Thesis Abstract

Abstract
The rapid advancement of technology has significantly transformed the banking sector, enabling financial institutions to enhance their operations and services. However, this progress has also led to a rise in sophisticated fraudulent activities, challenging the security measures in place. In response to this growing concern, the application of artificial intelligence (AI) in fraud detection has emerged as a promising solution for banks to strengthen their defenses against fraudulent activities. This thesis explores the utilization of AI techniques, such as machine learning and data analytics, in detecting and preventing fraud within banking systems. The research begins with an introduction that outlines the background of the study, highlighting the increasing prevalence of fraud in the banking sector and the need for more advanced detection methods to combat this threat. The problem statement identifies the limitations of traditional fraud detection systems and emphasizes the importance of integrating AI technologies to enhance the accuracy and efficiency of fraud detection processes. The objectives of the study focus on evaluating the effectiveness of AI in detecting fraud, improving fraud prevention strategies, and enhancing overall security in banking systems. The literature review in Chapter Two provides an in-depth analysis of existing research and studies related to AI in fraud detection within the banking sector. The review encompasses various AI techniques, including neural networks, decision trees, and anomaly detection algorithms, highlighting their strengths and limitations in detecting fraudulent activities. Furthermore, the chapter examines real-world applications of AI in fraud detection and the outcomes achieved by financial institutions that have implemented these technologies. Chapter Three outlines the research methodology adopted for this study, detailing the data collection process, selection of AI algorithms, and evaluation criteria used to measure the effectiveness of AI in fraud detection. The methodology incorporates a combination of quantitative analysis, case studies, and simulations to assess the impact of AI on fraud detection accuracy and efficiency. Additionally, the chapter discusses the ethical considerations and data privacy measures implemented to ensure the integrity and security of the research findings. Chapter Four presents a comprehensive discussion of the research findings, highlighting the performance of AI algorithms in detecting fraudulent activities within banking systems. The chapter analyzes the results obtained from the evaluation process, comparing the accuracy rates, false positive rates, and detection times of different AI models. The discussion also addresses the challenges and limitations encountered during the implementation of AI in fraud detection and proposes recommendations for overcoming these obstacles. Finally, Chapter Five concludes the thesis by summarizing the key findings, implications, and contributions of the study. The conclusion emphasizes the significance of AI in enhancing fraud detection capabilities in banking systems and its potential to revolutionize cybersecurity practices within the financial industry. The thesis concludes with recommendations for future research directions and practical applications of AI in combating fraud in banking systems. In conclusion, the application of artificial intelligence in fraud detection represents a critical advancement in strengthening security measures and protecting financial institutions from fraudulent activities. This thesis contributes to the growing body of knowledge on AI technologies in the banking sector and provides valuable insights for policymakers, researchers, and industry professionals seeking to leverage AI for enhancing fraud detection capabilities.

Thesis Overview