Abstract:
This research aims to investigate the role of big data analytics in credit risk assessment within the banking industry. With the increasing availability of vast amounts of data, banks have the opportunity to leverage big data analytics techniques to enhance their credit risk assessment processes. This study will explore the potential benefits, challenges, and implications of using big data analytics in credit risk assessment. By examining the current practices and analyzing case studies, this research will provide insights into how big data analytics can improve the accuracy and efficiency of credit risk assessment, ultimately leading to better decision-making and risk management in the banking sector.

Table of Contents:

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

Chapter 2: Literature Review
2.1 Overview of Credit Risk Assessment
2.2 Traditional Approaches to Credit Risk Assessment
2.3 Introduction to Big Data Analytics
2.4 Applications of Big Data Analytics in Banking
2.5 Big Data Analytics in Credit Risk Assessment

Chapter 3: Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sample Selection
3.5 Ethical Considerations

Chapter 4: Findings and Analysis
4.1 Overview of Credit Risk Assessment Practices
4.2 Case Studies of Big Data Analytics in Credit Risk Assessment
4.3 Benefits and Challenges of Using Big Data Analytics
4.4 Implications for Credit Risk Management

Chapter 5: Conclusion and Recommendations
5.1 Summary of Findings
5.2 Conclusion
5.3 Recommendations for Future Research
5.4 Practical Implications for the Banking Industry

This research will provide a comprehensive understanding of the role of big data analytics in credit risk assessment and offer valuable insights for banks and financial institutions looking to leverage big data for improved risk management.