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Introduction:

In recent years, the use of alternative data in credit risk assessment has gained significant attention in the financial industry. Alternative data refers to non-traditional sources of information that can be used to assess the creditworthiness of borrowers, such as social media data, online shopping behavior, and mobile phone data. This type of data provides additional insights into the credit risk profile of borrowers that may not be captured by traditional credit bureau data.

The conventional credit risk assessment models primarily rely on historical financial data, such as credit scores and income levels, to evaluate the probability of default for borrowers. However, these models may not always accurately reflect the credit risk of certain individuals, especially those with limited credit history or unconventional sources of income. Alternative data offers the potential to enhance the predictive power of credit risk assessment models by incorporating a wider range of information about borrowers.

This thesis aims to explore the effectiveness of using alternative data in credit risk assessment and examine its implications for the financial industry. The research will focus on identifying the types of alternative data that are most predictive of credit risk, evaluating the challenges and limitations of using alternative data, and assessing the impact of alternative data on credit risk modeling.

Table of Contents:

Chapter 1: Introduction
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 Risk Assessment
2.2 Traditional Credit Risk Models
2.3 Alternative Data in Credit Risk Assessment
2.4 Types of Alternative Data
2.5 Use of Alternative Data in the Financial Industry
2.6 Benefits of Using Alternative Data
2.7 Challenges of Using Alternative Data
2.8 Regulatory Considerations
2.9 Previous Studies on Alternative Data in Credit Risk Assessment
2.10 Gaps in Existing Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Analysis
3.4 Sampling Method
3.5 Variables
3.6 Model Specification
3.7 Hypotheses
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Descriptive Statistics
4.2 Regression Analysis
4.3 Comparative Analysis
4.4 Interpretation of Results
4.5 Implications for Credit Risk Assessment
4.6 Recommendations for Practitioners
4.7 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to the Literature
5.4 Practical Implications
5.5 Limitations of the Study
5.6 Directions for Future Research

This thesis will provide valuable insights into the use of alternative data in credit risk assessment and contribute to the growing body of literature on this topic. It will also offer recommendations for practitioners in the financial industry on how to effectively incorporate alternative data into their credit risk models.

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