Thesis Abstract

Abstract
The evolving landscape of banking transactions in the digital era has brought about numerous challenges, particularly in the realm of fraud detection. This thesis explores the application of machine learning techniques to enhance fraud detection in banking transactions. The study delves into the background of the problem, highlights the significance of the research, and outlines the methodology employed to achieve the objectives. Chapter 1 introduces the topic, providing an overview of the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and the definition of key terms. It sets the foundation for understanding the importance of leveraging machine learning in combating fraud within the banking sector. Chapter 2 embarks on a comprehensive literature review, covering ten key aspects related to fraud detection, machine learning algorithms, banking transactions, and existing research in the field. This chapter aims to establish a theoretical framework that underpins the application of machine learning in fraud detection for banking transactions. Chapter 3 focuses on the research methodology employed in this study. It includes detailed discussions on the research design, data collection methods, sampling techniques, data analysis processes, as well as the machine learning models utilized for fraud detection. This chapter elucidates the approach taken to address the research objectives effectively. Chapter 4 presents an in-depth discussion of the findings derived from the application of machine learning techniques in fraud detection for banking transactions. The chapter highlights the effectiveness of various algorithms in detecting and preventing fraudulent activities, providing insights into the practical implications of the study. Lastly, Chapter 5 encapsulates the conclusion and summary of the thesis. It synthesizes the key findings, discusses the implications of the research, and offers recommendations for future research endeavors in this domain. The conclusion reaffirms the significance of utilizing machine learning in enhancing fraud detection mechanisms within the banking sector. In conclusion, this thesis contributes to the existing body of knowledge by demonstrating the efficacy of machine learning in bolstering fraud detection capabilities for banking transactions. By leveraging advanced algorithms and data analytics, financial institutions can proactively combat fraudulent activities, safeguarding the integrity of their operations and enhancing customer trust.

Thesis Overview

The project titled "Application of Machine Learning in Fraud Detection for Banking Transactions" aims to explore the use of machine learning techniques to enhance fraud detection in the banking sector. With the increasing volume and complexity of financial transactions, traditional rule-based systems are proving to be insufficient in detecting sophisticated fraudulent activities. Machine learning algorithms, with their ability to analyze patterns and detect anomalies in large datasets, offer a promising solution to this challenge. The research will begin with a comprehensive literature review to examine existing studies on fraud detection in banking and the application of machine learning in this context. This will provide a solid theoretical foundation for the study and help identify gaps in current research that can be addressed through this project. The methodology section will outline the approach taken to develop and implement machine learning models for fraud detection. This will involve data collection, preprocessing, feature selection, model training, and evaluation. Various machine learning algorithms such as logistic regression, decision trees, random forests, and neural networks will be explored to identify the most effective approach for fraud detection in banking transactions. The findings section will present the results of the study, including the performance metrics of the developed machine learning models in detecting fraud. The discussion will analyze the strengths and limitations of the models, as well as provide insights into the factors influencing their effectiveness in real-world banking scenarios. In conclusion, this research aims to contribute to the growing body of knowledge on fraud detection in banking using machine learning techniques. By leveraging the power of artificial intelligence and advanced analytics, banks can enhance their fraud detection capabilities and protect their customers from financial losses. Overall, the project seeks to provide valuable insights and practical recommendations for implementing machine learning solutions in fraud detection for banking transactions.