Thesis Abstract

Abstract
The increasing complexity and sophistication of financial fraud have posed significant challenges for traditional fraud detection methods. In response to this challenge, the application of Artificial Intelligence (AI) technologies has gained prominence in the financial sector. This thesis investigates the utilization of AI in detecting financial fraud, with a focus on enhancing detection accuracy and efficiency. The research explores the development and implementation of AI-based fraud detection models, leveraging machine learning algorithms and data analytics techniques to identify fraudulent activities within financial transactions. Chapter One provides an introduction to the research topic, presenting the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of utilizing AI in detecting financial fraud within the context of the evolving financial landscape. Chapter Two conducts an extensive literature review on AI applications in fraud detection. The chapter examines existing research, theories, and methodologies related to AI technologies, machine learning algorithms, data analytics, and their applications in financial fraud detection. The review highlights the evolution of AI-based fraud detection systems, their effectiveness, challenges, and best practices in detecting various types of financial fraud. Chapter Three focuses on the research methodology employed in developing AI-based fraud detection models. The chapter discusses the research design, data collection methods, sampling techniques, variables, and statistical analysis tools utilized in the study. It also explores the selection and implementation of machine learning algorithms and data processing techniques to enhance fraud detection capabilities. Chapter Four presents a comprehensive discussion of the findings obtained from the implementation of AI-based fraud detection models. The chapter analyzes the performance, accuracy, and efficiency of the developed models in detecting financial fraud, comparing them against traditional fraud detection methods. It also discusses the implications of the findings, potential challenges, and future research directions in the field of AI-based fraud detection. Chapter Five concludes the thesis by summarizing the key findings, implications, and contributions of the research. The chapter offers insights into the significance of utilizing AI in detecting financial fraud, the limitations of the study, and recommendations for future research and practical applications. Overall, this thesis contributes to the advancement of fraud detection technology by demonstrating the potential of AI in enhancing the detection of financial fraud and safeguarding the integrity of financial systems. Keywords Artificial Intelligence, Financial Fraud Detection, Machine Learning, Data Analytics, Fraud Detection Models, Financial Transactions, Fraud Detection Technology.

Thesis Overview