Thesis Abstract

Abstract
The rapid growth of small and medium-sized enterprises (SMEs) has resulted in an increased demand for efficient credit risk assessment processes to support sustainable business operations. Traditional credit risk assessment methods have shown limitations in accurately evaluating the creditworthiness of SMEs due to their unique characteristics and limited financial histories. In response to these challenges, this research explores the application of machine learning algorithms in credit risk assessment for SMEs. This thesis investigates the potential of machine learning algorithms, specifically focusing on their ability to enhance the accuracy and efficiency of credit risk assessment for SMEs. The research aims to develop a predictive model that leverages machine learning techniques to evaluate the creditworthiness of SMEs based on various financial and non-financial data points. By utilizing historical credit data, financial statements, and other relevant information, the model aims to provide more reliable credit risk assessments compared to traditional methods. The literature review provides a comprehensive analysis of existing studies related to credit risk assessment, machine learning algorithms, and their applications in the financial industry. The review highlights the strengths and limitations of current approaches and identifies gaps in the literature that this research aims to address. The research methodology section outlines the process of data collection, preprocessing, feature selection, model training, and evaluation. Various machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, will be implemented and compared to determine the most effective approach for credit risk assessment in SMEs. The findings of this study are expected to contribute to the existing body of knowledge on credit risk assessment and machine learning applications in the banking and finance sector. The results will demonstrate the effectiveness of machine learning algorithms in improving the accuracy and efficiency of credit risk assessment for SMEs, ultimately helping financial institutions make more informed lending decisions. In conclusion, this research project aims to provide valuable insights into the potential benefits of applying machine learning algorithms in credit risk assessment for small and medium-sized enterprises. By developing a predictive model tailored to the unique characteristics of SMEs, this study seeks to enhance the credit evaluation process and support sustainable growth in the SME sector.

Thesis Overview