As digital lending continues to grow in size, companies are looking for ways to make their services more efficient and profitable to both lenders and borrowers. And they believe artificial intelligence and big data hold the key to the future of loans.

Lenders traditionally make decisions based on a loan applicant’s credit score, a three-digit number obtained from credit bureaus such as Experian and Equifax. Credit scores are calculated from data such as payment history, credit history length and credit line amounts. They’re used to determine how likely applicants are to repay their debts and to calculate the interest rate of loans. If you have a low credit score, you’re considered a risky borrower, which either means your loan application will be denied, or you’ll receive it at a high-interest rate.

Digital lending platforms believe that this kind of information does not paint a complete picture of a loan applicant’s creditworthiness. They’ve taken on to add hundreds and thousands of other data points to their process, not all of which are necessarily related to financial interactions. This can include information such as your educational merits and certifications, employment history, and even trivial information such as when you go to sleep, which websites you browse to, your messaging habits and daily location patterns.


Image: Business Insider

To be fair, big data can be a double-edged sword and create more confusion than clarity, and artificial intelligence has in large part become a marketing term for companies that want to sell their products and services. But experts in the online lending industry believe it can have a big impact on how fintech companies perform.

The data can enable companies to create a more complete profile of a loan applicant. This can help make more accurate underwriting decisions, which results in a reduction in defaults for lenders and lower interest rates for borrowers. It can also help automate parts—and maybe all—of the process.

How lending startups are leveraging AI

Upstart is a California-based peer-to-peer online lending company that is enhancing loans with artificial intelligence. Upstart uses machine learning algorithms, a subset of AI, to make underwriting decisions. Machine learning can analyze and correlate huge amounts of customer data to find patterns that would otherwise require considerable manual effort or go unnoticed to human analysts. For instance, it can determine if applicants are telling the truth about their income by looking through their employment history and comparing their data with that of similar clients. It can also find hidden patterns that might favor an applicant.

Upstart believes this can benefit people with limited credit history, low incomes and young borrowers, who are usually hit with higher interest rates. The company has also managed to automate 25 percent of its less risky loans, a figure it plans to improve over time. This can save a lot of time and energy from lenders, who will welcome a return on investments that requires less intervention on their part. The technology is planned to be available to banks, credit unions and even retailers that are interested in providing low-risk loans to their customers.

Avant, a Chicago-based startup that offers unsecured loans ranging between $1,000 and $35,000, uses analytics and machine learning to streamline borrowing for applicants whose credit score fall below the acceptable threshold of traditional loaning banks. The platform’s algorithms analyze 10,000 data points to evaluate the financial situation of consumers. For instance, these algorithms are helping the platform identify applicants who have low FICO scores (below 650) but manifest behavior similar to those with high credit scores.

The company is also using machine learning to detect fraud by comparing customer behavior with the baseline data of normal customers and singling out outliers. The platform analyzes data such as how much time people spend considering application questions, reading contracts or looking at pricing options.

Avant is exploring extending its services to brick-and-mortar banks that are interested in starting or expanding their online lending business.

Remaining challenges

Digital lending reportedly accounts for 10 percent of all loans across US and Europe, a figure that is steadily growing. The benefits of applying machine learning and analytics are evident, and according to CB Insights, there are more than a dozen fintech startups that are using the technology to evaluate loan applications and optimize the process.


Image: cbinsights

However, not everyone agrees that machine learning is the panacea to all the problems of online loans. For instance, many of these applications require you to download apps that collect all sorts of personal data. And as the Equifax hack shows, entrusting too much personal information to a single company can have dire security and privacy implications for you.

There’s also the issue of algorithmic bias. Machine learning algorithms too often make decisions that reflect the biases and preferences of the people who provide them with training data. Experts are concerned that this can introduce a whole new set of challenges for loan applicants. And the model has yet to prove its mettle during a downturn or financial crisis.

However, the proponents of machine learning–based loans are confident that AI will eventually become an inherent part of online lending. In an interview with NPR, Dave Girouard, the CEO of Upstart said, "In 10 years, there will hardly be a credit decision made that does not have some flavor of machine learning behind it."