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Debt Collections: Using Business Intelligence to Improve Efficiency

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The management of non-performing assets (NPAs) or delinquencies is the most pressing issue for Indian banks and non-banking financial institutions (NBFCs). Despite rigorous credit decisioning level checks and substantial reliance on credit bureau scores, lending institutions face a 10% to 20% (in some cases even higher) failure rate from retail clients who do not make payments on time. While overall lenders’ net nonperforming assets (NPAs) range from 3% to 8%, the figure for retail asset consumers can be as high as 20%. The most important advances in renewing collections operations to improve performance at a lower cost are being enabled by powerful digital innovations like advanced analytics and machine learning.

We, at Conneqt Business Solutions Limited, are assisting several Indian banks and NBFCs in improving collection efficiencies and lowering NPA levels. We are the largest and most well-organized third-party collection agency in India. We are supported by Field Officers who cover 94 percent of the country’s pin codes, in addition to state-of-the-art tele collecting capabilities.

Business Intelligence (BI) has been used by Conneqt for a long time. Our collection team employs the expertise of an in-house Data Science team for customer segmentation, and multiple algorithms are employed to design customer follow-up strategies.

Each Customer Is Different
The requirements for an early-stage debt collection differ from those for a mid- or late-stage debt collection approach. Our extensive expertise in handling a wide range of asset classes, from small-ticket consumer loans to unsecured personal loans, secured assets, farm, tractor, and SME/MSME, has taught us that each product and delinquency stage requires a unique solution.

Segmentation Approach
Conneqt’s BI team can accurately estimate customer segmentation based on propensity to pay and various payment methods. While the consumer is still in the early stages of delinquency, the goal is to contact them as quickly as possible using a communication method that suits them best. For a late-stage customer, on the other hand, the issue is one of abundance (of customers). We’ve used AI and machine learning to solve the problem of determining the mode of a consumer contact channel with great success. This method not only aids in increasing collection efficiency but also aids in lowering collection costs significantly. At the same time, providing customers