The repayment period of a loan can be several years and each additional year increases the risk.
By Amit Das, co-founder and CEO of Think360.ai
The pandemic has had a significant impact on the economic situation in India. The announcement of a new vaccination plan and the drop in the number of cases in hotspots across the country, however, indicate that the future looks brighter.
During this critical time, many people are looking to banks for loans in order to grow and stabilize. If the digital infrastructure of banks and non-bank financial corporations (NBFCs) were improved, application processing times and loan acceptance levels would increase, making liquidity available to those most able to move the business forward. ‘economy.
Now, getting loans off the books is just one aspect of banking. Once a bank has approved a loan, it must withhold the borrower until he has paid all dues. That is why banks do a thorough financial background check of borrowers before granting loans to them. This process is known as determining creditworthiness.
Even so, the repayment period of a loan can be several years and each additional year increases the risk. For example, a borrower may lose their job and therefore fail to repay their loan. When a certain number of loans default, banks are at a loss. Despite the initial due diligence to ensure minimum risk, things can go wrong before the loan is paid off.
That is why there is a huge need for data-based software in the banking industry, especially in India. Even today, many government banks still use legacy systems to manage financial accounts, a time-consuming and inefficient approach.
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Why is it necessary to monitor loan repayment in India?
According to a study, per capita credit increased from Rs 37,802 in FY12 to Rs 73,637 in FY19. This is a whopping 94.79% increase in seven years, which means more and more Indians are feeling comfortable taking loans today.
Many businesses finance loans based on their credit card history, but a large majority of Indians do not use credit cards to this day. While the number of credit card users in India in 2019 reached 52 million, there are only about 3 credit cards per 100 people in India. For this reason, Indian banks face a big hurdle in determining the creditworthiness of NTC (new 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 income.
This is why the monitoring of loan repayments becomes essential.
There is a growing need for a data driven approach in the loan 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 their lending decisions.
Let’s take a look at some common issues with traditional systems and how banks can solve them by taking a data-driven approach.
Difficult debt collection
NBFCs and banking institutions are subject to strict control by regulatory authorities, so they must implement debt collection strategies to achieve a steady stream of income. However, due to manual processes, banks and NBFCs tend to overlook the development of such strategies. As a result, when a loan repayment is not monitored, it can wreak havoc throughout the financial system. Therefore, using understandable data is the key to improving collection rates.
This could possibly be done by identifying behavioral repayment trends and anomalies, and implementing digital logic to develop an unbiased solution. These media have started to show faster recovery when data is validated from different jump tracking sources to simplify the process. They also incorporate ML algorithms to engage customers through hyper-personalized content and move them forward towards reimbursement. It is a fast method for dealing with frictionless debt collections.
Inefficient data processing
Lending is a big data issue, which makes it naturally suitable for machine learning, especially since manual data recording becomes insufficient in the long term. Banks collect various data from borrowers such as salary, guarantees, assets, etc. This data can be used to estimate the likelihood that the borrower will be able to repay the amount on time. But sorting through a thick stack of papers each time you want information on said borrowers takes time and manpower.
AI-based software can make data management efficient and intuitive. This can be implemented by automating the management of requests based on resource consumption, providing a more stable and reliable system that can prioritize requests, and reducing manual control and monitoring of the database.
How can AI transform the loan repayment system?
Artificial intelligence is rapidly developing many technological tools that influence many processes at the same time. That being said, incorporating artificial intelligence into the structuring of loan repayments can streamline tedious processes and dramatically improve the customer experience. This in turn helps banks dramatically reduce the time spent automating manual and repetitive administrative tasks as well as lower labor costs.
Banks determine the value of the majority of loans by determining the likelihood that the borrower will be able to repay the loan. Determining personal creditworthiness is essential for both banks and the financial sector as a whole. Accurately evaluating large amounts of information for a good risk assessment is much easier with AI. Early warning signals are one of the powerful applications used in credit risk management to identify entities that are exposed to a higher risk of default.
Automated debt collection
Automated debt collection tools can make debt collection easier for banks. They save time by instantly providing a summary of the customer’s loan history and sending automated reminders for loan repayment and follow-up. Now, instead of chasing borrowers, banks can focus more on core tasks.
Correct data processing
Data is a powerful asset for banks. It can be obtained from a very wide range of touch points, such as revenue sources, purchasing habits and overall financial behavior of customers. Using AI-powered software, banks can leverage this data to find hidden information, provide fair loan interest, and understand borrower history for cross-selling products and more. The more data you have on an individual, the more you can use it to access their creditworthiness.
Early warnings to reduce the impact of bad debts
Real-time analysis of a wide range of customer-specific data points can dramatically reduce the impact of bad debts on banks by enabling them to take action on warning signs. If a person, for example, stops paying rent or drastically reduces their monthly food expenses, data-driven tools can identify them and alert banks to the possibility of default.
Lenders are subject to strict oversight by regulatory authorities. Minor mistakes can have serious repercussions. In such cases, AI can remind you of potential compliance issues, which can save financial institutions from heavy fines and financial disasters.
Although banks do full credit reviews before granting loans, they cannot constantly monitor the entire process, making regular monitoring of borrowers essential. This helps to assess which loans may be under stress or which may default, causing losses.
As we saw above, AI can strengthen a borrower’s credit rating by providing a 360-degree analysis of their overall financial management. This analysis can further help ease the debt collection burden for banks by automating manual tasks and ensuring timely payments.
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