How to remove the bias for equitable lending and lesser defaults?
The Indian Learning | e-ISSN: 2582-5631 | Volume 1, Issue 2 (2021)
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-equifaxvs-highmark/
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THE INDIAN LEARNING/JANUARY 2021