A Multilayered Approach to Classifying Customer Responsiveness and Credit Risk
This is an incremental improvement for businesses in credit risk and marketing, using existing methods on specific data to optimize performance metrics.
The study tackled the problem of classifying customer responsiveness to credit card mail campaigns and credit risk, finding that Extra Trees achieved 79.1% recall for responsiveness, Random Forest had 84.1% specificity for risk, and Random Forest reached 83.2% accuracy in a combined model.
This study evaluates the performance of various classifiers in three distinct models: response, risk, and response-risk, concerning credit card mail campaigns and default prediction. In the response model, the Extra Trees classifier demonstrates the highest recall level (79.1%), emphasizing its effectiveness in identifying potential responders to targeted credit card offers. Conversely, in the risk model, the Random Forest classifier exhibits remarkable specificity of 84.1%, crucial for identifying customers least likely to default. Furthermore, in the multi-class response-risk model, the Random Forest classifier achieves the highest accuracy (83.2%), indicating its efficacy in discerning both potential responders to credit card mail campaign and low-risk credit card users. In this study, we optimized various performance metrics to solve a specific credit risk and mail responsiveness business problem.