Thesis Abstract

Abstract
The use of machine learning algorithms in credit scoring has gained significant attention in the banking sector due to its potential to enhance credit risk assessment and decision-making processes. This thesis explores the application of machine learning algorithms for credit scoring in banking, with a focus on improving the accuracy and efficiency of credit risk evaluation. The research investigates various machine learning techniques, including decision trees, random forests, support vector machines, and neural networks, to develop predictive models for credit scoring. The study begins with a comprehensive review of existing literature on credit scoring, machine learning, and their intersection in the banking industry. It examines the evolution of credit scoring methods, the challenges faced by traditional scoring models, and the advantages of utilizing machine learning algorithms for credit risk assessment. The research methodology section outlines the data collection process, feature selection methods, model development techniques, and performance evaluation criteria employed in the study. Empirical findings from the study indicate that machine learning algorithms demonstrate superior predictive power compared to traditional credit scoring models. The models developed using machine learning techniques exhibit higher accuracy, sensitivity, and specificity in identifying creditworthy borrowers and predicting default risks. The discussion of findings delves into the key factors influencing credit scoring outcomes, the interpretability of machine learning models, and the practical implications of adopting these models in banking institutions. The significance of this research lies in its contribution to advancing credit scoring practices in the banking sector through the integration of cutting-edge machine learning technologies. By leveraging the predictive capabilities of machine learning algorithms, banks can make more informed and data-driven credit decisions, leading to reduced credit losses, improved portfolio performance, and enhanced customer satisfaction. The study also highlights the importance of model transparency, explainability, and regulatory compliance in deploying machine learning-based credit scoring systems. In conclusion, this thesis underscores the potential of machine learning algorithms to revolutionize credit scoring in banking by offering more accurate, efficient, and reliable credit risk assessment tools. The findings of this research provide valuable insights for banking professionals, data scientists, and policymakers seeking to enhance credit risk management practices and drive innovation in the financial services industry. By embracing machine learning technologies for credit scoring, banks can stay ahead of the curve in a rapidly evolving financial landscape and achieve sustainable competitive advantages in the market.

Thesis Overview

The project titled "Using Machine Learning Algorithms for Credit Scoring in Banking" aims to explore the application of machine learning algorithms in the domain of credit scoring within the banking sector. Credit scoring is a crucial process used by financial institutions to evaluate the creditworthiness of potential borrowers, enabling them to make informed decisions regarding the extension of credit. Traditional credit scoring methods often rely on predefined rules and statistical models, which may have limitations in capturing complex patterns and dynamics present in credit data. Machine learning, a subset of artificial intelligence, offers a promising alternative by leveraging algorithms that can learn from data and make predictions or decisions without being explicitly programmed. By incorporating machine learning techniques into credit scoring processes, banks can potentially improve the accuracy and efficiency of their credit risk assessment, leading to better-informed lending decisions and reduced default rates. The research will begin with a comprehensive literature review to examine existing studies and methodologies related to credit scoring, machine learning algorithms, and their applications in the banking sector. This review will provide a solid foundation for understanding the current landscape and identifying gaps that warrant further exploration. The research methodology section will outline the specific machine learning algorithms to be implemented and the dataset to be used for training and testing. Various steps in the data preprocessing, feature selection, model training, and evaluation process will be elaborated upon to ensure a robust and systematic approach to the study. The findings and discussion chapter will present the results of the empirical analysis, including the performance metrics of the machine learning models in credit scoring tasks. The interpretability of the models, the impact of different features on credit decisions, and comparisons with traditional credit scoring methods will be thoroughly analyzed and discussed. In conclusion, the research will summarize the key findings, implications, and contributions to the field of credit scoring in banking through the application of machine learning algorithms. The limitations of the study, potential areas for future research, and practical recommendations for banks looking to adopt machine learning in credit scoring will also be addressed. Overall, this project seeks to advance our understanding of how machine learning algorithms can enhance credit scoring processes in the banking sector, ultimately leading to more accurate risk assessment and improved credit decision-making."