Thesis Abstract

Abstract
The rise of technology has revolutionized various industries, including accounting. This thesis explores the application of artificial intelligence (AI) in fraud detection and prevention within the accounting domain. The research aims to investigate how AI technologies can be leveraged to enhance the detection and prevention of fraudulent activities in financial transactions. The study delves into the current state of fraud detection methods in accounting, highlighting their limitations and the increasing challenges posed by sophisticated fraudulent schemes. The literature review examines existing research on AI applications in fraud detection across different industries and identifies the gaps that exist within the accounting field. By synthesizing the literature, this study aims to provide a comprehensive understanding of how AI can be effectively utilized to combat fraud in accounting practices. Various AI technologies such as machine learning, natural language processing, and anomaly detection algorithms are explored for their potential in enhancing fraud detection capabilities in accounting systems. The research methodology section outlines the approach taken to investigate the research questions and achieve the study objectives. The methodology includes data collection methods, sample selection criteria, and the analytical techniques employed to evaluate the effectiveness of AI-driven fraud detection systems. The study utilizes both qualitative and quantitative research methods to provide a holistic analysis of the research topic. The findings from the study reveal the efficacy of AI technologies in improving fraud detection and prevention mechanisms in accounting. The discussion of findings section presents a detailed analysis of the results, highlighting the key findings and their implications for accounting practitioners and organizations. The study also discusses the practical implementation challenges and considerations associated with integrating AI-based fraud detection systems into accounting processes. In conclusion, the thesis summarizes the key findings and contributions of the research, emphasizing the significance of AI in enhancing fraud detection and prevention in accounting. The study underscores the importance of adopting advanced technologies to combat evolving fraudulent activities and protect financial integrity within organizations. Recommendations for future research and practical implications for accounting professionals are also discussed to guide further advancements in the field of AI-driven fraud detection and prevention. Keywords Artificial Intelligence, Fraud Detection, Prevention, Accounting, Machine Learning, Anomaly Detection, Financial Transactions, Technology Integration.

Thesis Overview