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Table of Contents:

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Objectives of the Study
1.4 Research Questions
1.5 Significance of the Study
1.6 Scope and Limitations of the Study

Chapter 2: Literature Review
2.1 Overview of Credit Scoring
2.2 Traditional Credit Scoring Methods
2.3 Machine Learning in Credit Scoring
2.4 Interpretable Machine Learning Techniques
2.5 Previous Studies on Interpretation in Credit Scoring

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection and Preparation
3.3 Model Development
3.4 Model Evaluation
3.5 Interpretation Techniques

Chapter 4: Discussion of Findings
4.1 Analysis of Credit Scoring Models
4.2 Interpretation of Model Results
4.3 Key Factors Influencing Credit Scores
4.4 Comparison of Interpretable and Black-Box Models

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Credit Scoring Industry
5.3 Recommendations for Future Research
5.4 Conclusion

Brief Overview:

The thesis “Interpretable Machine Learning for Credit Scoring” focuses on the application of interpretable machine learning techniques in credit scoring. The study aims to address the limitations of traditional credit scoring methods by using models that are transparent and easily interpretable by end-users.

Chapter 1 introduces the background of the study, the problem statement, objectives, research questions, and the significance of the study. It also discusses the scope and limitations of the research.

Chapter 2 provides a comprehensive review of the literature on credit scoring, traditional methods, machine learning techniques, and previous studies on interpretation in credit scoring.

Chapter 3 outlines the research methodology, including the research design, data collection, model development, evaluation, and interpretation techniques.

Chapter 4 presents the discussion of findings, including the analysis of credit scoring models, interpretation of results, key factors influencing credit scores, and a comparison of interpretable and black-box models.

Chapter 5 concludes the thesis with a summary of findings, implications for the credit scoring industry, recommendations for future research, and a conclusion. The study contributes to the field by demonstrating the importance of interpretability in credit scoring models and providing insights for improving transparency and trust in credit decisions.

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