Thesis Abstract

Abstract
With the advancement of technology and the increasing availability of data, the banking sector has embraced machine learning algorithms to enhance various processes, including credit risk assessment. This thesis focuses on the application of machine learning algorithms to predict credit risk in banking institutions. The research aims to develop a model that can accurately assess the creditworthiness of customers, thereby assisting banks in making informed lending decisions and minimizing potential financial losses. The study begins with an exploration of the existing literature on credit risk assessment, machine learning algorithms, and their application in the banking sector. A comprehensive review of relevant studies provides a foundation for understanding the current state of the field and identifying gaps that this research aims to address. The methodology chapter outlines the research design, data collection methods, and the machine learning algorithms selected for credit risk prediction. The research methodology involves the collection of historical loan data from a banking institution, preprocessing the data, and training various machine learning models to predict credit risk. The performance of each model is evaluated based on metrics such as accuracy, precision, recall, and F1-score. The findings chapter presents the results of the credit risk prediction models developed in this study. The analysis demonstrates the effectiveness of machine learning algorithms in predicting credit risk and highlights the key factors that contribute to accurate risk assessment. The discussion delves into the implications of the findings for banking institutions and the potential benefits of adopting machine learning tools for credit risk management. In conclusion, this thesis provides valuable insights into the application of machine learning algorithms for credit risk prediction in the banking sector. The research contributes to the existing body of knowledge by demonstrating the efficacy of machine learning models in enhancing credit risk assessment processes. The findings of this study have practical implications for banking institutions seeking to improve their risk management practices and make more informed lending decisions. Overall, this research underscores the importance of leveraging machine learning algorithms to predict credit risk effectively, thereby enabling banks to mitigate potential financial losses and maintain a healthy loan portfolio. The study sets a foundation for further research in this area and offers recommendations for the practical implementation of machine learning solutions in credit risk management within the banking industry.

Thesis Overview