Applying Machine Learning Algorithms for Fraud Detection in Financial Transactions

Thesis Abstract

Abstract
The rise of digital transactions in the financial sector has brought about a significant increase in fraudulent activities. To combat this issue, the application of machine learning algorithms for fraud detection in financial transactions has gained prominence in recent years. This thesis explores the effectiveness of various machine learning algorithms in detecting fraudulent activities in financial transactions. Chapter One provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter Two presents a comprehensive literature review covering ten key aspects related to machine learning algorithms, fraud detection, and financial transactions. Chapter Three outlines the research methodology, including data collection methods, data preprocessing techniques, algorithm selection criteria, model training, and evaluation metrics. Furthermore, it discusses the limitations and ethical considerations of the research process. In Chapter Four, the findings of the study are discussed in detail, presenting the results of applying various machine learning algorithms to a dataset of financial transactions for fraud detection. The chapter analyzes the performance of each algorithm, comparing their accuracy, precision, recall, and F1-score. Additionally, it examines the computational efficiency and scalability of the algorithms in real-world scenarios. Finally, Chapter Five summarizes the research findings, discusses the implications of the results, and provides recommendations for future research in the field of fraud detection using machine learning algorithms. The conclusion reflects on the effectiveness of machine learning in combating fraud in financial transactions and highlights the importance of continuous research and development in this area to stay ahead of evolving fraudulent activities. Overall, this thesis contributes to the existing body of knowledge by providing insights into the application of machine learning algorithms for fraud detection in financial transactions and offers practical implications for financial institutions and regulatory bodies to enhance their fraud detection capabilities.