Thesis Abstract

Abstract
The banking sector plays a crucial role in the economic development of any country by providing financial services to businesses and individuals. Small and Medium Enterprises (SMEs) are considered the backbone of many economies, contributing significantly to job creation and economic growth. However, SMEs often face challenges in accessing credit due to their limited financial history and resources, making credit risk assessment a critical issue for banks. Traditional credit risk assessment methods may not effectively evaluate the creditworthiness of SMEs, leading to potential financial risks for banks. This thesis explores the application of machine learning techniques in credit risk assessment for SMEs in the banking sector. The study aims to leverage the power of machine learning algorithms to improve the accuracy and efficiency of credit risk assessment processes, enabling banks to make more informed lending decisions regarding SMEs. By analyzing a wide range of financial and non-financial data, machine learning models can identify patterns and relationships that traditional methods may overlook, providing a more holistic view of SME creditworthiness. The research begins with a comprehensive literature review that examines existing studies on credit risk assessment, machine learning applications in finance, and specific approaches to assessing credit risk for SMEs. The methodology section outlines the research design, data collection methods, variables selection, and model development process. The study uses a dataset of historical SME loan applications to train and test machine learning models, evaluating their performance in predicting credit risk compared to traditional methods. The findings of the study demonstrate the effectiveness of machine learning models in credit risk assessment for SMEs, showcasing their ability to outperform traditional credit scoring models in terms of accuracy and predictive power. The discussion section delves into the implications of these findings for banks and SMEs, highlighting the potential benefits of adopting machine learning techniques in credit risk assessment processes. The study concludes with a summary of key findings, limitations of the research, and recommendations for future studies in this area. Overall, this thesis contributes to the growing body of research on the application of machine learning in finance and provides valuable insights into how banks can enhance their credit risk assessment practices for SMEs. By harnessing the power of machine learning algorithms, banks can better support the growth and development of SMEs while managing credit risks effectively, ultimately fostering a more robust and sustainable banking sector.

Thesis Overview

The research project titled "Application of Machine Learning in Credit Risk Assessment for Small and Medium Enterprises (SMEs) in Banking Sector" aims to explore the utilization of machine learning techniques in enhancing the credit risk assessment process for SMEs within the banking industry. This study recognizes the critical role that SMEs play in economic development and the challenges they face in accessing credit due to limitations in traditional credit risk assessment methods. By leveraging machine learning algorithms and models, this research seeks to develop a more accurate, efficient, and scalable approach to evaluating the creditworthiness of SMEs, ultimately enabling banks to make more informed lending decisions. The project will commence with a comprehensive literature review to examine existing studies and practices related to credit risk assessment, machine learning applications in finance, and specifically in SME lending. By critically analyzing the current state of the art in these areas, the research aims to identify gaps and opportunities for innovation in credit risk assessment for SMEs. Subsequently, the research methodology will be structured to outline the data collection process, selection of machine learning algorithms, model development, and evaluation techniques. The methodology will incorporate both quantitative and qualitative aspects to ensure a robust and rigorous analysis of the credit risk assessment framework proposed for SMEs. The core of the study will involve implementing machine learning models such as logistic regression, decision trees, random forest, and neural networks to predict credit risk for SMEs based on historical financial data, industry trends, and other relevant variables. Through model training, testing, and validation using real-world SME datasets, the project aims to demonstrate the efficacy and accuracy of machine learning in assessing credit risk for this specific segment of borrowers. The findings and discussion section will present an in-depth analysis of the results obtained from the machine learning models, highlighting their predictive capabilities, strengths, limitations, and implications for banking institutions. This section will provide valuable insights into how machine learning can enhance the credit risk assessment process for SMEs, leading to more informed lending decisions, reduced default rates, and improved financial inclusion for small businesses. Finally, the conclusion and summary of the project will consolidate the key findings, implications, and contributions of the study. It will also discuss the practical implications of deploying machine learning in credit risk assessment for SMEs, addressing challenges, and providing recommendations for future research and industry adoption. In summary, the research project on the "Application of Machine Learning in Credit Risk Assessment for Small and Medium Enterprises (SMEs) in Banking Sector" represents a significant endeavor to advance the field of credit risk assessment by harnessing the power of machine learning technology to support financial inclusion and sustainable economic growth for SMEs."