012 2014 2013

5.0%

2015 2014

5.0%

2015

2.5% 2.5% 0 0

4

8

12

16

4

8

12

Months Since 16 20 Origination 24

20

24

28

32

36

28

32

36

Months Since Origination 11

Orchard Platform

Stress Testing

12

ANALYSIS OF HISTORICAL ORIGINATIONS

BENCHMARKING AGAINST OTHER ASSET CLASSES

The table below demonstrates the changes in borrower characteristics for this asset class across

What explains this performance? In particular, why

the past 10 years. In the first few years, we saw yearly originations of less than $100M, with

do 2006–2008 vintages perform so much more

average interest rates in the 16%–18% range and average borrower income of approximately

poorly than more recent ones?

$70K. We also saw small average loan sizes, ranging from $4K–$7K. In more recent years, we see significantly lower average interest rates and much higher average borrower incomes and average loan sizes. It’s worth mentioning that the income growth we are seeing is real; even after

One possible explanation is the financial crisis

accounting for wage inflation, we notice a material increase in average incomes over time.

in 2007–2008. During this period, performance

Vintage

Avg_loan_amount Avg_term

Avg_rate

Avg_income

Avg_dti

industry, which is why we should also expect to see

2006

$4,780.55

36 Mos

17.86%

$72,682.73

20.58%

2007

$7,040.29

36 Mos

16.40%

$72,733.35

25.40%

2008

$6,433.61

36 Mos

15.80%

$71,565.46

22.63%

deteriorated across nearly all sectors of the credit

from marketplace originators. While the credit crisis was clearly responsible for some of the performance differences we

$8,457.04

36 Mos

13.49%

$79,646.07

14.21%

experienced, it does not tell the full story. From

2010

$8,618.07

42.25 Mos

13.46%

$79,461.21

14.90%

2006–2008, marketplace lending was a nascent

2011

$10,305.95

46.00 Mos

14.71%

$81,124.71

16.10%

$12,115.69

43.53 Mos

15.33%

$80,677.49

18.22%

2013

$13,952.39

45.00 Mos

15.09%

$84,183.12

18.76%

2014

$14,308.12

45.99 Mos

14.09%

$87,976.76

20.83%

2015

$14,829.90

45.98 Mos

13.16%

$87,252.15

22.55%

Against Other Asset Classes

higher levels of non-performance among loans

2009

2012

Benchmarking

industry. Total originations in this period were $204M. When compared with originations of over $5B in 2014 alone, we see what a small sample size these first few years represent. The originators were just getting started and had not fully

In the graph below, we show a performance comparison of the

indices become tradable assets, we should expect to see higher

Orchard Consumer Lending Index vs. three market exchange-

levels of volatility in periodic returns, but we would expect similar

traded funds (ETFs), the S&P 500, the Barclays Aggregate Bond

cumulative returns.

Index, and an iShares High Yield Corporates Index. The graph shows the evolution of the portfolio value of an investment begun

The graph shows that marketplace assets have outperformed

on January 1, 2011 in each of these assets. Note that while the

traditional bond investments over this time period, but

Orchard Consumer Lending Index tracks the performance of an

underperformed the S&P equity index. While the above graph

investment in marketplace loans, it is not an investable index like

demonstrates the cumulative performance of asset returns over

the other three represented, and therefore it shows significantly

time, the final result is really a snapshot of one date, which could

lower daily volatility than those assets. Once marketplace lending

be considered somewhat arbitrary.

2.00

developed their underwriting criteria. They were targeting comparatively lower-quality borrowers at higher interest rates. All of these factors would

Comparitive Performance of Marketplace Lending and Traditional Investment Portfolios

also lead to higher charge-off levels as compared to

175%

recent years. Above, we present some stratifications

AGG: iShares Barclays Agg. Bond Fund

across key credit variables by year to demonstrate

HYG: iShares High Yield Corp Bond ETF

the difference in populations receiving loans during

150%

Orchard Consumer Lending Index SPY: SPDR S&P 500 Trust ETF

Portfolio Value

these different periods.

TICKERS

125%

100%

75% Jan 2011

Jan 2012

Jan 2013

Jan 2014

Jan 2015

Date

13

Orchard Platform

Stress Testing

14

75% Jan 2012

Portfolio Value

Portfolio

100% BENCHMARKING AGAINST OTHER ASSET CLASSES

125%

MANAGEMENT OF RISK IN ANTICIPATION OF STRESS

100% 75%

Jan 2013

Jan 2014

Jan 2015

Jan ‘13

Jul ‘13

Jan ‘14

Jul ‘14

Jan ‘15

Jul ‘15

Instead of the 2011–2015 period Date shown, we might consider the

Datein this case the Orchard For 2014, we see a similar result, although

yearly performance for a series of simulated investments made at the

Consumer Lending Index almost matches the returns of the S&P

beginning of each year from 2012 through 2015, with positions held

500. For investments beginning in January 2015, the Orchard

through 175% the end of 2015. For investments beginning in 2012 and

Consumer 175%Lending Index is the best performing of the four, given

2013, we see a result similar to what was observed above: The S&P 150%

the market turmoil experienced by the S&P over that period. As 150%

shows the best performance, although with higher volatility, and 125%

Portfolio Value

Portfolio

125%

mentioned above, some of the consistency in marketplace lending 125%

the Orchard Consumer Lending Index shows steady appreciation,

performance is because it is not a tradable index and therefore is not

ahead of both the Barclays Aggregate and the iShares High Yield

subject to the daily volatility experienced by the other investments.

100%

75% Corporate Bond ETF. Jan ‘14

Jul ‘14

100% 75%

Jan ‘15

Jul ‘15

Jan ‘15

Jul ‘15

Jan ‘15

Apr ‘15

Date

Jul ‘15

Oct ‘15

Date

Comparative Performance of Marketplace Lending and Traditional Investment Portfolios TICKERS

AGG: iShares Barclays Agg. Bond Fund

HYG: iShares High Yield Corp Bond ETF

Orchard Consumer Lending Index

SPY: SPDR S&P 500 Trust ETF

150%150% 150%150%

150%150% 150%150%

125%125% 125%125% 100%100% 100%100% 75% 75% 75% 75% Jan 2012 Jan 2012 Jan 2013 Jan 2013 Jan 2014 Jan 2014 Jan 2015 Jan 2015 Jan 2012 Jan 2012 Jan 2013 Jan 2013 Jan 2014 Jan 2014 Jan 2015 Jan 2015 Date Date Date Date

125%125% 125%125% 100%100% 100%100% 75% 75% 75% 75% Jan ‘13 Jan ‘14 Jan ‘15 Jan ‘13 Jul ‘13Jul ‘13 Jan ‘14 Jul ‘14Jul ‘14 Jan ‘15 Jul ‘15Jul ‘15 Jan ‘13 Jan ‘13 Jul ‘13Jul ‘13 Jan ‘14 Jan ‘14 Jul ‘14Jul ‘14 Jan ‘15 Jan ‘15 Jul ‘15Jul ‘15 Date Date Date Date

2015

Investors rely on their attitudes about the future to make decisions

which is relatively simple for institutional investors to achieve in a

on where to deploy capital, what level of risk to take, and how

short period of time given that the average loan size is just north

to diversify their portfolios. Originators make judgments about

of $11K. Orchard quantitative research has shown that a portfolio

the future when they develop new products, invest in customer

of at least 200 loans is fairly well diversified. Of course, this type

150%150% 150%150%

150%150% 150%150%

acquisition, and calibrate their underwriting criteria to approve

of diversification mainly protects an investor from variance in

or decline borrowers. While it is impossible to predict exactly

borrower credit risk, limiting the impact of a small number of

125%125% 125%125%

what the future will bring, we know that good times do not

loans on the investor’s overall return. In order to solve for other

100%100% 100%100%

last indefinitely, and it is prudent to discuss what marketplace

forms of risk, investors may wish to diversify in other ways as well.

participants might do to mitigate risk in the event of market stress.

125%125% 125%125% 100%100% 100%100% 75% 75% 75% 75% Jan ‘14 Jan ‘14 Jul ‘14Jul ‘14 Jan ‘15 Jan ‘15 Jul ‘15Jul ‘15 Jan ‘15 Jan ‘15 Jul ‘15Jul ‘15 Jan ‘14 Jul ‘14Jul ‘14 Jan ‘15 Jul ‘15Jul ‘15 Jan ‘15 Jul ‘15Jul ‘15 Jan ‘14 Jan ‘15 Jan ‘15 Date Date Date Date

15

Orchard Platform

Portfolio Portfolio Value Value

175%175% 175%175% Portfolio Value Portfolio Value

175%175% 175%175% Portfolio Portfolio Value Value

Portfolio Value Portfolio Value

2014 - 2015

Portfolio Portfolio Value Value

175%175% 175%175% Portfolio Value Portfolio Value

175%175% 175%175% Portfolio Portfolio Value Value

Portfolio Value Portfolio Value

TICKERS TICKERS AGG: iShares AGG: iShares Barclays Barclays Agg. Bond Agg.Fund Bond FundHYG: iShares HYG: iShares High Yield HighCorp YieldBond CorpETF Bond ETFOrchard Orchard Consumer Consumer LendingLending Index IndexSPY: SPDR SPY:S&P SPDR 500 S&P Trust 500ETF Trust ETF TICKERS TICKERS AGG: iShares AGG: iShares Barclays Barclays Agg. Bond Agg.Fund Bond FundHYG: iShares HYG: iShares High Yield HighCorp YieldBond CorpETF Bond 2013 ETFOrchard Orchard Consumer Consumer LendingLending Index IndexSPY: SPDR SPY:S&P SPDR 500 S&P Trust 500ETF Trust ETF 2012 - 2015 - 2015

Management Of Risk In Anticipation Of Stress

Throughout this paper, we have employed a scenario-based approach to stress testing. Rather than predicting the future, we attempt to understand the impact that various versions of a possible future would have on a marketplace lending portfolio. Indeed, attempting to predict the future can be seen as a fool’s errand: It’s impossible to be perfectly right; the question is the magnitude of difference between prediction and reality.

75% 75% 75% 75% Jan ‘15 Jan ‘15 Apr ‘15 Apr ‘15 Jul ‘15Jul ‘15 Oct ‘15 Oct ‘15 Jan ‘15 Apr ‘15 Apr ‘15 Jul ‘15Jul ‘15 Oct ‘15 Oct ‘15 Jan ‘15 Date Date Date Date

As in other asset classes, diversification is a vital component of any risk-mitigation approach. The primary way to diversify is to spread investments across a sufficiently large number of loans,

Stress Testing

16

MANAGEMENT OF RISK IN ANTICIPATION OF STRESS

Another key area of risk is that of the originator and servicer,

standards have been developed to handle cross-border transactions,

securitization, representing approximately $8B of transactions

often the same entity. While the leading originators in marketplace

and withholding-tax regimes make overseas investing unattractive in

over the past two years. While these deals are generally seen as a

New

lending have posted track records of high-quality underwriting

some jurisdictions.

positive development for the market and a beneficial component

Guard

and servicing, there are still risks to consider, such as originator

of investors’ financing strategy, they also have drawbacks,

bankruptcy, fraud, or technical failure. The practice of designating

Finally, diversification can also mean the inclusion of marketplace

including a size, cost, and complexity that make them available

a backup servicer is widespread and now considered essential,

loans as one asset in a broader portfolio. Even if conditions are

to only the largest of managers. A more nimble and transparent

but to date there has not been a major failure event that would

predicted to worsen, it may be difficult to get the timing of the

market for these loans would increase liquidity for a variety of

test the ability of a backup servicer to step in and take over from

deterioration right. If performance deteriorates more slowly than

asset owners.

the primary servicer. Even in a relatively seamless transition,

expected, there is an opportunity cost from not holding marketplace

there would likely be some disruption to borrower payments

loans in a portfolio. Furthermore, as a short-duration asset, if

Planning for a stressed environment and mitigating the risk of a

and cash flows to investors. Investors who diversify their market

performance deteriorates beyond what is expected, an investor can

downturn is not a task for investors alone. Indeed, the originators

participation across multiple originators—particularly among

recover principal through normal amortization, even in the absence

have a strong role to play in creating and nurturing a stable

those of different asset classes, borrower types, and loan product

of immediate liquidity.

financial ecosystem and a strong incentive to pursue responsible

durations—will be in a stronger position and less vulnerable to originator-related volatility.

DEVELOPING A ROBUST SET OF NEW ACCOUNT CRITERIA ALONG WITH A PREDICTIVE AND STABLE CREDIT RISK MODEL TO BE USED IN UNDERWRITING ARE THE MOST IMPORTANT THINGS AN ORIGINATOR NEEDS TO DO. In addition to investing in a large number of loans across multiple originators, investors can also diversify by spreading their exposure to macroeconomic factors. This could include investing in heterogeneous geographic areas, which would likely respond differently to trends in employment levels or home prices. Investing across geographic areas could involve spreading capital across multiple countries and currencies, though this has been quite difficult in marketplace lending thus far. No real international

growth. A core part of any originator’s business is deciding Some investors may wish to mitigate their marketplace lending

which customers to approve for loans. Developing a robust set of

exposure through hedging—executing transactions in other

new account criteria along with a predictive and stable credit risk

assets whose performance would be orthogonal to that of the

model to be used in underwriting are the most important things

marketplace portfolio. However, unlike in more mature asset

an originator needs to do. In this area, marketplace lenders

classes, there are neither out-of-the-box products nor the market

are in an interesting position relative to their more established

mechanics or legal framework to provide a true hedge. For

counterparts in the banking world, in terms of both challenge

instance, there is no way to “short” a marketplace asset, and there

and opportunity.

are no widely-available credit default swaps or similar derivatives

Traditional vs. New Guard in Times of Stress

Historical Data



Advantage: Traditional

Lending is not a new business, and experienced lenders have many years worth of data across multiple credit cycles.

Experience



Advantage: Traditional

Experience in multiple economic environments is valuable in times of stress.

System Flexibility

Advantage: New Guard



Systems built recently with flexible and modern technology are more easily modified and can handle a greater breadth of risk models and dataintensive strategies. Legacy systems at traditional financial institutions often slow a lender’s ability to adapt to changing conditions.

Data Science

Advantage: New Guard



Newer online lenders generally have the

that would pay off in a default scenario. Some investors in today’s

As marketplace lenders contemplate how to develop strategies

market have employed relatively primitive hedging strategies,

that will perform in a variety of environments, risk managers

sources along with novel modeling software and

such as trading in assets that track the performance of consumer

will benefit from conducting what is known as “through-the-

techniques, without the red tape of corporate

credit in general, or trading in currency swaps depending on the

cycle” risk modeling. Any modeling activity requires a set

purchasing bureaucracy and legacy technology.

currency in which their assets are owned. As the market grows and

of assumptions, and these should be constructed with the

matures, more specific and sophisticated hedges will likely emerge.

expectation that economic cycles will fluctuate. So, to provide

Human Capital

flexibility to use a variety of alternative data

Advantage: Uncertain

a fairly simple example, a lender issuing a 5-year term loan

In a stressed environment, traditional and

Of course, in any market with any form of uncertainty, the best

might assume that some of those years will take place in a

alternative lenders will likely use their human

protection is offered by liquidity. A liquid market makes it possible

good economy while others will take place in a bad economy,

for investors to unwind their positions if needed, to execute

ideally constructing models and strategies that can yield good

more nuanced transactions as a hedge against risk, and to obtain

performance through a varied economic cycle.

timely and updated pricing data for their assets based on market

Traditional

~

~

capital in different ways. Lenders with a long track record have employees with experience in handling different economic scenarios and processes for scaling human-driven collections activity up and down as needed. On the other

demand and transaction history. To date, several large holders

hand, alternative lenders may be able to make

of marketplace lending assets have obtained liquidity through

people-related changes more quickly in a downturn without the overhead of corporate bureaucracy and hindsight bias.

17

Orchard Platform

Stress Testing

18

MANAGEMENT OF RISK IN ANTICIPATION OF STRESS

CONCLUSION

In a stressed environment, lenders will also want to closely monitor

borrower’s deposit account. With such a frequent payment cycle,

their application volume, mix, and quality from various origination

lenders can more immediately identify borrowers in financial

channels, as these characteristics can change quickly. Inevitably,

distress and take action to ameliorate the situation.

certain heavily stressed lenders will pull out of or adjust their spend in a given channel, which may change the dynamics of the channel’s

In a challenging economy, consumer and business borrowers

applicant pool. In such a circumstance, originators relying on that

will inevitably face financial stress, which will impact their ability

channel for borrower acquisition will do well to monitor closely for

to make scheduled payments on their debt obligations. Even

negative selection bias or for an applicant mix that does not meet

with the best underwriting criteria and most thoughtful through-

the expectations they had when they began solicitation through that

the-cycle planning, originators will need to deal with a greater

channel. In the Great Recession of 2008–2009, this challenge was

potential for delinquencies and losses. This involves having the

felt acutely by credit card issuers who sourced applicants through

infrastructure, partnerships, and plans in place to rapidly scale

third-party affiliate websites. As some card issuers drastically

up servicing and collections capabilities when necessary. Smart

reduced their marketing expenditure or even shut down entirely,

originators will also pay close attention to customer experience

those remaining in the online channel faced a very different and

to ensure that it is not negatively affected by more-aggressive

potentially higher-risk applicant mix. Making both marketing spend and underwriting criteria channel-specific can help an originator to be more resilient in a changing environment.

As we’ve mentioned, the purpose of this discourse is not to try to

possible that they could be strategically subordinated to other debt

predict the future but rather to attempt to understand the impact

obligations that carry more significant or immediate consequences.

that various versions of the future would have on a marketplace

These types of aforementioned intangibles require additional

lending portfolio and offer our guidance. Our analysis, based on

deliberation and are more a matter of behavioral economics than

the critical assumptions we have made and the stress framework

rigorous data analysis. Planning for a stressed environment and

servicing and customer management.

we have defined, yields qualitative and quantitative conclusions

mitigating the risk of a downturn is not a task for investors and

about expected marketplace lending performance during a

originators alone, but a requirement for all industry participants.

For originators, just as for investors, liquidity is king. In order to

hypothetical challenged economic scenario.

continue lending to borrowers, an originator must have consistent

Of course, underwriting does not end with the approval and

access to capital. Given that the providers of capital—investors,

booking of a new loan. Successful lenders manage risk closely

banks, and the like—will be subject to many of the same economic

throughout the customer lifecycle. The levers available for customer

forces in a changing market, originators with many diverse sources

management vary with the type of loan product. For revolving

of capital will be better prepared to weather the storm without

products such as credit cards, lenders can increase or reduce the

facing liquidity challenges. During the Great Recession, certain

borrower’s credit limit at any time in response to account activity

funding channels dried up, and some lenders went out of business

or a changed assessment of the borrower’s risk. On term-loan

or were forced to sell because they could not raise enough new

products, lenders don’t have the same flexibility, but they have

funding to fulfill already-existing debt obligations. In good times,

other options, such as proactively adjusting the repayment term of

the securitization markets and bank warehouse facilities are useful

a borrower’s loan based on identification of financial stress. Any

and attractive options. However, markets open and close, spreads

customer-management strategy depends on the lender’s ability to

widen, and banks can pull their credit lines. As a result, access to a

continuously monitor both the status of the loan and the borrower’s

diverse marketplace of institutional capital is particularly valuable

credit. Lenders can pull updated credit bureau data on a periodic

for originators. If originators have the ability to fund loans using

basis to identify deterioration, and some credit bureaus offer

capital from a variety of investors, they will be less dependent on a

trigger-based systems that raise an alert if a certain action takes

single source.

place on a customer’s credit file. One innovation that is particularly helpful to some alternative lenders is that of the daily-pay credit product. Certain issuers offer products that pay down the loan by debiting a small amount each day, rather than each month, from the

19

Orchard Platform

Conclusion Perhaps the biggest takeaway relates not to the challenges that Our forecasts, using the data available to us and leveraging

would face marketplace lending, but rather to the opportunity

our team’s credit expertise, suggest that marketplace lending

that such economic uncertainty would present to the industry as a

should prove remarkably durable to various economic stressors.

whole. While every originator’s underwriting model looks flawless

Understandably, yields would be lower, but our sensitivity analysis

in a good economy, what happens when things start moving

indicates that it would require significant levels of stress in order

sideways? This would be marketplace lending’s “Darwinian”

for a thoughtfully constructed marketplace lending portfolio to

moment: Are the innovative underwriting models employed by

realize negative returns. As an asset class, marketplace lending has

marketplace lending upstarts capable of better managing risk

some clear advantages, including lower volatility and a greater

than their legacy counterparts? Is the ability of leading platforms

degree of flexibility. However, there are also some areas that

to leverage proprietary technology and large amounts of data

suggest cause for concern, and these should be closely monitored

adaptable to changing market conditions? With the world

and managed going forward. For example, the quality and

watching and naysayers expecting failure, a resilient performance

efficacy of backup servicing models in the event of originator

by marketplace lending in the face of significant adversity has

bankruptcy is untested. While originators and investors are

the potential to cement the current shift in credit and catalyze

paying an increasing amount of attention to this issue, no level of

continued growth and prosperity. 

analysis can simulate these types of untested systems and capacity challenges. As Mike Tyson famously said, “Everyone has a plan ’till they get punched in the mouth.” A similar concern is where marketplace debt obligations would register on the payment hierarchy for a borrower faced with financial stress, and it’s

Stress Testing

20

ABOUT THE AUTHORS

David Snitkof

Larry Haertel Jr.

Raymond Han

Michael Toth

CHIEF ANALYTICS OFFICER

SENIOR MARKETING MANAGER

PLATFORM DATA ANALYST

MANAGER, INVESTMENT AND

AND CO-FOUNDER

CREDIT STRATEGY

David Snitkof has applied analytics and technology to banking,

Larry manages Orchard’s product marketing efforts, working closely

Raymond started his career at a high-frequency trading shop in Chicago

Michael Toth has four years of experience working in credit risk

healthcare, travel, and media for over ten years. At American Express,

with the product and sales teams to develop Orchard’s positioning and

as a quantitative trader/programmer. While there, he made markets in

analytics. Prior to joining Orchard, Michael was an associate at

he worked on risk, product, and marketing analytics for new consumer

help bring new products to market. Larry is also a regular contributor

futures options and performed analyses on the firm’s pricing strategies.

BlackRock. He was responsible for the valuation and risk analysis of

card products and partnerships and also developed the underwriting

of content to the Orchard blog and serves as in-house editor for

Afterwards he joined Morgan Stanley as a trading desk strategist in

mortgage whole loans and securitized products for several financial

criteria used to approve or decline billions of dollars in new credit.

external content initiatives. Larry first joined Orchard as an intern while

the securitized products group where he focused on bonds backed by

institution clients, covering portfolios with valuations in excess of $400

At Citigroup, David led a team driving analytics and strategy for the

completing his MBA at UCLA Anderson and came on full time upon

consumer debt such as student loans, credit cards and auto loans as well

billion. Michael is a graduate of the Wharton School at the University

full Small Business credit lifecycle. David also was head of analytics

graduating and moving back to New York. Prior to getting his graduate

as esoteric finance. Raymond’s responsibilities included knowing the ins

of Pennsylvania, where he received a B.S. in Economics with Finance

and marketing at Oyster.com, an online travel startup since acquired

degree, Larry held sales roles at UBS and Curbed and also gained valuable

and outs of different bond structures and the credit of the underlying

and Statistics concentrations. In his free time, he enjoys traveling,

by TripAdvisor. David excels at developing creative uses for data,

marketing analytics experience as an intern at ChowNow. A graduate of

collateral. He has also built reports which helped the desk manage

playing chess, and programming.

communication of technical concepts, and building high-performing

Brown University, Larry won the Ivy League Championship while also

risk positions. Raymond is an alumnus of Brown University where he

teams and products. He graduated from Brown University, where he

earning three-time All Ivy League honors as captain of the golf team. He

studied applied math and computer science.

studied Economics, Cognitive Psychology, and Neuroscience.

remains active on the local and national competitive amateur golf circuit.

[email protected]

[email protected]

Orchard is the leading provider of technology and data to the marketplace lending industry, powering the interaction between institutional investors and loan originators. Founded in New York City in 2013, Orchard’s orchardplatform.com

mission is to build the systems and standardize the data that will allow marketplace lending to efficiently grow into a global financial market. Orchard provides products and services to investors that enable them to understand, access, and execute marketplace lending investments, including Market Data & Research, a powerful Order Management System, and detailed Reporting & Analytics. Orchard offers originators access

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[email protected]

DISCLAIMERS © 2016 Orchard Platform Advisors, LLC. All rights reserved. This paper is provided by Orchard for informational purposes only. Actual loan or business performance may differ significantly from historical performance or market data. Estimates and projections are based upon data provided by recipient and/or other sources that we deem to be reliable but we do not guarantee that such sources are complete, accurate, or free of errors or omissions. Any conclusions or estimates presented herein may become

in loans and related financial products should be financially sophisticated and prepared for significant losses. Investors may or may not be able to invest in loans that meet their desired characteristics. Loans are also subject to certain legal and regulatory risks that may render them unenforceable. Any graph, chart, formula, or other device represented or described herein should not be used exclusively and must be interpreted by an experienced and sophisticated investor in order to effectively assist in any investment decision. These materials are not an offer to sell, nor a solicitation of an offer to buy, any securities.

to a diverse group of institutional investors who use Orchard to purchase marketplace lending assets. A single

inaccurate over time. Orchard has no obligation to update any of the statements or analysis

integration using Orchard’s proprietary approach to data standardization enables end-to-end marketplace

presented. These materials are provided "as is" and without guarantees, assurances, or

Orchard Platform Advisors, LLC is an SEC registered investment adviser headquartered

warranties of any kind, express or implied. Orchard does not provide and these materials do

in New York, NY. Orchard does not provide legal advice, tax advice, or accounting advice.

support covering Investor Awareness, data-driven Market Insights and seamless Data Management. Orchard

not constitute legal advice, tax advice, or accounting advice.

Before making any investment or executing any transaction, you should consult with your

has been named to Forbes Next Billion Dollar Startups list, produces the Orchard US Consumer Marketplace

Loan purchases and similar transactions give rise to substantial risk and may not be suitable

Lending Index — the only industry wide benchmark distributed on the Bloomberg Professional service — and

that loans purchased will perform in line with historical loans, or that specific loan categories

These materials are proprietary and confidential and may not be copied, distributed, or

will be available or suitable for purchase by particular investors. Loans can and do default,

shared with any third party without the prior written consent of Orchard.

is recognized for its technical and analytical thought leadership as explored on its blog.

21

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for all investors. No assurance can be made regarding the future performance of any loan,

legal, tax, and accounting advisors with respect to the suitability, value, and risk of such investment or transaction.

which may result in significant losses, including the loss of all principal invested. Investors

Stress Testing

22

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