[ad_1]

Introduction

In recent years, peer-to-peer lending has gained popularity as an alternative to traditional banking for both borrowers and investors. However, one of the key challenges facing peer-to-peer lending platforms is the risk of loan defaults. As a PhD student in finance, my final thesis aims to address this issue by developing a predictive model for assessing loan default risk in peer-to-peer lending.

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 peer-to-peer lending
2.2 Loan default risk in peer-to-peer lending
2.3 Factors influencing loan default risk
2.4 Predictive models for loan default risk
2.5 Machine learning techniques in risk prediction
2.6 Previous studies on loan default risk
2.7 Empirical evidence on loan default risk
2.8 Regulatory framework for peer-to-peer lending
2.9 Impact of loan defaults on peer-to-peer lending platforms
2.10 Strategies for managing loan default risk

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Variable selection
3.4 Model development
3.5 Model validation
3.6 Sensitivity analysis
3.7 Interpretation of results
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Descriptive statistics
4.2 Model performance
4.3 Factors affecting loan default risk
4.4 Implications for peer-to-peer lending platforms
4.5 Comparison with existing models
4.6 Managerial implications
4.7 Policy recommendations
4.8 Future research directions

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

Thesis Overview

Peer-to-peer lending has emerged as an innovative alternative to traditional banking, allowing individuals to borrow and lend money directly through online platforms. However, one of the key challenges facing peer-to-peer lending is the risk of loan defaults, which can have significant financial implications for investors and platform operators. This thesis aims to develop a predictive model for assessing loan default risk in peer-to-peer lending, using a combination of machine learning techniques and traditional statistical methods.

The thesis begins with an introduction to the research topic, providing background information on peer-to-peer lending and highlighting the problem of loan default risk. The objectives of the study are outlined, along with the limitations and scope of the research. The significance of the study is also discussed, emphasizing the potential impact of the research findings on the peer-to-peer lending industry. The structure of the thesis is then presented, outlining the contents of each chapter.

The literature review chapter provides a comprehensive analysis of existing research on loan default risk in peer-to-peer lending, including factors influencing default rates, predictive models, and regulatory considerations. The research methodology chapter details the data collection process, variable selection, model development, and validation techniques used in the study. The discussion of findings chapter presents the results of the predictive model, identifying key factors contributing to loan default risk and proposing strategies for mitigating this risk.

In the conclusion and summary chapter, the key findings of the study are summarized, highlighting the contributions to the literature and practical implications for peer-to-peer lending platforms. The limitations of the study are acknowledged, and recommendations for future research are provided. Overall, this thesis aims to advance our understanding of loan default risk in peer-to-peer lending and provide actionable insights for industry stakeholders.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.