Early Warning Systems: Is AI the Future of Banking?
With bad loans stretching as far as Rs. 14.6 lakh crore in India, the need for technological intervention is at its prime. Banks today not only need critical IT infrastructures to power their users with convenience but also need systems to protect their interests.
This calls for artificial intelligence (AI) based solutions that detect/ predict non-performing assets (NPAs) in advance and save banks from possible hassles. One such technology that has proven to be beneficial in this sector is the Early Warning System (EWS).
Today, we explore what EWS is and how things can change for good with this application.
So, What Exactly is EWS?
Early Warning System or EWS is an AI-based application that feeds on previous transactions and highlights possible bad loans. The system analyses hundreds of different signals before making any conclusion. The results are promising and hence could not be ignored. McKinsey report highlights that with EWS, a loan-loss contingency could decrease by as much as 10%-20%.
Why is Modern EWS Important?
Diving deep into the world of EWS, we discover the multiple facets of the system. Some of the key reasons why EWS plays a pivotal role in today’s banking environment are as follows.
In 2015, The Reserve Bank of India ruled out a hard lined stance between processes and systems. This includes compliance requirements of Early Warning Systems as well. In the process, the non-performers were dragged into limelight before even they defaulted and hence aroused the interests of banks in these advanced technologies.
The traditional model of just wait and watch has now a new competitor. Banks could now detect fraudulent activities along with a myriad of features all in real time. This has now emerged as a major trend where financial institutions have implemented EWS systems to better track their resources and avoid strain while recovering loans.
How Does EWS Systems Work?
Now that we clearly understand the ins and outs of the Early Warning Systems, it is critical to understand the functioning of EWS systems. Here is how the system works and how data plays a pivotal role for the system:
Data: The AI-powered EWS systems run on data. Based on its origin, the data could be categorized into two major categories. These categories are a) Self-Reported like salary structure, existing loans, and other factors and b) Data sourced formed external resources.
The self-reported factors are now even more optimized by the use of modern day technologies. For instance, banks can now track the buying behavior across the internet or the purchase pattern and gauge savings through mobile banking services. Technologies like UPI now know how much exactly you spend, where you spend, and when through its state of the art algorithms.
The data sourced from external resources include credit card ratings, CPI reports, home price indices, and several others. These sources combined with a dynamic architecture ensure a robust solution that solves problems in money lending to a great extent.
Reviews: With hundreds of thousands of loans impending, it is quite a difficult task for banks to monitor individual accounts and automate loan retrieval systems. While loan retrieval could be automated with time, the data signals and the behavioral patterns involved in the same could indicate a ton of inferences. Hence, EWS systems solve the issue by analyzing the data points and monitoring each account to its precision. The real-time account monitoring system plays the most important part as it could raise red flags the very moment it identifies issues.
Prediction: One of the strongest signals that EWS systems rely on is the prediction of the defaults. The system relies on hundreds of sources for data and employs modern-day algorithms to get accurate results. To add further, the accuracy improves with time and hence is one of the promising software of our time. The system’s critical data depends upon changing market dynamics, organizational restructuring, and policy changes.
Insights: Most repayment systems are designed to detect abnormalities in repayment patterns to associate the risk to it and take necessary action with due course of time. However, the modern-day EWS systems go one step further and dig into data like customer’s spending patterns, personal savings, and overall financial performance of an individual. This, in turn, provides more accurate results and differentiates a “good” borrower from a “bad” borrower.
When it comes to EWS, the system prevents operational overheads, warns possible defaulters, and stays ahead of the competition in several ways. Moreover, the system is designed for traditional institutional setups and involves emerging solutions like peer-to-peer lending and hence could not be ignored.
Benefits of Having an EWS System in Place
● Reduction in Default Rate: EWS tracks and monitors loans at the portfolio level. A red flag could excite bank officials to take necessary measures to avoid defaults and thus, reduce default rate substantially. For instance, financial institutions could now put psychological strain through repeated automated messages and calls once they detect a red sign.
● Stricter Agreements: EWS systems enable lenders to gauge a loan’s financial viability and hence allow the lender to have higher collaterals. In the event the loan defaults, the lender could recover its money through the collateral assets, thus, minimizing losses by a greater extent.
● Deciding Portfolio Composition: The exposure to multiple industries and access to high-grade data points enable banks to identify threatened sectors early in the process. Thus, this helps banks decide whether they should invest in a sector or not and, if yes, based on what propositions.
● Safeguarding The Interests of the Bank: Several negative signals combined with human intuitions can help banks recover their loans from borrowers. The process is much easier with advanced EWS solutions. Banks can sell the assets of the stressed accounts to avoid losses.
The Bottom-line
The endless advantages of EWS systems have propelled the growth story of the software. The advanced possibilities of the systems have worked out in favor of financial institutions like Urban Cooperatives, NBFCs, and Regional Rural Banks who traditionally lived under the shadow and met losses with almost every gamble.
This, hence, has become essential for banks and private lenders to have the system in place to avoid unwanted losses in the future. That being said, the software is not limited to be used by banks alone. The emergence of credit lending startups and peer-to-peer programs could benefit from the software as well.
