Predictive Analytics for Personalized Debt Management: Leveraging Machine Learning to Provide Actionable Financial Advice

Abstract

In today's complex financial environment, managing personal debt effectively has become a significant challenge, often leading to increased loan defaults. This study aims to develop a machine learning-based framework for personalized debt management by analyzing borrower data to identify risk levels and offer tailored financial advice. The scientific novelty of this research lies in its integration of both supervised and unsupervised learning techniques to gain deeper insights into the characteristics of defaulters and predict their risk levels. This methodology improves prediction accuracy and interpretability and applicability in real-world lending. The study offers actionable strategies for debt reduction, optimized spending, and personalized financial planning based on risk profiles. The findings can support financial institutions in refining credit risk assessment models, promoting responsible lending, and contributing to the achievement of broader sustainability goals through improved financial inclusion and stability. Unsupervised learning techniques, such as K-nearest neighbors (KNN), DBSCAN, and rule-based methods, were applied to cluster defaulters based on their risk profiles. These clustering methods allowed us to distinguish various groups of defaulters, providing a nuanced view of risk categories. Financial institutions can use these risk categories to design tailored financial products and adjust lending strategies and policies for lower-risk groups or offer guidance to higher-risk defaulters on areas needing improvement, such as increasing income or enhancing credit scores. Governments in developing countries could make the most use of this study, where most of the population lacks financial knowledge and struggles to get financial help from private institutions once they are categorized as defaulters. For instance, insurance companies have different policies for different age groups, and financial institutions can also make such policies for different risk levels that benefit both parties in the long term.

Received: 4 March 2025 | Revised: 20 June 2025 | Accepted: 2 July 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support this work are available upon reasonable request to the corresponding author.

Author Contribution Statement

Amir Ahmad Dar: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration. Trushal Hirani: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Vanshita Arora: Writing – review & editing, Visualization. Sukriti Kakkar: Writing – review & editing, Visualization.