Monitoring LOC Activity: Which Systems Are the Need of the Hour?

New-age credit monitoring systems improve monitoring for all types of borrower accounts

Traditionally, loans are sanctioned based on available data on borrower behavior. Banks would determine companies’ qualifications for Line of Credit (LOC) on three factors the maturity of the business, the involved collateral, and the capacity limit - the capability of repaying the borrowed amount, and collateral.


But now, both regulators demand better, analytics-driven measures.


Regulators suggest, lending should be supported by modern day credit monitoring and risk mitigation data systems. These analytics systems, today, offer insights on potential for an account to default. The ask extends to involve line of credit (LOC) borrowers too.


Inclusion of Big Data: Helping Monitor Credit Lines Better


The fundamental advantage of line of credit (LOC) is its high flexibility. Borrowers can customize LOC expenditure to their particular needs, paying small amounts of interest on the amount borrowed instead of the entire credit line. Borrowers can also change their payments based on their budget and cash flow.


Most banks in India still use a compliance-driven factoring mechanism for lending. On the other hand, modern digital lenders are simplifying borrowers' repayment journeys with proactive account monitoring. Prescribed by regulators, newer guidelines for account monitoring give lenders the option to factor in available news and social media accounts too.


Here, predictive analytics, a major function of early warning systems (EWS), helps lenders with timely decisions for potential defaulters. Since LOCs are dynamic lending models, AI/ML-powered EWS solutions help companies source and process data quickly, from extensive systems for banking transactions made by the borrower and external data.

What is Helping Banks Lean On Advanced Analytics?


An in-depth analysis of a borrower’s past behavior can help predict only to a limit. With big data at the forefront, data analytics can help predict borrower behavior in real-time.


Along with market performance, trigger-based red flags for borrowers’ internal and operational activity help banks stay one step ahead of accounts showing incipient stress.


Here's how


Client’s Real-time Financial Data


Lenders can analyze real-time data that contributes to risk. The most important being

Transactional Triggers Early warning signals embedded in and emanating from a borrower's transactions recorded in a lender’s systems.


Financial Triggers – Financial information of borrowers normally not residing within the transactions recorded in bank systems. Sources: Certified borrower submissions, specialized data agencies, Registrar of Companies, Eqifax, etc.


External Triggers – based on market sentiments on the borrower, Group, Industry, Regulatory changes etc.


Statistical Triggers – considers the statistical analysis of borrowers' historical data, past performance, industry performance, etc. Going forward, predictions for potential defaults are based on statistical computations for Probability of Default (PD), Distance to Default (D2D), and Loss Given Default (LGD).


Highly Informative Data-Visualization


Multi-purpose, interactive dashboards of EWS solutions offer a detailed view of borrowers. Teams get access to heat maps for risk categorization on all borrowers and can easily identify the ones that require immediate attention.


Detailed borrower wise EWS reports are made available and executive summaries can easily be created for cases. Furthermore, as data stored in the system increases, Machine Learning (ML) modules for statistical computations improve in accuracy for predictions.


Quick Deployment of Advanced Analytics


Software as a Service (SaaS) deployment through cloud implementation breaks down data silos, improves team connectivity, and increases data visibility. An EWS solution integrates data through cloud-based applications and automates the generation of actionable insights.


When implementing a proactive credit risk reporting system like EWS, one significant step is implementation of cloud-based systems. Apart from quick deployment, it improves data access, tracking, and comprehensive reporting by offering multiple teams easy access to all borrower data.