Enhancing Debt Recovery in Financial Institutions: A Delinquency String - Based Approach for Customer Risk Segmentation

Abstract

Collection is the most important task for any financial Institution. This research paper suggests a unique approach to classify customers into different risk bucket by using delinquency string - based approach. By leveraging delinquency string, we have optimized collection strategies within the context of financial institution. We have developed a comprehensive framework for categorising customers into 5 different risk segments: “No Risk”, “Low Risk”, “Medium Risk”, “High Risk” and “Very High Risk”. The Main objective of this system is to enhance the efficiency and effectiveness of debt recovery efforts. Every risk category has a set of collection strategies associated with it results in minimizes loss and optimizing resource allocation. Methodologies presented in this paper have potential to reduce the collection efforts by stopping customers to move to higher risk bucket while simultaneously improve customer satisfaction. Purpose: The purpose of this article is to present a novel methodology for categorizing customers into different risk segments in financial institutions using delinquency strings, aimed at enhancing the efficiency and effectiveness of debt recovery efforts. Significance: The significance of this research lies in its potential to transform debt recovery in financial institutions by implementing a data - driven approach to classify customer risk, thereby improving resource allocation and reducing financial losses.