50% Reduction in Bad Debts Achieved by a Telecom Operator by Using Credit Risk Modeling

Sep 22, 2016

Business Challenge

Developing a risk scorecard to predict the payment behavior of existing and future loan accounts.

A leading telecom operator wanted to create a risk profile of its existing customers, to optimize collection efforts and reduce defaults.

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Lack of a robust solution for classifying the customers based on payment behavior.

The client wanted to develop a scorecard based model for its customers to identify and separate the good accounts with a higher likelihood of regular payment, from potential bad accounts with high potential for defaults, for managing credit risk, bad debts and developing a targeted collections approach.


Credit risk modeling and early warning signals tracking followed by the creation of a risk scorecard.

We used regression modeling to assess the historical payment data to identify the key triggers and features indicating payment defaults. We used cross tab analysis to map the triggers to future events followed by credit risk modeling and early warning signals tracking on the customer data and created a risk scorecard/ scoring model for all customer accounts.

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Reduced payment defaults by 50% and improved revenues.

The client utilized the scoring model in its credit extension decisions, to cap defaults by 50%. The client also identified potential defaulters and optimized its collection efforts to achieve improved collection revenues.

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