Early Warning System: Loan Default Predictions for Lending

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

In the financial year 2021, the Reserve Bank of India (RBI) reported bank frauds amounting to INR 1.38 trillion.

A lack of due diligence by lenders is one major contributing factor to losses from credit accounts and fraud. Here, an Early Warning System (EWS) driven by Artificial Intelligence (AI) and Machine Learning (ML) predicts, prevents, and prepares lenders for credit risk.

Apart from constant monitoring of a company's transactions and reported financials, the system enables lenders to track external causes of stress and its effects on accounts before it manifests in loan books.

How Do Early Warning Systems Prepare Banks for Loan Defaults?

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

The system is based on the concept of Early Warning Signals. These signals are generated based on a number of triggers computed using statistical models, mainly emanating from - transactional, financial, non-financial, external (alternative), and statistical data. Here's how.

Enables Big Data Monitoring for Predictive Analytics

Big Data Analytics (BDA) collects, organizes, processes, and analyses large, diverse (structured and unstructured) and complex sets of data. It helps build systems for 'Predictive Analytics' to predict future events using data on a borrower's current activities, market standing, and backward-forward linkages.

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

Based on the banking insights, the analytics process, including the deployment and use of BDA tools, improves operational efficiency. This strategic potential lets Banks and Financial Institutions (FIs) explore new revenue streams and gain a competitive advantage.

Monitors Regulator-prescribed Borrower Activity

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 business-related internal and external factors of borrower's accounts can now be monitored.

When EWS is in place, accurate data for Forward-and-Backward linkages are also monitored. The system provides a backward industry and forward industry impact analysis that creates multiple visualizations and insightful reports for a holistic credit evaluation.

Automated 24x7x365 Borrower Account Monitoring

The solution helps analyze financial statements in real-time, by aggregating all the business stakeholders' account details. To streamline credit evaluation, the solution automatically identifies and classifies different kinds of transactions, running in-built potential fraud detection checks.

This results in a precise report about 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 the individual's company bank accounts.

Uses Alternative Data - News Alerts and Social Media

Detailed borrower-wise EWS reports are available and an executive summary is provided to know the gist of the cases. For a comprehensive outlook on borrower activity, the system also sources data from multiple alternative data sources that include external public data, news, tweets, social media activity, etc.

Interactive dashboards are available in the EWS system to view borrower-wise details. Heat maps on risk categorization on all borrowers are provided to easily identify the ones that required immediate attention.