To what extent can Artificial Intelligence be leveraged to enhance fraud detection and prevention mechanisms within accounting systems, and what are the implications for financial security?
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Mentor Information
Dr. Jong Chool Park
Description
This thesis examines the potential of Artificial Intelligence (AI) to revolutionize fraud detection and prevention mechanisms in accounting systems, aiming to understand its extent and implications for financial security. With the increasing sophistication of financial frauds, traditional methods of detection have often fallen short, necessitating the exploration of more advanced solutions. AI, with its ability to learn from data patterns and identify anomalies, presents a promising avenue for bolstering the integrity and security of financial systems. This research identifies the current state of AI application in fraud detection, including machine learning algorithms, data mining techniques, and neural networks. The study also explores the practical implications of integrating AI into existing accounting frameworks, including the challenges of implementation, the need for regulatory adaptation, and the impact on professional practices and employment within the accounting sector. Finally, the thesis discusses the broader implications of AI-enhanced fraud detection for financial security, highlighting potential benefits such as reduced financial losses, enhanced investor confidence, and a more stable financial environment. This research contributes to the academic and practical understanding of AI’s role in transforming fraud detection mechanisms within accounting systems, offering insights for policymakers, practitioners, and scholars interested in the intersection of technology, finance, and security.”
To what extent can Artificial Intelligence be leveraged to enhance fraud detection and prevention mechanisms within accounting systems, and what are the implications for financial security?
This thesis examines the potential of Artificial Intelligence (AI) to revolutionize fraud detection and prevention mechanisms in accounting systems, aiming to understand its extent and implications for financial security. With the increasing sophistication of financial frauds, traditional methods of detection have often fallen short, necessitating the exploration of more advanced solutions. AI, with its ability to learn from data patterns and identify anomalies, presents a promising avenue for bolstering the integrity and security of financial systems. This research identifies the current state of AI application in fraud detection, including machine learning algorithms, data mining techniques, and neural networks. The study also explores the practical implications of integrating AI into existing accounting frameworks, including the challenges of implementation, the need for regulatory adaptation, and the impact on professional practices and employment within the accounting sector. Finally, the thesis discusses the broader implications of AI-enhanced fraud detection for financial security, highlighting potential benefits such as reduced financial losses, enhanced investor confidence, and a more stable financial environment. This research contributes to the academic and practical understanding of AI’s role in transforming fraud detection mechanisms within accounting systems, offering insights for policymakers, practitioners, and scholars interested in the intersection of technology, finance, and security.”
