Artificial Intelligence and big data in digital lending


Peer-to-Peer lending is aiming to double in size over the next three years, reaching nearly 10 percent of all loans in the US and Europe. There are now 2,000 digital startups, which are using artificial intelligence to analyze the data created every day. Concerning the matter, will artificial intelligence and big data in digital lending revolutionize lending business?

5 October, AtoZForex - Many financial companies are looking for ways to make their services more efficient and profitable to both lenders and borrowers. As digital lending continues growing in size, they believe artificial intelligence (AI) and big data have control to the future of loans.

Artificial Intelligence and big data in digital lending

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. Hence, data calculated credit scores such as payment history, credit history length, and credit line amounts. As well as it determines how likely applicants are to repay their debts and to calculate the interest rate of loans. If you have a low credit score, you become a risky borrower, which either means your loan application will be denied, or you’ll receive it at a high-interest rate.

97/100
Multibank Review
Visit Site
96/100
Capital.com Review
Visit Site
96/100
Markets.com Review
Visit Site

P2P 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.

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.

In the same way, big data can be a double-edged sword and create more confusion than clarity. 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 complete profile of a loan applicant. This will make more accurate underwriting decisions 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 startups are leveraging artificial intelligence?

Upstart, a California-based peer-to-peer online lending company, enhances 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.

Otherwise, it will 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. It can also find hidden patterns that might favor an applicant.

Upstart believes this can benefit people with limited credit history, low incomes. The company has also managed to automate 25 percent of its less risky loans. This can save a lot of time and energy from lenders, who will welcome a return on investments that require less intervention on their part. The planned-technology will available to banks, credit unions and even interested retailers who will provide low-risk loans to their customers.

Avant, a Chicago-based startup, offers unsecured loans ranging between $1,000 and $35,000. It uses analytics and machine learning to streamline borrowing for applicants whose credit score fall below the acceptable 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). 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.

Challenges of digital lending

Digital lending reportedly accounts for 10 percent of all loans across the US and Europe. The benefits of applying machine learning and analytics are evident. According to CB Insights, more than a dozen fintech startups are using the technology to evaluate loan applications.

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. There’s also the issue of algorithmic bias. Machine learning algorithms too often make decisions that reflect the biases.

Experts are concerned that this can introduce as 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, 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."

Let us know your opinion about artificial intelligence and big data in digital lending in the comments section below.

Leave a Reply

Your email address will not be published.