Originally published by Quantzig: Credit Risk Modelling Helps Telecom Client Realize 50% Reduction in Bad Debts
Telecom Analytics Business Challenge:
A prominent telecom operator faced the challenge of predicting payment behavior among its customers, aiming to optimize collection efforts and mitigate defaults. The absence of a robust solution for classifying customers based on their payment behavior prompted the need for a tailored approach.
Situation:
The telecom operator lacked an efficient method to differentiate between customers with a high likelihood of regular payment and those prone to defaults. To address this, they sought to develop a scorecard-based model to manage credit risk effectively and tailor collection strategies.
Credit Risk Modelling Solution/Approach:
Utilizing credit risk modelling and early warning signals tracking, our team embarked on a comprehensive solution. By analyzing historical payment data through regression modeling, we identified key triggers indicative of payment defaults. Cross-tab analysis facilitated mapping these triggers to future events, enabling the creation of a risk scorecard for all customer accounts.
Impact of Credit Risk Modelling:
The implementation of our credit risk modelling solution yielded significant results. Payment defaults were reduced by a notable 50%, leading to enhanced revenues for the telecom operator. Leveraging the scoring model, the client made informed credit extension decisions, identified potential defaulters, and optimized collection efforts, resulting in improved collection revenues.
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