Enhancing loan portfolio management through multi-class classification of credit risk: a case of Kenyan financial institutions

Abstract

The effective management of loan portfolios and credit risk is crucial for the financial stability of lending institutions. However, recent economic challenges in Kenya have heightened loan default rates, underscoring the need for improved credit risk assessment processes. Traditional methods are increasingly inadequate in addressing evolving market dynamics, prompting some lenders to explore advanced techniques such as machine learning and predictive analytics. Despite their potential benefits, the adoption of these advanced techniques remains limited, particularly among smaller financial institutions. In response to these challenges, this study developed a predictive tool for multi-class loan classification to enhance credit risk assessment and loan portfolio management. Several machine learning algorithms were compared, with XGBoost emerging as the most effective model. The study also evaluated the use of the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance, which improved classification of minority risk categories. The proposed prediction tool aligns with regulatory guidelines and offers a practical solution for lenders to strengthen credit risk monitoring and decision-making, contributing to the resilience and sustainability of financial institutions in Kenya.

Keywords: Credit Risk, Loan Portfolios, Lending Institutions, Loan Default Rates, Machine Learning, Predictive Analytics, Multi-Class Loan Classification, XGBoost, Synthetic Minority Oversampling Technique (SMOTE), Data Imbalance, Decision Making, Kenya