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

New age credit monitoring systems offer upgrades for all types of active borrowal accounts

The financial crisis exposed the dire need for credit monitoring systems. Traditionally, banks would determine companies’ qualifications for Line of Credit (LOC) on 3 factors - the maturity of business, capacity limit (capability of repaying the borrowed amount) and collateral.


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


Lenders are expected to know the drill. Regulators suggest, lending should be supported by modern day credit monitoring and risk mitigation data systems. Analytics systems, today, offer insights on potential for an account to default and this ask extends to involve line-of-credit borrowers too.


Inclusion of Big Data: Helping Monitor Credit Lines Better


The fundamental advantage of line of credit 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 or cash flow.


Most banks in India still use a compliance-driven factoring mechanism for lending. On the other hand, modern digital lenders are changing the playing field with proactive control-based account monitoring. As prescribed by regulators recently, lenders have the option to monitor publicly available news, social media accounts, and related-industry performance.


Analyzing this information with advanced analytics, mainly early warning systems (EWS), helps lenders in timely decisions. Since LOCs are dynamic lending models, AI/ML-powered vigilance is the only solution. Apart from market performance, trigger-based red-flags for borrower’s internal and operational activity helps banks stay one step ahead of defaulters.

What is Helping Banks Lean On Advanced Analytics?

An in-depth analysis of a borrower’s past behavior is not enough to lessen risk today. With big data at the forefront, data analytics can help predict borrower behavior in real-time.


Client’s Real-time Financial Data


Apart from industry-related factors, 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 borrowers transactions recorded in a lender’s systems. Sources: Core Banking System, Credit Bureaus, Regulatory Data Bases (CFR, CERSAI, SEBI).


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.


Highly Informative Data-Visualization


Multi-purpose, interactive dashboards of EWS systems offer a detailed view of borrower’s details. 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 available and executive summaries can easily be created for cases. Also, the system displays data from multiple sources which include external public data, news and tweets, etc.


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. The system integrates data through cloud-based applications and to unifies information components.


When examining your existing credit MIS systems and implementing pro-active credit risk reporting 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 easy access to all borrower data.