Predictive modeling of loan default risk using machine learning techniques in banking sector

Thesis Abstract

Abstract
The banking sector plays a crucial role in the economy by facilitating financial transactions and providing various services to individuals and businesses. One of the key challenges faced by banks is managing the risk of loan defaults, which can have significant financial implications. In recent years, advances in machine learning techniques have provided new opportunities to address this challenge by enabling the development of predictive models that can assess the likelihood of borrowers defaulting on their loans. This thesis explores the application of machine learning techniques in predicting loan default risk in the banking sector. The research aims to develop a predictive model that can effectively identify borrowers who are at a higher risk of defaulting on their loans, thereby helping banks make more informed lending decisions and reduce potential losses. The study focuses on analyzing a large dataset of historical loan information, including borrower characteristics, loan terms, and repayment behavior, to train and evaluate machine learning models for predicting loan default risk. The research methodology involves a comprehensive literature review to examine existing studies on loan default prediction, machine learning techniques, and their applications in the banking sector. The study then outlines the data collection process and preprocessing steps, including feature selection and engineering, to prepare the dataset for model development. Various machine learning algorithms, such as logistic regression, decision trees, random forest, and gradient boosting, are implemented and evaluated using performance metrics like accuracy, precision, recall, and F1 score. The findings of the study demonstrate the effectiveness of machine learning techniques in predicting loan default risk, with certain algorithms outperforming others in terms of predictive accuracy and stability. The results also highlight the importance of feature selection and model tuning in improving the performance of predictive models. The discussion section provides insights into the factors that influence loan default risk, such as borrower credit history, income level, loan amount, and economic conditions. In conclusion, this thesis contributes to the existing body of knowledge on loan default prediction in the banking sector by showcasing the potential of machine learning techniques to enhance risk management practices. The study underscores the significance of leveraging advanced analytics to improve decision-making processes and mitigate financial risks in lending operations. The implications of this research extend to financial institutions seeking to optimize their loan portfolio management strategies and enhance overall operational efficiency in a competitive banking landscape.