Early Warning System: Loan Default Predictions for Lending

Banks using Early Warning Systems are a step closer to unlocking the potential of Advanced Analytics

As per the Reserve Bank of India (RBI) financial report, the banking system of India has encountered 6,500 incidents of fraud worth 30,000 crores in 2019 alone. Adding to these stats, Non-Performing Assets (NPAs) ratio was predicted to rise from 8.5% in March 2020 to 12.5% by March 2021.

The lack of due diligence by lenders is one major contributing factor to these frauds.

An Early Warning System (EWS) driven by Artificial Intelligence (AI) predicts, prevents, and prepares the Indian banking system for mitigating risks in credit. It also enables lenders to track external causes and their effects before it manifests in loan books.

How Do Early Warning Systems Prepare Banks for Loan Defaults?

A comprehensive predictive analytics feature analyzes innumerable operational events during the entire lending cycle. EWS analyzes different patterns during borrowers’ tenures and uses big data analytics to generate rich, deeper, and meaningful reports on borrower behavior.

Big Data and Predictive Analytics Capabilities

Big Data Analytics (BDA) collects, organizes, processes and analyzes large, diverse (structured and unstructured) and complex sets of data. Predictive Analytics helps predict future events mainly referring to specific types of analysis on current activities, market standing, and backward-forward linkages.

These bring transparency and help minimize risks, but more importantly help improve customer experience with foresight into portfolio performance.

Based on the banking insights, the analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency. This strategic potential let’s businesses explore new revenue streams and gain competitive advantages.

One-stop Risk Management for Lenders

Financial Entities conduct an in-depth analysis of a borrower’s past behavior to get an insight and assess the level of risk involved in the loan process. However, this has become obsolete as additional internal and external factors contribute to risk.

When EWS is implemented, accurate data (internal or external) is feed to provide Forward/Backward linkages. The system provides a backward industry and forward industry impact analysis that creates multiple visualizations and insightful reports for the benefit of the lender’s credit evaluation process.

24/7/365 Borrower Account Monitoring

Analyze the statements in real-time, by aggregating all the business stakeholders/applicant’s accounts. To streamline credit evaluation, the system automatically identifies and classifies different kinds of transactions, running in-built fraud detection checks.

This results in a precise report about the inflows and outflows and the average monthly balance, among other things. The framework also identifies monthly, weekly, and quarterly patterns of transactions made by the borrower through his/companies’ bank accounts.

Backward and Forward Industry Linkages

After a loan is sanctioned, Early Warning Systems check the company status vis-à-vis its Backward and Forward linkages and verifies the five parameters, i.e. Transactional, Financial, Non-Financial, External and Statistical, and generates a Portfolio Rank/EWS Score to help decide on the loan process and amount.

Factoring News Alerts and Social Media

Interactive dashboard available in the EWS system to view borrower wise details. Heat map on risk categorization on all borrowers is provided to easily identify the ones that required immediate attention.

Detailed borrower wise EWS reports are available and executive summary is provided to know the gist of the cases. Also the system takes data from multiple sources that include bank’s internal data, external public data, news and tweets, etc.