Thesis Abstract

Abstract
This thesis investigates the application of machine learning techniques in credit risk assessment for small businesses within the banking sector. Small businesses play a crucial role in economic development, and their access to credit is vital for growth and sustainability. However, traditional credit risk assessment methods often fall short in accurately evaluating the creditworthiness of small businesses, leading to increased risk for lenders. Machine learning, with its ability to analyze large datasets and identify complex patterns, presents a promising alternative for enhancing credit risk assessment processes. The study begins with an introduction to the research problem, highlighting the challenges faced by small businesses in accessing credit and the limitations of current credit risk assessment practices. The objectives of the study are outlined, focusing on the development of a machine learning model that can improve the accuracy and efficiency of credit risk assessment for small businesses. The scope of the study is defined, emphasizing the specific focus on small businesses within the banking sector. A comprehensive literature review is conducted to explore existing research on credit risk assessment, machine learning applications in finance, and the challenges and opportunities in assessing credit risk for small businesses. The review highlights the potential benefits of integrating machine learning techniques into credit risk assessment processes and identifies key factors influencing credit risk evaluation for small businesses. The research methodology chapter outlines the approach taken to develop and evaluate the machine learning model for credit risk assessment. Data collection methods, feature selection techniques, model development procedures, and evaluation metrics are discussed in detail. The chapter also addresses ethical considerations and data privacy issues related to the use of machine learning in credit risk assessment. Findings from the study are presented and analyzed in the discussion chapter, focusing on the performance of the developed machine learning model in predicting credit risk for small businesses. The results demonstrate the effectiveness of machine learning techniques in improving the accuracy and efficiency of credit risk assessment, leading to more informed lending decisions and reduced default rates. In conclusion, the study highlights the significance of applying machine learning in credit risk assessment for small businesses in the banking sector. The thesis contributes to the growing body of research on fintech applications in finance and provides practical insights for lenders and policymakers seeking to enhance credit risk assessment processes. Recommendations for future research and implications for practice are also discussed, emphasizing the potential of machine learning to transform credit risk assessment practices and support the growth of small businesses in the banking sector.

Thesis Overview

The project titled "Application of Machine Learning in Credit Risk Assessment for Small Businesses in Banking Sector" aims to explore the utilization of machine learning techniques in enhancing credit risk assessment processes specifically tailored for small businesses within the banking sector. This research endeavors to address the challenges faced by financial institutions in accurately assessing the creditworthiness of small businesses, which are often characterized by limited credit history, volatile cash flows, and higher default rates compared to larger enterprises. The primary objective of this study is to investigate how machine learning algorithms can be leveraged to improve the efficiency and accuracy of credit risk assessment for small businesses. By analyzing a diverse set of data points such as financial statements, transaction history, industry trends, and macroeconomic indicators, machine learning models can assist banks in identifying patterns and predicting credit risk more effectively than traditional methods. Through an extensive literature review, this research will delve into existing studies on credit risk assessment, machine learning applications in finance, and specifically, the intersection of these fields in small business lending. By synthesizing relevant theories, methodologies, and empirical findings, this study aims to build a comprehensive understanding of the current landscape and potential opportunities for innovation in credit risk assessment practices. The research methodology will involve data collection from financial institutions, small business owners, and industry experts to gather insights on the challenges and requirements of credit risk assessment in the context of small businesses. Utilizing quantitative analysis techniques and machine learning algorithms, the study will develop predictive models to evaluate credit risk for small businesses and compare their performance with traditional credit scoring methods. The findings of this study are expected to contribute to the body of knowledge on credit risk assessment and machine learning applications in the banking sector, with a specific focus on small businesses. By demonstrating the potential benefits of integrating machine learning into credit risk assessment processes, this research aims to provide practical recommendations for financial institutions to enhance their risk management practices and support sustainable lending to small businesses. In conclusion, the "Application of Machine Learning in Credit Risk Assessment for Small Businesses in Banking Sector" project represents a significant step towards improving the accuracy, efficiency, and inclusivity of credit risk assessment processes for small businesses. By harnessing the power of machine learning technologies, this research aims to pave the way for more informed decision-making in small business lending, ultimately fostering financial stability and growth in the banking sector.