Predictive Modeling for Credit Risk Assessment in Banking Using Machine Learning Algorithms

Thesis Abstract

Abstract
The banking sector plays a critical role in the economy by providing financial services and facilitating economic growth. With the increasing complexity of financial transactions and the rise in non-performing loans, accurate credit risk assessment is essential for maintaining financial stability. Traditional credit risk assessment methods have limitations in dealing with the dynamic nature of credit risk. This research project focuses on developing a predictive modeling framework for credit risk assessment in banking using machine learning algorithms. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for the study, emphasizing the importance of accurate credit risk assessment in banking operations. Chapter 2 conducts a comprehensive literature review on credit risk assessment, machine learning algorithms, and their application in the banking sector. The review explores existing studies, frameworks, and methodologies related to credit risk assessment and machine learning, providing a theoretical background for the research. Chapter 3 outlines the research methodology adopted in this study, including data collection methods, data preprocessing techniques, feature selection, model development, model evaluation, and validation strategies. The chapter details the steps involved in building the predictive modeling framework for credit risk assessment. Chapter 4 presents an in-depth discussion of the findings from the application of machine learning algorithms in credit risk assessment. The chapter analyzes the performance of different algorithms in predicting credit risk, identifies key factors influencing credit risk, and evaluates the effectiveness of the predictive modeling framework developed in this study. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research results for the banking sector, highlighting the contributions of the study, and suggesting areas for future research. The chapter emphasizes the significance of accurate credit risk assessment in enhancing financial stability and improving decision-making processes in banking operations. Overall, this research project contributes to the existing literature by developing a predictive modeling framework for credit risk assessment in banking using machine learning algorithms. The findings of the study have practical implications for banks and financial institutions in improving credit risk management practices and enhancing financial stability in the banking sector.