The success of a small business depends on a healthy cash flow. Access to working capital is a much-needed lifeline for these businesses as they are constantly managing and juggling their cash flows. Paying bills, making payroll and filling orders while waiting for the client to pay the invoice is a day-to-day financial reality of these businesses. Hence, timely and adequate availability of capital is of paramount importance.  

Banks and non-bank lenders have capital available to deploy as they depend on interest rate differential to make money. Credit may get constrained or flow freely based on monetary policy, but is never dry. Still, a large number of businesses fail due to lack of financing. Clearly, the problem is somewhere else. Let’s break it down. 

There are two key parties in a lending transaction – the lender and the business borrower. Lenders often perceive small businesses to be high risk, and to preserve capital, either say ‘no’ to a borrowing request or charge a high cost. Both these outcomes are detrimental to business cash flow and further limit working capital. This outlines the need for a solution that has the potential to change this scenario, while aligning with the objectives of both the parties. There is a solution that lies in the advancements in data science technology, especially AI and ML. 

AI powered lending vs. traditional lending

In a traditional lending flow, borrower provides their information, and banks collects limited financial data, such as credit bureau, bank statements, maybe financial statements. Lately, lenders have used technology to gather banking and accounting transactions. This information is plugged into internal scores for evaluation. The problem with this approach is narrow and backward-looking data, which leads to sub-optimal outcomes. And the result is self-evident: a majority of loan applications are declined or partially approved. The process takes a long time, with no guarantee of a positive outcome. A bad outcome for both the lender and the business.  

To get to a positive outcome, lenders need to deploy risk assessment powered by AI tools. AI powered lending incorporates thousands of contextual, market and external data points to build a complete picture of the business. A picture that shows the true income potential of the business based on the economic environment they operate in. This has the potential to satisfy the requirements of both the lenders and the small business borrowers alike.

The key, though, is that data needs to be reliable, validated and bias-free, so that the lending can be inclusive. In the US, close to 20% of the businesses are minority owned, and a similar number are women owned. Their challenge in accessing credit is more pronounced. A well-designed AI model has the potential to bring objectivity to that process by removing subjective biases. Furthermore, such a system will allow lenders to meet evolving regulations, such as CFPB Rule 1071 or the CRA guidelines.   

Another area where AI can help is in tackling affordability. Credit must be affordable, especially for small businesses with low levels of capital, to make it accessible. This affordability issue is a bigger hurdle for women-owned and minority-owned businesses. According to a study from the Federal Reserve, Black- and Hispanic-owned businesses have a harder time accessing credit from traditional sources. This could be due to reasons like limited assets on books. An AI lending assessment tool can include such real-life constraints in assessment and find ways to capture assets that do not show on the balance sheets. 

If we move to the other party, which is the small business borrower, artificial intelligence has a key role to play. According to a recently published US Chamber of Commerce report, almost 25% of small businesses have adopted AI, citing improved performance. AI systems are improving decision-making across business operations, thereby optimizing resources and providing valuable insights on market trends. Small businesses now have a powerful way to show their worth to a banker by using AI tools. Small businesses can use AI to automate processes and thereby: 

• Bring all their receivables and payable data together 

• Build cash flow projections 

• Effectively build what-if scenarios, and,

• Show indicators of resilience despite turbulent economic environment

Beyond numbers, a key area that AI could be used is for financial knowledge building. Many small businesses lack education and credit experience. OECD data shows that women in particular face a gender gap in knowledge about financial management. Small businesses and banks can collaborate in delivery of these programs using AI tools in a cost effective and scalable way. 

In conclusion, AI is here to stay. It is being and should be adopted by both lenders and small businesses to enable value creation and access to financing with the ultimate pursuit of sustainable, profitable and inclusive growth.