Thesis Abstract

Abstract
In the rapidly evolving landscape of financial services, credit scoring plays a crucial role in assessing the creditworthiness of individuals and businesses seeking loans. Traditional credit scoring methods have limitations in accurately predicting credit risk, prompting the need for innovative approaches. This thesis explores the application of machine learning techniques in credit scoring for financial institutions. The research begins with a comprehensive introduction that highlights the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter two delves into a detailed literature review, covering ten key areas that provide a foundation for understanding the role of machine learning in credit scoring. Chapter three focuses on the research methodology, outlining the research design, data collection methods, variables, sampling techniques, model development, and validation procedures. The methodology section provides a roadmap for implementing machine learning algorithms in credit scoring models. Chapter four presents an in-depth discussion of the findings from applying machine learning techniques to credit scoring. The chapter highlights the performance of various machine learning models in predicting credit risk compared to traditional scoring methods. The results are analyzed, interpreted, and implications for financial institutions are discussed. Finally, chapter five offers a conclusion and summary of the thesis. The findings underscore the potential benefits of integrating machine learning into credit scoring processes, such as improved accuracy, efficiency, and risk management. Recommendations for future research and practical implications for financial institutions are also discussed. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in credit scoring for financial institutions. The research underscores the importance of leveraging advanced technologies to enhance credit risk assessment processes, ultimately benefiting both lenders and borrowers in the financial ecosystem.

Thesis Overview

The project titled "Application of Machine Learning in Credit Scoring for Financial Institutions" aims to explore the potential benefits and challenges of implementing machine learning algorithms in the credit scoring process within financial institutions. This research seeks to address the growing need for more accurate and efficient credit scoring methods to assess the creditworthiness of individuals and businesses. By leveraging the power of machine learning techniques, such as supervised learning, unsupervised learning, and deep learning, this study aims to enhance the predictive accuracy of credit scoring models and improve decision-making processes in the financial industry. The project will begin with a comprehensive review of existing literature on credit scoring methodologies, machine learning algorithms, and their applications in the financial sector. This literature review will provide a solid foundation for understanding the current state of credit scoring practices and the potential benefits of incorporating machine learning techniques. The research methodology will involve collecting and analyzing real-world credit data from financial institutions to develop and evaluate machine learning models for credit scoring. Various machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, will be applied to the dataset to compare their performance in predicting creditworthiness. The study will also consider feature engineering techniques and model optimization to enhance the predictive accuracy of the credit scoring models. The findings of this research will contribute to the existing body of knowledge on credit scoring and machine learning applications in the financial industry. By demonstrating the effectiveness of machine learning in credit scoring, this study aims to provide valuable insights for financial institutions seeking to improve their credit risk assessment processes and make more informed lending decisions. Overall, the project on the "Application of Machine Learning in Credit Scoring for Financial Institutions" is expected to shed light on the potential benefits and challenges of integrating machine learning algorithms into credit scoring practices. This research has the potential to drive innovation in the financial sector, improve risk management strategies, and enhance the overall efficiency and accuracy of credit assessment processes within financial institutions.