Corporate Finance and AI: Closing the Funding Gap and Reaching the Dark Credits

By Arvind Nimbalker, Global Product Manager at Tribal

At first glance, the criteria that lenders typically use to determine which companies are creditworthy and will have access to capital seem quite reasonable. These traditional creditors look at a company’s current balance sheet, revenue, and cash flow. They can examine the financial history of the main people involved. Ultimately, they use this information to decide who gets funded and who doesn’t. It’s a methodology that has worked for many years and in many circumstances, but also leaves out a wide range of companies for which these criteria are inadequate, called by some the “credit invisibles”.

In particular, companies at earlier stages may not have the historical financials required by traditional credit computing. Other companies that have been operating for years in emerging economies around the world with less mature financing ecosystems may not yet have the type of traditional financial documentation required by many lenders. In fact, according to the World Bank, small and medium-sized enterprises (SMEs) account for around 90% of businesses and more than 50% of jobs worldwide, but around half of formal SMEs do not have access to traditional credit. Excluding this wide range of potentially deserving businesses is not good for borrowers, lenders, or ultimately consumers of the goods and services these businesses produce.

The good news is that credit providers are no longer restricted to lending practices that have confined capital markets in the past. Increasingly, lenders are turning to new technologies, such as artificial intelligence and machine learning to improve their decision-making. With these tools, capital providers can evaluate many more data points in their decision making. Additionally, with the growing adoption of Web3, the inherent transparency of blockchain technology will bring a whole new category of information.

The analogue of consumer credit

When thinking about trade finance, it is instructive to look at the consumer loan market. The now ubiquitous personal FICO score, created over thirty years ago, was arguably the first algorithmic credit protocol. This score takes into account payment history, income, and debt-to-credit ratios, among other inputs, and reduces a candidate’s creditworthiness to single digits. In the case of consumer credit, although the FICO score is a handy shortcut that has streamlined the approval process, these calculations can end up excluding younger people or others who have less established credit profiles. In 2020, the average FICO score for young adults aged 18-29 in the United States was 677, the lowest of all age demographics and 34 points lower than the US average of 711. Similarly, some who have had instances of bad credit behavior in their past might nonetheless present better credit risks than their track record suggests.

Modern protocols and artificial intelligence improve this process in several ways. First of all, modern lenders can consider a lot more than FICO scores which would have less than 50 entries. Data points from non-traditional sources such as utility bills, rental payments, cell phone and cable bills, social media sites, online search histories, and other “Big Data allow lenders to better understand whether people who might have fallen into the category of so-called “credit invisibles” may be more willing and able to repay loans than the old analysis suggested. Second, and perhaps more interestingly, the latest protocols, beyond simply analyzing larger volumes of data, actually have the ability to learn and better predict the credit behavior of the people they grant funding to. .

At the firm level, capital markets benefit from a similar expansion of learning inputs and algorithms that improve the measurement of credit risk without human intervention. Startups and other non-traditional borrowers can have their claims bolstered by the inclusion of receivable and sales histories, customer reviews, and their repayment histories. Industry-wide trends can also be used to better reflect the creditworthiness of these borrowers and improve their prospects. All of this has the effect of filling the void left by traditional methodologies for extending credit to companies that are too often left behind.

The Competitive Advantage of AI

It is also important to note that in addition to providing better opportunities for borrowers, these new technologies also have immense supply side benefits. Lenders who do not incorporate them do so at their own risk. In particular, while conventional places too much emphasis on past behavior in its analysis, artificial intelligence also considers current industry trends and future potential. The inclusion of these additional inputs leads to a more accurate assessment of borrower risk, which means less non-performance, more appropriate pricing, and more profits for lenders who use this technology to make credit determinations. faster, cheaper and more predictive. And with more inclusive analytics, these lenders are expanding their potential target market to include more potentially profitable lending opportunities, filling a niche that traditional lenders currently overlook.

Finally, AI-based analytics is a potential service that lenders can offer their customers.

Using AI-powered analytics, they can empower startups to analyze their spending and spending habits, providing transparency and insights on how to control their costs. These value-added services thus provide an additional revenue stream to lenders, increasing their profitability while simultaneously improving borrowers’ ability to manage repayment.

Of course, none of this is meant to suggest that AI and credit algorithms are perfect. Especially as they leverage machine learning, their protocols may become less connected to their human-guided origins, and their methodologies and biases less transparent, causing a potential “black box” problem. But ultimately, with vigilant attention to their outcomes, the benefits of this new technology will far outweigh these concerns and usher in a more inclusive and efficient future of finance on all sides.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.