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Introduction

Small businesses play a significant role in any economy by contributing to job creation and economic growth. However, these businesses face numerous challenges, one of which is the risk of credit default. Credit default occurs when a borrower fails to repay a loan according to the terms agreed upon with the lender. Predicting credit default for small businesses is crucial for lenders to minimize their risk exposure and make informed lending decisions. This thesis aims to investigate the factors that influence credit default for small businesses and develop a predictive model to assess the creditworthiness of these entities.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of credit default in small businesses
2.2 Factors influencing credit default
2.3 Previous studies on credit default prediction
2.4 Credit risk assessment models
2.5 Machine learning techniques in credit default prediction
2.6 Evaluation metrics for credit default prediction models
2.7 Regulatory framework for small business lending
2.8 The impact of economic factors on credit default
2.9 The role of credit scoring in small business lending
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Variable selection
3.4 Model development
3.5 Model evaluation
3.6 Data preprocessing techniques
3.7 Sampling techniques
3.8 Ethical considerations
3.9 Limitations of the research methodology

Chapter 4: Findings
4.1 Descriptive statistics of the dataset
4.2 Correlation analysis of variables
4.3 Results of the predictive model
4.4 Comparison of different models
4.5 Interpretation of model results
4.6 Discussion of key findings
4.7 Managerial implications
4.8 Recommendations for future research

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to existing literature
5.3 Practical implications
5.4 Limitations of the study
5.5 Recommendations for policymakers
5.6 Suggestions for future research

Thesis Overview

Credit default prediction is a critical area of research in the field of finance, particularly for small businesses. Small businesses often lack the resources and track record that larger corporations have, making them more susceptible to credit defaults. Understanding the factors that contribute to credit default in small businesses and developing predictive models to assess this risk is essential for lenders to make informed lending decisions.

This thesis aims to explore the various factors influencing credit default in small businesses and develop a predictive model to assess the creditworthiness of these entities. By analyzing historical data and employing machine learning techniques, the study seeks to identify patterns and trends that can help predict credit defaults accurately.

The literature review provides an overview of credit default in small businesses, factors influencing credit default, previous studies on credit default prediction, credit risk assessment models, machine learning techniques, evaluation metrics, regulatory frameworks, economic factors, and credit scoring. The research methodology explains the design, data collection methods, variable selection, model development, evaluation, preprocessing techniques, sampling, and ethical considerations.

The findings chapter presents descriptive statistics, correlation analysis, results of the predictive model, interpretation, discussion of key findings, managerial implications, and recommendations. The conclusion and summary chapter summarizes key findings, contributions to literature, practical implications, limitations, recommendations for policymakers, and suggestions for future research.

Overall, this thesis aims to contribute to the existing literature on credit default prediction for small businesses and provide valuable insights for lenders, policymakers, and researchers in the field of finance.

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