Project Abstract

Abstract
The banking sector plays a crucial role in the economy by providing financial services to individuals and businesses. In recent years, the application of machine learning techniques in credit scoring has gained significant attention due to its potential to improve the accuracy and efficiency of assessing creditworthiness. This research project focuses on exploring the application of machine learning in credit scoring for small businesses in the banking sector. The study begins with an introduction that highlights the importance of credit scoring in the lending process and the challenges faced by traditional credit scoring methods. The background of the study provides an overview of the evolution of credit scoring models and the emergence of machine learning as a promising alternative. The problem statement identifies the limitations of traditional credit scoring methods in assessing the creditworthiness of small businesses accurately and efficiently. The objectives of the study include evaluating the effectiveness of machine learning algorithms in credit scoring for small businesses, identifying the key factors that influence creditworthiness in this context, and proposing a model that combines machine learning techniques with traditional credit scoring methods. The limitations of the study, such as data availability and model interpretability, are also discussed, along with the scope of the research, which focuses on small businesses in the banking sector. The significance of the study lies in its potential to enhance the credit assessment process for small businesses, leading to more informed lending decisions and improved risk management for banks. The structure of the research outlines the chapters and content covered in the study, including the literature review, research methodology, discussion of findings, and conclusion. The literature review in Chapter Two provides a comprehensive overview of existing research on credit scoring in the banking sector, with a focus on machine learning applications for small businesses. Key topics covered include the evolution of credit scoring models, the challenges of credit assessment for small businesses, and the advantages of machine learning in credit scoring. Chapter Three details the research methodology, including the research design, data collection methods, and the selection of machine learning algorithms for credit scoring. The chapter also discusses the variables considered in the model, such as financial ratios, business performance metrics, and industry-specific factors. In Chapter Four, the discussion of findings presents the results of applying machine learning algorithms to credit scoring for small businesses. The analysis includes the performance metrics of the model, the key factors influencing creditworthiness, and the comparison with traditional credit scoring methods. Finally, Chapter Five concludes the research by summarizing the findings, discussing the implications for the banking sector, and suggesting areas for future research. The project contributes to the growing body of literature on machine learning in credit scoring and provides valuable insights for banks seeking to enhance their credit assessment processes for small businesses.

Project Overview