Predictive modeling of customer credit risk in retail banking using machine learning techniques

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the application of machine learning techniques in predictive modeling of customer credit risk in retail banking. The aim of this research is to develop a predictive model that can effectively assess and predict the credit risk associated with individual retail banking customers. The study focuses on utilizing machine learning algorithms to analyze historical customer data and identify patterns that can be used to predict future credit risk. The research begins with an extensive review of existing literature on customer credit risk assessment, machine learning techniques, and their applications in the banking sector. The literature review highlights the importance of accurate credit risk assessment in retail banking and the potential benefits of using machine learning algorithms for this purpose. The methodology chapter outlines the research design, data collection methods, and the machine learning algorithms selected for the study. The research methodology includes data preprocessing, feature selection, model training, and evaluation techniques to develop an effective credit risk prediction model. The findings chapter presents the results of the study, including the performance evaluation of the developed predictive model. The findings demonstrate the effectiveness of machine learning techniques in accurately predicting customer credit risk in retail banking. The discussion chapter provides an in-depth analysis of the results, discussing the implications of the findings and their relevance to the banking industry. In conclusion, this thesis contributes to the existing body of knowledge by demonstrating the potential of machine learning techniques in improving credit risk assessment in retail banking. The study highlights the importance of leveraging advanced analytical tools to enhance the accuracy and efficiency of credit risk prediction models. The findings of this research can help banking institutions make informed decisions in managing credit risk and improving overall financial performance.