It’s no secret times are changing for Small and Medium Enterprise (SME) lending. I think we can all agree the easy return on investment has given way to rising defaults and an origination engine that sounds like it’s barely chugging along. We all know the basic math in this business. Put lots of money on the street and hope the factor rates cover the losses while managing operating expenses to achieve a rate of return acceptable to private equity investors. So, when originations stall and defaults increase, the math becomes scary.
As the fintech business leader for DataRobot, the leader in automated machine learning, I’ve had the opportunity to meet many SME business owners at LendIt in New York, and more recently at the Innovate Finance Global Summit in London. It’s very clear interest in machine learning (a branch of artificial intelligence used by many lenders to build risk-based pricing models) is at an all-time high. We’re grateful for the support of our customers, and we take our leadership role in predictive analytics and automated machine learning very seriously. We don’t just see ourselves as a software vendor. We’re innovators that listen intently to our customers (and potential customers), and build solutions that help them achieve their goals. So, when I see this critical juncture in the SME lending industry, I feel compelled to comment.
I believe the response in the industry to the current problem of growing defaults and dwindling originations is the wrong approach. Lenders seem to be doing one of three things (and potentially all of them): lowering underwriting standards, raising factor rates, or pushing independent sales organizations (ISOs) to bring in more applications by handing out one-time commission bumps or in-kind resources such as marketing support. I believe these actions are indicative of lenders who don’t fully realize the wealth of data they have in their companies. Let’s review what I would call a “prototypical” merchant cash advance or business loan lender. First, as Sean Murray, publisher of deBanked, recently pointed out in a LinkedIn post (see, https://www.linkedin.com/feed/update/urn:li:activity:6253411111144685568/), most of the applications lenders receive are of very low quality, and in some cases are fraudulent. This will never be an industry where 50 percent of the applications will result in an offer. From the perspective of someone who talks to lenders almost every day, a company is lucky if 25 percent of its applicant pool can justify an offer. The fact is there just aren’t enough applicants in the industry to force that percentage to go much higher while still maintaining an acceptable default rate. And in terms of default rate, I’d question any lender who says their default rate is single digits. What that leaves us with is the number of applications which are approved and result in a closed deal (i.e., the “win-rate”). And this is where lenders are missing opportunities.
If lenders want to win in this industry, they need to win the right kind of deals that work for their individual business. If all lenders pursue the same strategy of pushing originations and taking on more risks, then many lenders are going to fail because a one-size fits all response to the current funding environment doesn’t work because there is no such thing as a one-size fits all portfolio. For example, if you specialize in “C” and “D” paper in the 3rd or 4th lien position, you want to win deals that look much different than lenders who want “A” paper in the 1st or 2nd lien position. Knowing how to increase your “win-rate” for YOUR type of deals is the key to success. And the key to the lender winning more of their type of deals is being able to understand their underwriting history and immediately spot deals that fit their framework, price them accordingly, and aggressively secure them before the competition. And that only happens if lenders understand their own data and have a risk-based pricing system which is lightning fast and makes the right decisions.
I’m not going to tell every lender to pursue automated machine learning. If a lender is brand-new to the market and isn’t keeping their data for analytical purposes, then deploying advanced machine learning algorithms is an inferior solution to good old-fashioned rules-based underwriting. But for everyone else, here is something you need to know. Many private equity funds are using machine learning algorithms to allocate their investments. It doesn’t take much imagination to consider those funds may start asking themselves why they are using machine learning to allocate their investments to companies not using machine learning to manage their risk exposure. The key to achieving success in a tightening market such as SME lending is to intimately understand your business. There is no more room for one-size fits all strategies. Consider what would happen if you simply doubled your “win-rate” for the deals that fit your business without looking at more applications? You would start spending a lot less time beating the originations drum and have more time to deliver superior customer service to increase your renewal rate. Automated machine learning can make that happen.
Two years ago, I left a promising career at PayPal, a major technology giant, for what some considered a risky move: I joined BlueVine, a young fintech startup. My title: vice president of risk.
This year, I took on an even bigger role when I was named chief risk officer of the Silicon Valley company, which offers working capital financing to small and medium-sized businesses.
My promotion comes at a time when risk is becoming a bigger concern in fintech, which is ushering in big changes in banking and financial services.
Fintech revolutionizes financial services
Data science technology has dramatically improved access to financing and the way we manage our money. The fintech wave that began with my former company, PayPal, and the world of payments, has spread to other aspects of personal finance, from mortgages to student and auto loans to investing.
This expansion was accompanied by growing concern that the fintech boom is fraught with risks that, if left unchecked, could lead to a major bust in the financial services industry that could in turn cause harm to the broader economy.
In a speech in January, Mark Carney, the governor of the Bank of England, cited the need to “ensure that fintech develops in a way that maximises the opportunities and minimises the risks for society.” “After all, the history of financial innovation is littered with examples that led to early booms, growing unintended consequences, and eventual busts,” he said.
Risk management as key to success
Risk management certainly has been a focus area for BlueVine from the beginning.
BlueVine joined the revolution in small business financing in 2014 when it rolled out an innovative online invoice factoring platform.
Factoring is a 4,000-year old financing system that allows small businesses to get advances on their unpaid invoices by providing easy, convenient access to working capital. BlueVine transformed what had been a slow, clunky, paper-based solution into a flexible and convenient online financing system that enables entrepreneurs to plug cash flow gaps that often hamper business growth.
Because the BlueVine platform is based on cutting-edge data science technology that can process and analyze information to make quick funding decisions, managing risk inevitably became a major challenge in building our business. As Eyal Lifshiftz, our founder and CEO, recalled in a recent column, in BlueVine’s first month of operation, almost every other borrower defaulted.
In fact, that was partly the reason Eyal invited me to join his team. BlueVine serves small and medium-sized businesses seeking substantial working capital financing of up to $2 million. To succeed, we needed to build a robust data and risk infrastructure.
Small startup with big data needs
Joining BlueVine also posed a personal challenge.
At PayPal, where I started as a fraud analyst and then moved into the company’s data science division where I helped develop behavior-based risk models, I had enormous amounts of data to work with to do my job. Now, I was joining a young startup with very limited data history, but with big data needs.
This meant putting together exceptional and experienced teams of data scientists and underwriters and developing a technology that becomes progressively more precise and accurate as it draw lessons from our steadily expanding data and underwriting decisions. It was important for us to have a group of super smart, highly-motivated and technologically-strong people working closely with a team of experienced and sharp underwriters.
Here’s how the process works: Our underwriters develop a robust methodology which is then translated into detailed logic decision trees.
Each decision tree includes dozens, even hundreds of branches, made up of question sets on different underwriting situations.
For example, a decision tree could focus on approving new clients coming from a specific industry, such as transportation or construction, or on increasing the credit line for a client with a specific financial profile.
A typical decision tree would drill down on further financial questions: What’s the expected cash-flow of the business in three to six months? What’s the pace at which it has accumulated debt over the past year? Are the business sales seasonal in a material way?
The questions could also focus on non-financial areas: Does the company’s website look professional? How does it compare with major companies in its industry? Does the business actively maintain its Facebook and Twitter accounts?
The goal is to build a risk infrastructure that steadily becomes more efficient in answering questions in an automated, large-scale and highly accurate manner. Our data scientists leverage multiple external data sources and use dynamic advanced machine learning models to answer these questions pretty much in real-time and with a high degree of accuracy.
So it’s a combination of technology and human input. There will always be gray areas, questions and situations that cannot immediately be addressed by our computer models.
But as the models get better and more robust, the gray areas will shrink. Our models are constantly and automatically enhanced, re-trained and expanded by the most recent data and input from our underwriters.
Think of it as the fintech version of Deep Blue and AlphaGo, the powerful computer programs that famously outplayed topnotch chess grandmasters. Both programs were based on similar principles: the more they played, the more knowledge they absorbed and the more formidable they became at chess.
Technology and teamwork
An even better example is the self-driving car powered by Google’s artificial intelligence technology. Human input is still required, but the more driving the car does, the smarter and more autonomous it becomes.
Building our risk infrastructure is an ongoing process for BlueVine. But it already has helped us steadily expand our reach, making us stronger, smarter and even faster in financing small and medium-sized businesses.
In just a couple of years, the strides we’ve made in managing risk more effectively enabled us to increase our credit lines to $2 million for invoice factoring and $100,000 for business lines of credit, which means we’ve been able to serve bigger businesses with bigger financing needs.
While we initially focused mainly on small businesses with annual revenue of under $250,000, today we have an increasing number of clients with annual sales of more than $1 million and increasingly, we’ve been able to serve clients with revenue of more than $10 million a year.
By the end of 2016, BlueVine had funded roughly $200 million. We’re on track to fund half a billion dollars by the end of this year.
We’ve accomplished this in a time of heightened skepticism about fintech in general and alternative business lending in particular. But rather than scoff at this skepticism, I’d point out two things.
First, fear often accompanies the rise of a new technology. Second, in the wake of the 2009 financial crisis, it’s prudent to raise hard questions about the rapid emergence of new financial technologies.
While building technologies and companies that can provide financial services faster and easier is a laudable goal, It’s wise to move cautiously and with humility.
The BlueVine experience underscores this.
Risk is still a challenge we take on every day. But we have found ways to take it on confidently and effectively with a vigorous combination of technology and teamwork.
Ido Lustig is Chief Risk Officer of BlueVine.
In March 2014, I wrote the following for DailyFunder.com: I think we are either currently in, or are fast approaching a “market bubble” in MCA. Bubbles never end well…When I see some of the business practices, offers, terms and other aspects of our business today, I am worried…assets are being overpaid for through higher than economically justified commissions …and [funders are] stretch[ing] the repayment term of the MCA or loan even further. I went on to say that this felt to me an awful lot like the subprime mortgage meltdown of 2008.
Like all good bear market prognosticators, I was a touch early in my forecast. 2014 and 2015 were continued boom years for small business alternative lenders (or “small business Alt Lender.” I don’t agree with applying the moniker “online lender” for our industry. It might be sexy, but it’s not accurate.) Loan and MCA terms got longer, loan pricing to the client dropped further, companies grew 100% year over year. And then 2016 happened.
The most shocking event for me in 2016 was the disruption at CAN Capital. They had the most data, the most experience, market dominance, and the most in-depth institutional knowledge. The granddaddy of all of us. Not far behind is the fiasco that is On Deck, the only publicly traded small business Alt Lender. In the past 12 months alone, the stock price has declined by over 40%. And that is after a roughly 50% drop in stock price in 2015. The first 9 months of 2016, driven in part because of market required changes to their business model when they could no longer profitably sell a sufficient volume of loan originations, they have a GAAP net loss of almost $50 million. There have also been a number of other lesser but still high profile failures, shutdowns, and exits from the industry in the past several months alone.
So what is driving this abnormally high rate of failure in the Alt Lending industry? Is it the “New Normal?” And what do I think lies ahead in 2017 and beyond? Before revealing my personal crystal ball again, I will share an anecdote from earlier in my business career.
I was the CFO (and eventually CEO) of a profitable, long-tenured family owned construction company. We had a working capital credit line from a major bank secured by a first position lien on our accounts receivable. The credit line was also personally guaranteed. We borrowed from the credit line for three reasons. For cash flow, when our receivables paid more slowly than expected; we had tax payments due; or we purchased a large piece of equipment. We always paid back the draw on the credit line as quickly as we could, to keep interests costs low, to impose cash management discipline, and to create future availability on the line once repaid.
The credit line was for one year. It was always renewed. But I was frustrated to have to go through an annual underwrite process with our bank, despite the personal guarantee, consistent profitability, and that we always paid back our draw on the credit line. Our banker (patiently) explained to me that economic cycles changed, and medium sized businesses – we had about 200 employees – suffered ups and downs and sometimes became financially distressed and even went out of business. The bank wanted to protect their position and not overextend the term of the credit line.
When I started RapidAdvance in 2005, I drew on my personal knowledge and previous experience as a borrower. The products we offered made sense based on our customer profile which was main street small business. We needed to protect against economic cycles and the high rate of small business failure. The maximum term offered by any company in 2005 was 8 months, at that time only for an advance product (future purchase and sale of credit card receivables), not a loan. Payment was received daily through a credit card split, thus allowing for a future capital advance (renewal) within about five or six months as the open advance was paid down. Cash advances could be used for taxes, equipment purchases, or business expansion. The price of the product reflected the risk of the credit offered.
What many in the small business Alt Lending industry seem to have forgotten, or never learned, is that our business is fundamentally a subprime credit industry. We are either lending to subprime borrowers, because of either the personal credit of the owner or the balance sheet of the borrower, or if the credit is strong and the business is more substantial, the loan itself is a subprime risk because we are at the bottom of the capital stack – behind the bank loan, the business property mortgage loan, the other personal guarantees of the owner, the factoring company, etc. We are taking the most risk. To offer two and three year terms and to try to pretend to get to “bank like” rates is, in my opinion, committing lending suicide.
At Rapid, we were dragged kicking and screaming into slightly longer term and lower cost products in order to stay competitive with certain customers. But we have kept that pool of customers as a very small percentage of our overall receivables.
Going into 2017 and beyond, I see five major trends. First, terms will get shorter, prices will increase, and offers will become more rational. That is already happening. Second, capital to this industry will become less available. The best companies with proven data driven models, consistent underwriting, a strong balance sheet and predictable loss rates will get financed. The days of easy money chasing this space are over. Equity will be particularly hard to come by.
Third, there will be continued disruption of funding companies. Companies will consolidate and some will disappear. On Deck may be in for a big challenge. They had a tremendous cash burn converting their business model to more balance sheet financed instead of originating and selling loans. Their market cap today is approximately book value, i.e. if you could buy up all the shares of the company at today’s trading price that would be roughly equal to their cash on the balance sheet and the value of their net receivables. The next two quarters are crucial for them to show the market they have turned the corner to become a self-sustaining lender. I am not optimistic, but I am rooting for them to succeed as it is in the best interests of the industry.
Fourth, stacking will continue to be an issue. I believe that the legal system over the next few years will bring some semblance of order to this industry scourge. At Rapid we have taken an aggressive legal stance against stacking, with some success in the courts. The challenge is that each situation is fact specific, and to prevail in a claim of tortious interference, the first position lender has to prove damages. I think that an unrelated decision at the end of 2016, Merchant Funding Services, LLC vs. Volunteer Pharmacy in New York State, could be a game changer. Because of the form of contract and the business practices in Volunteer, the judge ruled that the transaction constituted criminal usury. Knowing the business practices of the stackers, specifically the practice of writing an agreement that pretends to be a sale and purchase of future receivables but is in fact a loan, which is the basis for the judge’s ruling in Volunteer, I can see lawyers seizing on this precedent to help overstressed small business owners attempt to void their stacked loan agreements. The small business would first block the stacker’s ACH, claim the contract is void because of criminal usury, and then sue the stacking company. There could also be class action lawsuits like we saw a few years ago in California – bundle together a number of these claimants and go after the deep pocketed investors and banks that finance the stacking companies. The State’s Attorney General in New York may take a public policy interest in these types of loans. Once the dominoes start to fall, the costs of stacking – litigation and unpaid loans, in addition to proactive claims for damages – could be enormous for both the stacking companies and their owners and investors.
Lastly, and to my great pleasure, I think we will stop hearing small business Alt Lenders calling themselves “Fintech.” I think we will see the beginning of the demise of fully automated, no manual touch funding. At Rapid we have data and risk and pricing algorithms but we have always had an underwriter at a minimum review every file. At conferences when I have presented or participated in Fintech panels I always referred to Rapid as a technology enabled, non-bank small business lender. Now even On Deck describes themselves in similar terms.
I titled this post “The New Normal.” In the classic Mel Brooks movie Young Frankenstein, Dr. Frankenstein sends his assistant Igor to steal a brain from a cadaver to implant into his monster. But Igor accidentally drops the genius brain he was supposed to steal, and brings the doctor a different brain without telling him. When the monster awakes and has the personality of a psychotic five year old, Igor tells him he brought him a brain that was labeled “normal” instead of the one he was supposed to steal. It was, as Igor read it, “Abby Normal.” Abnormal, I believe, is the “New Normal” we will be dealing with in 2017.
The economic recession over the last decade significantly slowed banks’ willingness to approve small business loans, and the impact on small businesses’ ability to get loans from banks is still being felt today. According to the Wall Street Journal last year, big banks have decreased the number of loans to small businesses by more than 38 percent since 2006.
But the recession helped pave way for another industry – alternative lending – which has significantly improved access to capital for small businesses. According to the Small Business Administration (SBA), the 2016 fiscal year was a record setting year for loans, with more than 70,000 approved that totaled $28.9 billion and supported nearly 694,000 jobs.
The success of alternative lending showed banks the importance of expanding their offerings, particularly with online loans and small businesses. Over eight years removed from the recession, banks are taking notice and rebounding to grant more small business loans and release new financial services. More and more headlines show that banks are shifting their strategies to keep up with America’s technology and alternative lending habits, making 2017 the year banks finally get back into the fray and play ball with alternative lenders to improve the lending process.
For one, banks already have built-in advantages to accomplish this:
- an extremely low cost of capital
- a built in customer base that can be targeted
- visibility into accounts and access to a treasure trove of key data
In 2016, we saw large banks explore three key strategies: build, buy or partner. Let’s look at a few examples of each:
Build: Wells Fargo went to market with its own technology in 2014, called Wells Fargo FastFlex for Small Businesses. Opening access to lines of credit, term loans, and SBA loans, Wells Fargo set a five-year goal to extend $100 billion in loans to small businesses. In December 2016, Citizens Bank announced plans to start offering its own digital small-business loans by the middle of 2017.
Buy or License: Instead of building infrastructure, banks can acquire or license off-the-shelf technology. This route is for the financial institutions that don’t believe in building tools themselves or want to move more quickly than their internal development resources will allow. Instead of expanding its suite of offerings on its own, they would rather acquire an existing infrastructure and focus on the top end of the lending market. Kabbage has led the way on the licensing deals by announcing partnerships with ScotiaBank, Santander, and ING.
Partner: Through partnerships, banks can expand their loan offering and quickly leverage other’s technology. Through licensing deals or white-labels, banks can send businesses they decline to work with to alternative lending options to give their customers additional access to small business loans. In December 2015, JPMorgan Chase took this route and partnered with On Deck Capital to provide alternative lending and small businesses loans to its customers. JPMorgan Chase also partnered with LiftFund in October 2016 to fill the remaining gaps in its small business lending services.
It was a resurgent year for banks’ ability to offer small business lending. In fact, going into 2016, American Banker predicted that banks would set their sights on online lending by signing strategic partnerships with the leading platforms. That came true to an extent, but based on recent trends, 2017 will really be the year that banks and alternative lenders start to work together.
No longer content to be sidelined, banks are starting to play ball, and they will continue to do so at an even faster pace. The fact that banks are moving in now and increasing small business loans validates alternative lending. As JPMorgan Chase has showcased, partnerships between banks and alternative lending can offer channels of sales for both parties and improve the small business lending process. The next step is for banks and alternative lending to work together.
My name is Justin Dickerson. For most of 2016, I was the Chief Data Scientist at Snap Advances (Snap), a funding company of merchant cash advances based in Salt Lake City, Utah. I can’t discuss my awesome work at Snap for obvious reasons. And fortunately, I don’t need to in order to make the key points I want to convey through this article. That’s because I’ve also been a senior level data scientist at two other companies, and I’m also a well-regarded statistician who holds one of the most prestigious credentials offered by the American Statistical Association.
One discovery over the past year prompted me to start collecting my thoughts for this article. I was looking at the financial performance of On Deck Capital (the largest company in the alternative fintech industry which is also publicly traded) through the first nine months of 2016 relative to the same period in 2015. Gross revenue increased more than $22 million while net income for the same period fell nearly $50 million. I’m not an accountant, but that doesn’t sound good to me. And let’s face it, this fact doesn’t surprise anyone in our industry, especially given what’s happening at CAN Capital. But one interesting and overlooked fact is worth considering. According to my Linkedin search, there were between 30 and 40 data scientists (all levels) working for On Deck Capital during the same time period in which they lost $50 million. So, not only does On Deck Capital lose a lot of money, it appears they need a lot of intellectual horsepower to figure out how to do so.
And here we are today. We’re looking at an industry full of companies trying to navigate the abyss of hyper-aggressive originators and spiraling default rates. If you’re a Chief Data Scientist for one of these companies, you’re undoubtedly feeling the heat from your management team. The problem is simple. How do you grow your business (or even stabilize it) in an environment where you have to take too many uncomfortable risks? We’ll ignore the fact this question has plagued much larger industries for many years (e.g., trying to compete against Wal Mart in the retail space). Boards of Directors in alternative fintech have short memories and believe this is a unique problem to their industry and era. As a result, data scientists are at a premium as they’re seen as key players in how to resolve this crisis and steer their companies to safe harbors. Well, here is my opinion. They’re dead wrong, and here is why.
Data Scientists Are Tactical, not Strategic
This statement may end up being the most controversial thing said in the data science industry this year. But let me make my case. Of those 30-40 data scientists working for On Deck Capital, more than 80% of them have a Master’s degree in a field of study synonymous with data science. Specifically, many of them attended Columbia University’s Master’s degree program in Operations Research. The four required courses for that degree are: Optimization Models and Methods, Introduction to Probability and Statistics, Stochastic Models, and Simulation. From there, students can choose from one of six concentrations (all but one of which are targeted toward quantitative methods). Further, students selected for this program already have highly refined quantitative skills as demonstrated by the pre-requisite courses for admission (e.g., multivariate calculus, linear algebra, etc.). So, in essence, the program takes really smart quantitative people (quants) and makes them even smarter quants, while sprinkling in 6 elective courses which may or may not provide an opportunity to learn something about the “real” world of business.
Make no mistake, the students attracted to programs such as these generally aren’t the professionals you send to meet with investors and pitch them on new strategic directions for a company. They are the professionals who sit in cubicles and spend their days writing code. They are experts in programming languages such as R, Python, Java, Scala, and many others. Ironically, they are enslaved to similar rules which govern the same supervised machine learning algorithms they create each day. They aren’t allowed to “get out of the box” and see the “forest through the trees.” If I’m portraying them as a bit robotic, that’s intentional on my part.
I don’t want to leave the impression data scientists can’t think for themselves. Specifically, those who earn a PhD are known to have such skills and are often praised for their abilities to rise above the technical chains of their existence and offer strategic direction to an organization. But they are few and far between in the data science factory found deep in the bowels of companies like On Deck Capital. Instead, more and more alternative fintech companies seek out the same “cookie-cutter” data scientist who can check off the same boxes on the hiring list. This means the data scientist role is relegated to a part of the company lacking diversity of thought, creativity, and the organizational respect needed to save a company from itself.
The Law of Diminishing Returns
One of the most intelligent questions asked of me within the alternative fintech industry was, “do we really have enough data to justify so many data scientists?” As a Chief Data Scientist, you always want to answer that question with an emphatic, “YES!” Even better, you may tell your management team you need even more data scientists to make a “real and lasting contribution to the company.” After all, the existence of your team depends on it. But when you’re away from the management team and thinking about the structure of your department, the honest Chief Data Scientist knows the company is at risk of experiencing the law of diminishing returns.
All of us can recognize the law of diminishing returns from our freshman year Economics course. In short, it’s the concept of achieving less than a one to one relationship between an additional unit of input relative to the resulting measured output. For example, the reduction in default rate for a financial product is hardly ever proportional to the number of data scientists employed by the company to predict default rates. In fact, I would argue once you have more than two or three data scientists, even the largest organizations would have a difficult time justifying the payroll investment based on proportional gains in default rate management.
So, why do companies like On Deck Capital have so many data scientists? I believe it’s more akin to the comfort food we all like to eat in the winter. There is hardly anything as satisfying as my grandmother’s homemade chili during a cold Utah night. And the more of it I get, the warmer I feel! The problem is the chill of winter eventually fades and the light of day shone on financial statements eventually begs the question of whether we’ve simply eaten too much.
Make no mistake, NO organization needs endless amounts of data scientists to be successful. In fact, I would argue two or three excellent data scientists armed with superior data science/machine learning platform technology such as those offered by IBM, Microsoft, or DataRobot is more than enough to guide an organization to success. The key when thinking about staffing a data science department is to think in terms of credibility. If I have three data scientists each armed with PhD training, 15 years of industry experience, and the tools (such as a great machine learning platform) to do the mundane parts of data science usually done by legions of Master’s degree data scientists, am I more credible in the organization than I am with 30 quants who all grew up in an economy where nothing bad ever happened to financial institutions? If you want your data scientists to help your organization, you’ve got to be willing to let them into the board room and present digestible recommendations for action. So the question becomes, do I have a team that is credible enough to meet such a standard?
The Supremacy of Domain Expertise
I learned a lot during my time as a Chief Data Scientist. Since leaving Snap, I’ve established two companies. The first is Crossfold Analytics. This is my data science consulting company. We only serve the fintech industry and we spend most of our time building real-time machine learning prediction services for small to mid-sized fintech companies. And I think we’re darn good at it! The second company is Crossfold Capital. This is my independent sales organization (ISO) focusing on merchant cash advance, business loan, and factoring products. It was when I established Crossfold Capital that I learned the most valuable lesson of all about data science in alternative fintech. Nothing will ever replace the experience of working in the trenches of the business (what I call “domain” expertise). In alternative fintech, this is generally working within the trenches of a sales organization. If I could go back in time and start over as Chief Data Scientist at Snap, I would start my job by underwriting files and selling merchant cash advances for a month. Absolutely nothing I learned in math, statistics, or any quantitative subject can replace what I’ve learned running my own ISO in just the past two months. I wish every alternative fintech company would adopt a training program for data scientists that allowed them to spend their first month in the field calling on clients and working with potential customers. If you understand the business, you can bring immeasurable value to your company by blending that understanding with your technical skills as a data scientist. I truly believe such an approach could take the power of a data scientist and magnify it three-fold. Otherwise, you end up having a rogue department of quants that people in the trenches of the business either don’t understand or don’t trust.
My Recommendation to Alternative Fintech Companies
Based on what I’ve learned as an alternative fintech data science professional, I would make three recommendations to all companies in our industry. First, hire diverse talent. It’s imperative a data scientist knows enough about coding to be effective at building predictive models. But I would trade extensive coding expertise for a data scientist who also had a Bachelor’s or Master’s degree in business administration. We don’t need an army of robots in data science. We need gifted thinkers who also happen to have advanced technical skills. Second, don’t “over-eat” even though it can be cold outside. More data scientists aren’t going to solve your problems. In fact, hiring the same type of data scientist only encourages “group-think” which can actually be very detrimental to your organization. Focus on building a credible data science department, not a massive data science department. Finally, put your smartest people in the dirt of the business. Have them spend a week underwriting files. Then send them to sell your products with one of your ISO managers. Don’t treat your data scientists as fragile figurines. As a good friend of mine from Texas says about his gun collection, “they may be worth a lot, but they’re so dirty from hunting you wouldn’t know it!”
I hope my confessions help your organization navigate both fair seas and choppy water.