Project Abstract

Abstract
The banking sector plays a pivotal role in facilitating economic growth by providing financial services to businesses, particularly Small and Medium Enterprises (SMEs). Credit scoring is a crucial process in assessing the creditworthiness of potential borrowers, aiding banks in making informed decisions on loan approvals. Traditional credit scoring methods have limitations in evaluating the credit risk of SMEs due to their unique characteristics and limited historical financial data. This research focuses on the application of machine learning techniques to enhance credit scoring for SMEs in the banking sector. Chapter One Introduction 1.1 Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objectives of Study 1.5 Limitations of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the Research 1.9 Definition of Terms Chapter Two Literature Review 2.1 Traditional Credit Scoring Methods 2.2 Challenges in Credit Scoring for SMEs 2.3 Importance of Credit Scoring in Banking 2.4 Machine Learning in Financial Services 2.5 Applications of Machine Learning in Credit Scoring 2.6 Studies on Machine Learning in SME Credit Scoring 2.7 Evaluation Metrics for Credit Scoring Models 2.8 Feature Selection and Model Interpretability 2.9 Data Preprocessing Techniques 2.10 Model Performance Comparison Chapter Three Research Methodology 3.1 Research Design 3.2 Data Collection 3.3 Data Preprocessing 3.4 Feature Selection 3.5 Model Selection 3.6 Model Training and Evaluation 3.7 Performance Metrics 3.8 Ethical Considerations Chapter Four Discussion of Findings 4.1 Descriptive Analysis of SME Data 4.2 Feature Importance in Credit Scoring Models 4.3 Model Performance Evaluation 4.4 Comparison with Traditional Credit Scoring Methods 4.5 Interpretability of Machine Learning Models 4.6 Challenges and Limitations 4.7 Implications for Banking Sector 4.8 Recommendations for Future Research Chapter Five Conclusion and Summary 5.1 Summary of Findings 5.2 Contributions to Knowledge 5.3 Practical Implications 5.4 Conclusion 5.5 Recommendations for Banking Institutions 5.6 Areas for Future Research This research aims to contribute to the existing literature by demonstrating the effectiveness of machine learning in enhancing credit scoring for SMEs in the banking sector. By leveraging advanced algorithms and data analytics, banks can improve their risk assessment processes, leading to more accurate credit decisions and better financial inclusion for SMEs. The findings of this study have implications for both academia and industry, offering insights into the adoption of machine learning techniques in credit risk management.

Project Overview

The project aims to explore the application of machine learning techniques in credit scoring specifically tailored for Small and Medium Enterprises (SMEs) within the banking sector. SMEs play a crucial role in the economy by contributing significantly to economic growth, innovation, and employment opportunities. However, one of the challenges faced by SMEs is access to finance, with credit scoring being a critical component in the lending decision-making process. Traditional credit scoring models often struggle to accurately assess the creditworthiness of SMEs due to their unique characteristics, such as limited credit history, volatile cash flows, and diverse business models. Machine learning algorithms offer a promising solution to enhance credit scoring for SMEs by leveraging vast amounts of data to identify patterns and predict credit risk more accurately. This research will delve into the current landscape of credit scoring for SMEs in the banking sector, highlighting the limitations of existing models and the potential benefits of incorporating machine learning techniques. By conducting a thorough literature review, the project will explore the latest advancements and best practices in machine learning applications for credit scoring. The research methodology will involve data collection from banking institutions, including historical financial data of SMEs and credit scoring outcomes. Various machine learning algorithms, such as decision trees, random forests, and neural networks, will be applied to analyze the data and develop predictive models for credit risk assessment. Furthermore, the project will assess the performance of these machine learning models in comparison to traditional credit scoring methods through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score. The findings will provide insights into the effectiveness and feasibility of implementing machine learning in credit scoring for SMEs in real-world banking scenarios. Ultimately, this research aims to contribute to the advancement of credit risk assessment practices for SMEs in the banking sector by harnessing the power of machine learning algorithms. By enhancing the accuracy and efficiency of credit scoring processes, banks can make more informed lending decisions, mitigate risks, and support the growth and sustainability of SMEs in the economy.