The epidemic has had a severe economic impact on India. However, news of a new vaccination programme and declining caseloads in hotspots around the country suggest that the future is looking brighter. During this critical moment, many people are turning to banks for loans in order to expand and stabilise their businesses. If banks and non-banking financial firms (NBFCs) enhance their digital infrastructure, application processing times and loan acceptance levels will improve, making capital available to the individuals most capable of moving the economy ahead. Getting loans on the books is no longer the main element of banking. When a bank authorises a loan, it is required to keep the borrower until all outstanding debts are paid in full. This is why banks perform extensive financial background checks on borrowers before approving loans. This is referred to as determining creditworthiness. Even yet, the payback time for a loan might be years, and each additional year raises the danger. A borrower, for example, may lose his or her employment and so fail to repay their debt. Banks incur losses when a certain amount of loans default. Despite the initial due diligence to guarantee the lowest possible risk, things might go wrong before the loan is returned. This is why there is such a high need for data-driven software in the banking sector, particularly in India. Even today, many government banks utilise outdated systems to manage financial accounts, which is both time-consuming and inefficient. Lending is a huge data problem, therefore it's a perfect fit for machine learning, especially when human data collection becomes insufficient in the long run. Banks gather a range of information from borrowers, including salaries, collateral, assets, and so on. This information may be used to calculate the chance of the borrower repaying the loan on schedule. Sorting through a big stack of papers every time you require information about these borrowers, on the other hand, is time-consuming and labour-intensive.
Artificial Intelligence (AI) - powered software can make data processing more efficient and straightforward. This may be accomplished by automating request management depending on resource usage, resulting in a more reliable and trustworthy system capable of prioritising queries and eliminating manual database administration and monitoring. Banks can benefit from automated debt recovery technologies that make debt collection easier. They save time by giving a quick summary of the customer's borrowing history and sending automatic loan repayment and tracking reminders. Instead of pursuing debtors, banks may now concentrate on other important responsibilities. Although banks undertake thorough credit assessments before issuing loans, they cannot continually supervise the entire process, therefore borrowers must be monitored on a frequent basis. It aids in determining whether loans are likely to become stressed or default, resulting in losses. As we've shown, AI can improve borrower's credit scores by doing a 360-degree review of their entire financial management. This analysis can also assist banks to reduce their debt recovery load by automating routine procedures and guaranteeing timely payments.