Project Abstract

Abstract
The financial sector, particularly the banking industry, plays a crucial role in fostering economic growth by providing capital to businesses. Small businesses, being the backbone of many economies, often face challenges in accessing credit due to risk assessment processes implemented by financial institutions. Traditional credit risk assessment methods have limitations in accurately evaluating the creditworthiness of small businesses, leading to potential misallocation of resources and increased default rates. This research project aims to explore the application of machine learning techniques in enhancing credit risk assessment for small businesses in the banking sector. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure, and definition of key terms. Chapter Two reviews relevant literature on credit risk assessment, machine learning applications in finance, and specifically in credit risk evaluation for small businesses. The literature review will encompass various models, methodologies, and empirical studies in the field. Chapter Three outlines the research methodology, detailing the research design, data collection methods, variables selection, model development, and evaluation criteria. It also discusses the ethical considerations and potential biases in the study. The chapter further presents the sampling technique, data sources, and analytical tools employed in the research process. In Chapter Four, the findings of the research are extensively discussed, focusing on the application of machine learning algorithms in credit risk assessment for small businesses. The chapter presents the results of the model implementation, performance evaluation metrics, and comparative analysis with traditional methods. Moreover, it delves into the implications of the findings on credit risk management practices in the banking sector. Chapter Five concludes the research by summarizing the key findings, discussing the implications for theory and practice, and providing recommendations for future research. The study contributes to the existing literature by demonstrating the effectiveness of machine learning in improving credit risk assessment processes for small businesses, thereby aiding financial institutions in making informed lending decisions and mitigating risks. In conclusion, this research project bridges the gap between machine learning technology and credit risk assessment in the banking sector, offering valuable insights for policymakers, financial institutions, and researchers. By harnessing the power of machine learning algorithms, small businesses can benefit from more accurate credit evaluations, increased access to finance, and sustainable growth opportunities.

Project Overview

The project topic, "Application of Machine Learning in Credit Risk Assessment for Small Businesses in Banking Sector," focuses on the utilization of machine learning techniques to enhance the credit risk assessment process for small businesses within the banking sector. This research aims to address the challenges faced by financial institutions in accurately evaluating the creditworthiness of small business owners, who often have limited credit histories and financial data available for traditional risk assessment methods. By leveraging machine learning algorithms and predictive modeling, this study seeks to develop a more efficient and reliable credit risk assessment framework tailored specifically for small business clients. The banking sector plays a crucial role in facilitating financial services for small businesses, which are vital contributors to economic growth and job creation. However, assessing the credit risk associated with lending to small businesses can be complex due to the lack of comprehensive financial information and historical data. Traditional credit risk assessment methods may not effectively capture the unique characteristics and risk factors associated with small businesses, leading to suboptimal lending decisions and increased default rates. Machine learning offers a promising approach to enhance credit risk assessment processes by analyzing large volumes of data, identifying patterns, and making data-driven predictions. By training machine learning models on diverse sets of input variables, including financial statements, transaction records, market data, and business performance metrics, banks can improve their ability to evaluate the creditworthiness of small business borrowers more accurately and efficiently. This research project will involve a comprehensive review of existing literature on credit risk assessment, machine learning applications in finance, and small business lending practices. It will also explore relevant methodologies for data collection, feature engineering, model training, and performance evaluation. The study will utilize real-world datasets obtained from banking institutions to develop and validate machine learning models for credit risk assessment in small business lending. The findings of this research are expected to provide valuable insights into the effectiveness of machine learning techniques in enhancing credit risk assessment for small businesses in the banking sector. By developing a robust and data-driven credit risk assessment framework, financial institutions can improve their lending practices, mitigate risks, and support the growth and sustainability of small businesses. Ultimately, the application of machine learning in credit risk assessment has the potential to foster greater financial inclusion, enable more informed lending decisions, and drive positive economic outcomes for small businesses and the banking sector as a whole.