CASE STUDY

Risk Modeling Increases Collection Revenues by US$ 8 Million for a Financial Services Organization

Sep 22, 2016

Business Challenge

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

A leading financial services organization wanted to create risk profile of its existing and future large loan accounts, to optimize collection efforts and reduce defaults.

Situation: Segmentation of good and bad loan accounts, to optimize collection

The client wanted a scorecard based model for its large loan accounts, to identify and segment the good accounts with higher likelihood of regular payment, from potential bad accounts with high potential for defaults, in order to reduce credit risk, credit losses and develop a targeted collections approach.

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Solution

Risk modeling and early warning signals tracking followed by creation of risk scorecard

We used logistic regression on the historical credit and loan 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 risk modeling and early warning signals tracking on the customer information, and created a risk scorecard/ scoring model for all loan accounts.

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Impact

Reduced payment defaults by 50% and improved collection revenue by USD 8 million

The client utilized the scoring model in its credit extension decisions and was able to cap defaults by 50%. The client also identified potential defaulters and optimized its collection efforts to boost collection revenues by USD 8 million.

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