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Introduction

Peer-to-peer lending, also known as P2P lending, has gained significant popularity in recent years as an alternative form of financing that connects borrowers with lenders through online platforms. While P2P lending offers many benefits such as lower interest rates for borrowers and higher returns for lenders, one of the key challenges faced by this industry is the risk of defaults. Default prediction is crucial for both borrowers and lenders in order to assess creditworthiness and minimize financial losses.

This thesis aims to explore the topic of Peer-to-peer lending default prediction in order to develop predictive models that can accurately assess the risk of default for borrowers. By analyzing historical data from P2P lending platforms, this research seeks to identify key factors that influence default rates and develop predictive algorithms that can help lenders make informed lending decisions.

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 P2P lending
2.2 Default prediction in financial markets
2.3 Factors influencing default rates in P2P lending
2.4 Existing models for default prediction
2.5 Machine learning techniques for predictive modeling
2.6 Evaluation metrics for predictive models
2.7 Comparison of different approaches to default prediction
2.8 Challenges and future directions in default prediction
2.9 Summary of literature review

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Feature selection and engineering
3.3 Model selection and evaluation
3.4 Cross-validation and parameter tuning
3.5 Performance metrics for model evaluation
3.6 Interpretation of results
3.7 Ethical considerations
3.8 Limitations of research methodology

Chapter 4: Discussion of Findings
4.1 Descriptive analysis of dataset
4.2 Identification of key predictors of default
4.3 Development of predictive models
4.4 Evaluation of model performance
4.5 Comparison with existing models
4.6 Interpretation of results
4.7 Implications for P2P lending industry
4.8 Recommendations for future research

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for practice
5.4 Limitations of the study
5.5 Future research directions
5.6 Conclusion

Thesis Overview on Peer-to-peer lending default prediction:

Peer-to-peer lending has emerged as an innovative alternative to traditional banking systems, offering individuals and small businesses the opportunity to access financing through online platforms. However, one of the major risks associated with P2P lending is the potential for borrower defaults, which can result in financial losses for lenders. Default prediction is therefore a critical area of research that aims to develop models and algorithms to assess the creditworthiness of borrowers and minimize default rates.

This thesis focuses on exploring the topic of Peer-to-peer lending default prediction by analyzing historical data from P2P lending platforms and identifying key factors that influence default rates. By developing predictive models using machine learning techniques, this research aims to provide valuable insights for lenders in making informed lending decisions and managing risks effectively. The findings of this study have the potential to contribute to the development of more accurate and reliable models for default prediction in the P2P lending industry.

Through a comprehensive literature review, research methodology, and discussion of findings, this thesis aims to provide a thorough analysis of the factors influencing default rates in P2P lending and offer practical recommendations for lenders and policymakers. The conclusion and summary of this research project will highlight key insights, contributions to the field, limitations of the study, and directions for future research in the area of Peer-to-peer lending default prediction.

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