Application of Machine Learning Algorithms in Credit Scoring for Improved Risk Assessment in Banking

Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in credit scoring to enhance risk assessment within the banking sector. The study aims to address the limitations of traditional credit scoring models by leveraging advanced machine learning techniques to improve the accuracy and efficiency of risk assessment processes. The introduction presents the background of the study, highlighting the challenges faced by banks in accurately assessing credit risk and the potential benefits of incorporating machine learning algorithms into existing credit scoring frameworks. The problem statement identifies the gaps in current credit scoring practices and emphasizes the need for more sophisticated risk assessment tools to enhance decision-making processes within financial institutions. The objectives of the study are to evaluate the performance of machine learning algorithms in credit scoring, compare their effectiveness with traditional models, and provide recommendations for implementing these techniques in banking operations. The limitations of the study are also acknowledged, including data availability constraints and the need for further research to validate the findings. The scope of the study encompasses a comprehensive analysis of different machine learning algorithms, such as logistic regression, random forests, and neural networks, in credit scoring applications. The significance of the study lies in its potential to revolutionize credit risk assessment practices, leading to more accurate predictions and reduced default rates in lending activities. The literature review delves into existing research on credit scoring methodologies, machine learning applications in finance, and the advantages of utilizing advanced algorithms for risk assessment in banking. The research methodology outlines the data collection process, model development, and evaluation metrics used to assess the performance of machine learning algorithms in credit scoring. The findings from the study demonstrate that machine learning algorithms outperform traditional credit scoring models in terms of accuracy, sensitivity, and specificity. The discussion in Chapter Four elaborates on the implications of these results for banking institutions, emphasizing the potential cost savings and risk mitigation benefits associated with adopting machine learning techniques. In conclusion, this thesis highlights the transformative potential of machine learning algorithms in credit scoring for improving risk assessment practices in banking. By leveraging advanced analytics and predictive modeling, financial institutions can enhance their decision-making processes, reduce credit losses, and optimize lending strategies to achieve sustainable growth and profitability.