Can AI Enable Equitable Lending?

Rezbi Kaur,

Research Contributor,

Indian Society of Artificial Intelligence and Law.


In one of his interviews, Dave Chappelle had said, “Money is the fuel for choices.” But what happens if the prime source and keeper of money in the economy is facing a crisis? The answer is that the crisis hampers the bank’s ability to give out loans, to say the least. If these loans are stopped, it causes a lag in economic growth. The liquidity that every company maintains with the help of debt capital almost vanishes as the funds that it needed are no longer available. A banking crisis also leads to higher unemployment rates as the company does not even have funds to sustain itself in the first place and is usually hesitant to shell out retained earnings for hiring human capital. Due to lack of appropriate funding, the production capacity also suffers, and the products reach the customers at a much slower pace than before. So, not only production is affected (supply) but also basic consumption by end-users (demand) which leads to reduction in profits. When banks do not give out personal loans, the amount of money invested in financial markets also decreases as a result of which the national market indices like SENSEX or NIFTY underperform when compared with other global indices. This further lowers the confidence of domestic investors as well as foreign investors. Therefore, it is safe to say that the failure of the banking industry is not just one isolated problem but creates a domino effect, making the whole economy suffer.


To get a better understanding, let’s answer a few pressing questions.


What is wrong with the banking system?

Among many sad wrongs that the banking industry is facing in India, the most substantial one is bad loans. As it is commonly known, the basic task of banks is lending funds or loans to individuals and accepting deposits. The advanced loans can be stripped into two parts - the principal and interest. The loan portion is considered to be an asset for the bank and, as long as it receives timely interest payments, the loan is considered to be a Performing Asset. But when the interest is not paid for more than 90 days, the loan becomes a Non-Performing Asset as it is not being able to generate the income (interest) the way it is supposed to. And this becomes a “bad loan.” The bearable percentage of Non-Performing Assets is 1-2% of the total amount of loans as any standard economics or accountancy book states. But, in every bank in India, the percentage is easily over 4% and sometimes even 10%, as per the books of accounts [1]. Needless to say, this is a huge setback.


What is causing this problem?

There could be many macroeconomic factors to blame. But a large portion of this problem is caused by “esteemed” industrialists. For example, if in 2004 you and Vijay Mallya had walked into a bank to ask for the same amount of loan that he had, you obviously would not have been able to avail that loan. Why? You are just a common man, but he was setting up an airline business. It becomes a matter of esteem for the bank about who their corporate debtors are. Vijay Mallya was a great addition to their list of customers which a common man would not have been. And this is the problem. The banks do not or fail to take into account the facts that matter when lending corporate debts. The other reason is that the amount of data that banks have to assess the creditworthiness has risen over the years but banks are still bound by the age-old underwriting technique - the “credit bureau check.” A credit bureau is a company that collects information about an individual’s credit history by relying on salary slips, bank statements, and address verification and selling their information in a summarised credit report. This usually takes as low as Rs 164! [2] The credit report is concluded by a credit score, and that is what decides the fate of the potential borrower. But why are credit scores bad? Here’s why. Rita, a 45-year-old, took a loan of one lakh rupees to buy a two-wheeler for herself. Having a steady flow of income enabled her to pay back the amount and interest. This made her credit score look good. But will she be able to pay back a home loan of five crore rupees without defaulting? This is where the problem arises. A credit score cannot differentiate between the two cases but only makes her appear creditworthy. The reliability of credit scores becomes seriously questionable when the potential borrowers do not have an extensive credit history due to their young age or factors such as getting their first job after waiting for too long as the job scenario is not particularly forgiving or taking a break from their career to fulfill other obligations. These factors do not make the applicant any less worthy but due to the restrictive nature of credit bureau checks, their credit scores will be unsatisfactory. Credit scores fail to take into account a large amount of data available from contemporary and modern platforms about online purchases, travel patterns, etc. which can give lenders a much more holistic picture of the creditworthiness of the borrower. So, relying on machines to process a huge amount of data without relying on human judgment and picky data sources seems like a reliable solution.


How can AI help?

Artificial Intelligence is well equipped to process a large amount of data that human underwriters simply fail to understand. Let us consider a potential borrower named Aman, a businessman. His bank statement is filled with hundreds of items that covers thousands of lines, his credit information also lists hundreds of items, his call records seem endless and his digital footprint covers “miles.” From all this, it seems that Aman engages in many activities, both of financial and non-financial nature. But, the credit bureau checks do not have the ability nor the required level of technology to make sense of the huge amount of data available. This shortcoming of humans is overcome by AI. A subset of AI called Machine Learning can help in analysing non-numerical data, apart from the obvious numerical data, such as buying or spending behaviour, social media activity including the profiles of the people they visit frequently, employment breaks, the organisation they are a part of, etc. This holistic assessment will also allow the banks to charge interest rates accordingly so that the borrowers do not default. If AI is used in the phase of loan origination, it can help to detect and, thereby eliminate human errors made in the application. Since AI is helpful in understanding patterns of consumers in different aspects, it can be used for assessing whether the potential borrower is close to bankruptcy or would become bankrupt or not during the tenure of the loan. All these uses of AI help significantly in lowering credit risk of banks, saving it from defaults and also the economy from facing a liquidity crisis. But if AI is so helpful then, what is the hesitation? The answer is the existence of bias in the AI powered machines.


How to remove the bias for equitable lending and lesser defaults?

The AI-based machines are fed past data so as to predict a future course of action. This historical data that is available to be fed to the machines, is already riddled with biases that humans have been exhibiting over the past years. Therefore, the AI-based machines that are supposed to bridge the gap through equitable lending end up expanding that gap further. A simple way to remove the bias is to make the data “discrimination-free” before it is entered in the machine. Various factors should be analysed first and then entered into the machine only if they are relevant and able to explain the data adequately. For example, if the sample data suggests that fewer numbers of loans are given out to people in their twenties then, the machine would end up making the same bias even after taking relevant factors into account. To avoid this bias, the bank could use AI to spot these patterns and correct them by altering the data artificially to compensate for the changes that have taken place over time and removing the factor of age as a relevant one in the process of lending. This would provide a sense of equity in the fed data as the decision of lending would only depend on the financials of the person and not the age, unlike the traditional standards. This process removes prejudice and exclusion biases at the same time. But even after making the data free from irrelevant factors, the remaining data may still not represent the scenarios that a bank may face. This sample bias can be removed by exposing the data from “stress” to “calm” scenarios so that the data is evenly distributed among every possible circumstance and the machine would compare all the scenarios with the ability of the potential borrower and then formulate the final output. Following these processes could make the machines “fair” and enable equitable lending.



All in all, it can be said that using AI in the lending process is not just an inflated hype but a reality. Banking is a risky business due to the presence of high credit risk but, using AI lowers that credit risk and makes banking a profitable business which in turn maintains the appropriate amount of much-needed liquidity in the economy. After all, who wouldn’t like stable economic conditions?



References

[1] PRSIndia. 2020. Examining The Rise Of Non-Performing Assets In India. Available at: https://www.prsindia.org/content/examining-rise-non-performing-assets-india [2] Paisa Bazaar, “CIBIL Vs Experian Vs Equifax Vs Highmark Credit Score & Report,” Compare & Apply Loans & Credit Cards in India, August 27, 2020, https://www.paisabazaar.com/credit-score/cibil-vs-experian-vs-equifax-vs-highmark/



The Indian Learning, e-ISSN: 2582-5631, Volume 1, Issue 2, January 31, 2021.

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