For some people with gambling, drug use, or other risky behaviors, the answer may be different. Even if those behaviors are in the past, it takes even more time to build trust that has to overcome a challenging history. 

 

Technology is changing all of that. The company I joined one year ago, Yirendai, has created a pioneering peer-to-peer lending platform in China that is using big data and the processing power of cloud computing to quickly establish a level of trust between strangers that was previously found only with close friends. It can assess trustworthiness when the stakes are as high as making a loan equal to an entire year's salary. Serving as Chief Data Scientist, I have seen total loans originated quadruple in less than 12 months, and risk levels remain low with the company seeing a more than three-fold increase in net profit year-over-year.

I am observing that with companies like Uber, AirBnB, and Yirendai, trust-production, a deeply human and quantitatively challenging endeavor, is being revolutionized by the use of machine learning and big data technologies around the world. The 24-member data team I have created at Yirendai, with technology and industry best practices straight from Silicon Valley, is at the forefront of this endeavor in the FinTech space.

 

Producing trust when it comes to money is a particularly big challenge. It's at the core of the connection between individual borrowers and investors in Yirendai's marketplace. We've been able to automate underwriting, matching borrowers and investors, and execution of the loan transactions -- all online. This has led to origination of more than US$2.4B in loans since founding, servicing more than 10M people.

Our technology allows us to not only establish trust, but to do it in minutes through our app. A user's application and credit assessment can be completed in 10 minutes, and, if approved, the funds ($8,000-$80,000) can be deposited in the applicant's account in as little as two hours.

How can this level of trust be generated in such a short amount of time? How can it be done in a complicated market like China? 

 

There are now nearly 1.4 billion people in China, and consumer lending is a $2.5T market. Within that market unsecured consumer lending is more than $500B a year. Only 63% of the population has a bank account, only 27% has any recorded credit activity, and only 20% has enough credit information for making an assessment of creditworthiness. Yet, Yirendai has been able to build a model where credit assessments can be made more accurately and quickly than ever before. We've been able to identify and access alternative data sources, such as online e-commerce purchase history. Furthermore, we can quickly process this data to assess risk and match marketplace participants.  

Others are building similar models. One of China's largest online retailers, JD.com, has partnered with an online financial services company, ZestFinance, to offer its customers microloans for purchases. The joint venture is called JD-ZestFinance Gaia, and it will use ZestFinance's machine learning technology and JD.com's consumer data to make credit decisions. These extensions of credit will go to consumers who do not have the type of established credit history required by traditional lenders, and who would have otherwise been unable to borrow.    

So, we see how the model is changing for P2P marketplaces. I believe it's just as important to understand why this going to prove so disruptive to the traditional lending industry. I also believe that we've only scratched the surface. 

The crux of financial markets is, and always has been, to determine how best to allocate resources. Since the inception of banking in ancient Babylon in 2000 BC, the key to investing has been determining who is more willing and able to repay a loan.

That need to assess predictability of returns hasn't changed in the last four thousand years. It's still the cornerstone of banking. We can look at the example of private equity investments and see that the amount of time and data needed to evaluate an opportunity is significant. A team of seasoned (and expensive) analysts conducts 1-3 months of industry analysis and another month of acute opportunity analysis to produce a term sheet. Then there is a multi-month process undertaken to negotiate and finalize all of the legal and financial documents. Any returns would come 3-36 months later.

For investors, getting access to these types of opportunities is expensive. The price of entry is generally $5M minimum and, therefore, out of reach for all but the wealthiest investors. For borrowers, it's just as challenging. The cost of evaluating a deal alone could reach into millions of dollars.

Clearly, this type of model would be overkill for loans of smaller amounts. For someone who wants to invest a few thousand dollars or someone who wants to borrow $20K to remodel their home, a simpler system is needed. In order to accommodate all of those smaller transactions, the system has to scale. With what could be called a perfect storm of technological advances -- processing power, global market scale, and big data -- FinTech has been able to tackle these very challenges. So, FinTech is solving a problem for the existing market. However, the even bigger disruptive force comes from the fact that it is reshaping the market by opening the doors for net new users to start participating.

 

I believe the opportunities for FinTech to fundamentally change the lending market are only beginning. The initial breakthroughs we've seen in accessing, processing and leveraging big data are opening the floodgates to more advances.

One of the big opportunities we're seeing is business lending from payment gateways, such as Square with their Square Capital service. This is the ultimate in "relationship lending", and it creates a trove of new data for the payment gateway service. The gateway can now directly assess the health of the business and understand local and industry trends in real-time. This data can be leveraged to choose only the most profitable market and customer segments to service.

This model also removes a lot of friction from the traditional lending process. Customer acquisition costs are drastically decreased because merchants are already customers of payment gateway services. Furthermore, it puts the payments processor first in line for repayment, because loan payments can be taken directly out of customer transactions as a percentage of the sale.

The power of big data has created a newfound ability to reach and accurately evaluate vast swaths of the population who have been out of reach for financial markets. I believe we're seeing the first of many breakthroughs in this market. Investors must agree, as we've just seen the largest private funding round ever raised by a company happen in FinTech with Ant Financial. The company backed by Jack Ma, the man who built Alibaba, is at the heart of FinTech. It not only operates the behemoth online mobile payments business, Alipay, but it also has MYbank, an online-only lender that targets consumers and small businesses and Sesame Credit, an experimental credit rating service.

As you can see, the promise of FinTech lies in its ability to access, process and analyze large quantities of complex data. It gives us the information we need to establish trust, lower costs, and reach more people. It ultimately allows us to make faster and better decisions. Most people would agree that our ability to leverage big data is only going to improve, so with that the promise of FinTech only becomes brighter.