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Introduction

Over the past few years, the rise of peer-to-peer (P2P) payments has revolutionized the way individuals transfer money to one another. With the convenience of mobile wallets and apps, P2P payments have become an integral part of people’s daily lives. However, with the increase in P2P transactions, the risk of fraud has also escalated. Fraudulent activities such as identity theft, account takeover, and unauthorized transactions are some of the common challenges faced in the P2P payment ecosystem. Real-time fraud detection has become crucial in identifying and preventing fraudulent transactions in P2P payments.

This thesis focuses on Real-time Fraud Detection for Peer-to-Peer Payments, aiming to develop effective strategies and techniques to combat fraud in the P2P payment landscape. By leveraging real-time data analytics and machine learning algorithms, this research seeks to enhance the security measures in place for P2P payments, thus ensuring a safe and secure transaction environment for users.

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 Payments
2.2 Fraudulent Activities in Peer-to-Peer Payments
2.3 Existing Fraud Detection Techniques
2.4 Real-time Data Analytics in Fraud Detection
2.5 Machine Learning Algorithms for Fraud Detection
2.6 Challenges in Fraud Detection for P2P Payments
2.7 Best Practices in Fraud Prevention for P2P Payments
2.8 Regulatory Framework for P2P Payments
2.9 Case Studies on Fraudulent Activities in P2P Payments
2.10 Future Trends in Real-time Fraud Detection for P2P Payments

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sample Selection
3.5 Variables and Measures
3.6 Model Development
3.7 Testing and Validation
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Fraud Detection Techniques
4.2 Evaluation of Machine Learning Algorithms
4.3 Comparison of Real-time Data Analytics Approaches
4.4 Implications for P2P Payment Providers
4.5 Recommendations for Enhanced Fraud Detection
4.6 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to the Field
5.4 Implications for Practice
5.5 Limitations of the Study
5.6 Recommendations for Future Research

Thesis Overview:

Real-time Fraud Detection for Peer-to-Peer Payments is a comprehensive study focused on enhancing the security measures in place for P2P payments. The thesis begins with an introduction setting the stage for the research, followed by a detailed literature review that explores the current landscape of P2P payments, fraudulent activities, existing fraud detection techniques, and future trends in the industry. The research methodology section outlines the approach taken in conducting the study, including data collection methods, analysis techniques, and ethical considerations.

The discussion of findings chapter delves into the analysis of fraud detection techniques, evaluation of machine learning algorithms, comparisons of real-time data analytics approaches, and recommendations for enhanced fraud detection in P2P payments. The conclusion and summary chapter provides a synthesis of the research findings, contributions to the field, implications for practice, limitations of the study, and recommendations for future research.

Overall, this thesis contributes to the existing body of knowledge on real-time fraud detection for P2P payments and offers valuable insights for P2P payment providers, regulators, and researchers in the field. By developing effective strategies and techniques to combat fraud, this research aims to create a safe and secure transaction environment for users engaging in P2P payments.

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