How AI is helping in ensuring robust credit assessment, timely EMI payments

If digital infrastructure at banks and non-banking financial companies (NBFCs) is improved, application processing times and loan acceptance levels would increase.

How AI is helping in ensuring robust credit assessment, timely EMI payments
You can split the EMI and take income tax exemption under Section 80C and Section 24, individually.

By Amit Das, Co-founder, and CEO of

The pandemic has had a significant impact on India’s economic situation. News of a new vaccination plan and falling caseloads in hotspots across the country indicate that the future’s looking brighter, however.

During this critical period, many individuals are turning to banks for loans to grow and stabilize themselves. If digital infrastructure at banks and non-banking financial companies (NBFCs) is improved, application processing times and loan acceptance levels would increase, making cash available to the people most capable of driving the economy forward.

Now, getting loans on the books is only one aspect of banking. After a bank approves a loan, it has to retain the borrower until they clear all the dues. This is why banks conduct a thorough financial background check of borrowers before giving them loans. This process is known as determining creditworthiness.

Even so, the repayment period for a loan could be years, and every extra year increases the risk. For example, a borrower may lose his/her job, and hence, fail to repay their loan. When a certain number of loans default, banks end up at a loss. Despite the initial due diligence to ensure minimum risk, things can go wrong before the loan gets repaid.

This is why there is an immense need for data-driven software in the banking sector, especially in India. Even today, many government banks still use legacy systems to manage financial accounts, a time-consuming and ineffective approach.

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Why is there a need to monitor loan repayment in India?

According to a study, per capita credit rose from Rs 37,802 in FY12 to Rs 73,637 in FY19. That is a whopping 94.79 per cent increase in seven years, which means more and more Indians are getting comfortable taking loans today.

Many companies finance loans based on credit card history, but a large majority of Indians do not use credit cards till date. While the number of credit card users in India in 2019 touched 52 million, there are only about 3 credit cards for every 100 people in India. Due to this, Indian banks face a large hurdle in determining the creditworthiness of NTC (new to credit) customers. This means that they run the serious risk of lending to a borrower who might default, which becomes a significant threat to overall revenue.

This is why monitoring loan repayment becomes essential.

There is an ever-growing need for a data-driven approach in the loan lending system. Banks need to adopt new technologies and methods to create a less risky environment for debt collection. Eventually, financial institutions will be forced to rethink the way they make lending decisions.

Let us see some commonly faced problems with traditional systems and how banks can solve them by following a data-driven approach.

Difficult debt recovery

NBFCs and banking institutions come under the strict scrutiny of regulatory authorities, so they must have debt recovery strategies in place to get a steady stream of revenue. However, due to manual processes, banks and NBFCs tend to disregard drafting such strategies. As a result, when any loan repayment remains unmonitored, it can wreak havoc on the overall financial system. Hence using understandable data is the key to improving collection rates.

This could possibly be done by identifying behavioral repayment trends and abnormalities, and implementing numerical logic to develop an unbiased solution. These mediums have started to show faster recovery when data is validated from different skip tracing sources to simplify the process. They also incorporate ML algorithms to engage customers via hyper-personalised content and inch them towards repayment. It is a quick method of addressing debt collections without friction.

Inefficient data handling

Lending is a big data issue, which makes it naturally suited to machine learning, especially since manual data recording becomes insufficient in the long term. Banks collect a variety of data from borrowers such as salary, collateral, assets etc. This data can be used to estimate the likelihood of the borrower being able to repay the amount in time. But sorting through a thick stack of papers every time you want information on said borrowers is time and labor-intensive.

AI-based software can make data handling effective and intuitive. This can be implemented by automating request management based on resource consumption, providing a more stable and trustworthy system that can prioritise queries and reducing manual control and monitoring of the database.

How can AI transform the loan repayment system?

Artificial Intelligence is promptly developing numerous technological tools that influence many processes at one go. That being said, incorporating Artificial intelligence in structuring loan repayment can streamline tedious processes and vastly improve customer experience. This in return helps banks considerably reduce time spent by automating manual and repetitive administrative tasks as well as cut labor expenses.

Risk assessment

Banks determine the value of the majority of loans by determining how likely it is that the borrower can pay back the loan. Determining the creditworthiness of individuals is critical for both banks and the whole financial sector. Accurately assessing large quantities of information for good risk assessment is much easier with AI. Early warning signals are one of the impactful applications used in credit risk management to identify entities that are exposed to higher risk of defaults.

Automated debt recovery

Automated Debt Recovery tools can make debt collection easier for banks. They save time by instantly providing a summary of the customer’s borrowing history and sending automated reminders for loan repayment and tracking. Now, instead of chasing borrowers, banks can focus more on essential tasks.

Proper data handling

Data is a powerful asset for banks. It can be obtained from a very wide range of touch points, such as income sources, purchase patterns and overall financial behaviors of customers. Using AI-based software, banks can leverage this data to find hidden insights, provide fair loan interests, and understand borrower history for product cross-selling and so much more. The more data you have on an individual, the more you can leverage it to access their creditworthiness.

Early warnings to reduce the impact of bad loans

Real-time analysis of a wide range of customer specific data points can significantly reduce the impact of bad loans on banks by enabling them to take action based on early warning signs. If a person, for instance, stops paying rent or significantly cuts down on monthly food expenses, data-driven tools can identify this and alert banks about the possibility of default.

Regulatory Compliance

Lenders are under strict scrutiny from regulatory authorities. Minor mistakes can lead to severe repercussions. In such cases, AI can remind you about potential compliance issues, which can save financial institutions from heavy fines and financial disasters.


Although banks conduct full credit assessments before providing loans, they cannot constantly control the entire process, making regular monitoring of borrowers critical. It helps to gauge which loans can become stressed or which ones can be defaulted, leading to losses.

As we have seen above, AI can strengthen the credit assessment of a borrower by providing a 360-degree analysis of their overall financial management. This analysis can further help lessen the debt recovery burden on banks by automating manual tasks, and ensuring timely payments.

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