LendingClub (Pending:LC) is a San Francisco-based online peer-to-peer lender. On Wednesday, December 10th, 2014, it is scheduled to price 57.7 million shares for between $12 and $14 a share and will begin trading on the NYSE the next day.
This IPO will raise $900 Million for the company, be the second largest IPO of the year behind Alibaba’s (NYSE:BABA) whopping $21 Billion offering and be one of the top 10 technology IPOs of all time.
Based on the upper range price of $14/share and about 370 million shares outstanding after the IPO, which includes a 8.7 million share option likely to be picked up by underwriters, LC will have a market value of $5.18 Billion. Yet, the company currently is GAAP and EBITDA unprofitable and will remain so for the next year or two.
Despite its short-term profitability prospects, what makes LC so valuable today is its proprietary, innovative and, last but not least, value-creating “FinTech” – applications of computer and software technology to financial services that, for whatever reason, traditional financial institutions have failed to adopt.
We rate LC a buy generally on the basis of its FinTech to drive future deal flow, rather than any current financial metrics. We also rate it a buy based on the size of its target market – $882 billion (with a ‘B’) in outstanding revolving consumer credit, which many consumers seek to refinance.
The purpose of this paper is to pinpoint where and how LC’s FinTech creates value.
If this IPO is successful, and all indications are that it will be, it will open up a floodgate of other IPOs from companies touting their FinTech and claiming enormous valuations despite no profits. For stock market investors, it is important early on to distinguish between proprietary, value-creating FinTech and easy-to-replicate and/or whiz-bang, fluff FinTech.
In its Amendment 3 to Form S-1, LendingClub lists six different areas that it applies FinTech: (numbers added are mine)
Our proprietary technology automates key aspects of our operations, including the (1) borrower application process, (2) data gathering, (3) credit decisioning and scoring, (4) loan funding, (5) investing and servicing, (6) regulatory compliance and fraud detection.
The purpose of this article is to present the case that LC’s most value-creating application of FinTech is at the intersection of scoring and loan funding.
The second is the speed at which the whole intermediation process takes place. Beginning-to-end speed enables LC to avoid balance sheet risk, so deadly in the past to traditional financial institutions, who were slow to tranch, securitize and sell bought mortgages in the prior decade. Indeed, isolating the value of beginning-to-end speed may be impossible as there would be no viable business here in first place without it.
Specifically, the core of LC’s value-creation is a more granular scoring than banks (“Base Risk Grade” A to G) of credit card and other debt refinancing applications based on a proprietary algorithm, and a more granular array of refinancing rates based on these scores.
LC bundles these loans into tranches based on scores and offer lenders, via securitized notes or directly, more granular investing options than previously.
Based on S-1 data, the following table represents the core of LC’s FinTech innovation:
LendingClub – Interest Rates on Standard Loans as a Function of Base Risk Grade, October 2014
|Base Risk Grade
||Interest Rate (%)
||6.03 – 8.19
||6.67 – 11.99
||12.39 – 14.99
||15.99 – 17.86
||18.54 – 21.99
||22.99 – 26.06
Basically, LC is doing a better job than banks at matching credit card rates to consumer risk profiles and cherry-picking the A-to-D consumers by offering, according to its S-1, an average of 680 basis points (6.8 percentage points) below existing rates. E-to-G consumers, if they qualify at all, are offered refinancing rates above their current rates and are likely to decline LC’s loan offer.
At the same time, investors, lately financial institutions more than individuals, eagerly buy A-to-D tranches even though the loans are 680 basis lower than what consumers previously paid. This is because the risk profile has been granularized by LC to such an extent that the risk-adjusted rate of return for these A-to-D tranches has proven to be favorable relative to other offerings in the marketplace.
The following table is derived from a LendingClub information sheet for prospective investors found on their website:
LendingClub – Investor Nominal and Net Adjusted Rate of Return as a Function of Base Risk Grade, October 2014 for loans made in last 18 months
|Base Risk Grade
||Nominal Return (%)
||Net Adjusted Return (%)
We complete the article with our case against other areas identified by LC as sources of FinTech innovation and value creation. These areas are loan origination, data gathering, scoring and loan servicing.
LC’s online loan application process significantly reduces origination costs and is quantifiable. According to its S-1, its “adjusted contribution margin” was a very healthy 44% of trailing 9 month revenue. Adjusted contribution margin is revenue less origination and sales and marketing costs. It excludes engineering and G&A and stock-based compensation, which is substantial especially in the quarter before this IPO.
Origination and sales and marketing costs, net of stock-based compensation, as a percent of total loan flow was a mere 2.14% for 9 months trailing. This compares with a reportedly 5%-7% for traditional brick-and-mortar loan origination operations.
While impressive, the real source of LC’s current valuation is the expectation for rapid scaling of deal flow, not unit margins. Deal flow is a function of LC’s ability to offer consumers significantly lower loan rates.
LC’s customers are, by and large, refinancing credit card debt. Origination fees are a one-time negative and a minor portion of the total financing costs. Cutting origination costs in half through FinTech is not the source of LC’s current and future deal flow.
Nominally, there is nothing very innovative or FinTech about LC’s data gathering. They start with FICO scores purchased from traditional agencies. According to their S-1, they supplement FICO scores with “behavioral data, transactional data and employment information.” But LC is vague (intentionally?) about what this data is, how it obtained it, and how it enhances credit decisions.
Does LC data mine customer Facebook (NASDAQ:FB), Twitter (NYSE:TWTR), LinkedIn (NYSE:LNKD), eBay (NASDAQ:EBAY), Netflix (NASDAQ:NFLX), and Amazon.com (NASDAQ:AMZN) accounts? Can they get at customer cookies? Do they feed this data into a proprietary “spendthrift” algorithm? If so, we would be impressed. But, we just don’t know the extent of LC’s use of FinTech data gathering.
In addition, according to their S-1, LC does little to no independent verification of data supplied on applications.
LC definitely has a proprietary FinTech algorithm that spits out loan scores. But it is not the scoring algorithm per se that is innovative. It is the granularity of scores and related interest offerings that sets it apart from traditional banks issuing credit cards and making unsecured, small denomination consumer loans.
And it is the more granular tranching of consumer loans by score that is the innovative and value creator on the investor side.
Finally, there is nothing very innovative or FinTech about LC’s loan servicing, other than insisting that all loan repayments be remitted via ACH to avoid the more costly paper check in the mail approach that banks lazily accept.
LC has its own in-house collection teams that work delinquent loans for the first 30 days, but according to its S-1, it outsources subsequent servicing efforts to tradition collection agencies.
The is no mention of any FinTech way of dealing with delinquent accounts, such as automated text messages or use of social networks to shame. (How about @bobsmith is #LCdelinquent tweets?) Or offering 100 basis point credits on loans in return for being able to post on your Facebook page that you are delinquent?
Seriously though, LC has left many opportunities out there for start-ups to apply value-creating FinTech in the area of data mining and verification relevant to credit scoring and decisioning and use of texting and social media to improve collections.