AI Fraud Detection and Financial Transparency: Enhancing Accountability in Financial Systems

Abstract

This study explores the role of artificial intelligence (AI) in fraud detection and its impact on financial transparency. By integrating AI technologies with financial auditing processes, organizations can significantly improve fraud identification and promote greater accountability. This paper examines existing AI methodologies, their effectiveness in detecting fraudulent activities, and the implications for financial transparency. Using a thematic synthesis approach, the research highlights the intersection of AI, ethics, and regulatory frameworks. The findings suggest that AI-driven fraud detection enhances transparency but also raises ethical considerations requiring balanced governance. The purpose of this study is to investigate how AI technologies contribute to fraud detection in financial systems and to assess their influence on promoting financial transparency and accountability. This research employed systematic literature PRIMA with a qualitative thematic synthesis methodology, reviewing peer-reviewed literature, case studies, and regulatory reports related to AI applications in fraud detection. The study integrates theoretical frameworks from information systems and ethics to analyze findings. AI techniques, including machine learning and neural networks, demonstrate high accuracy in identifying fraudulent patterns. The integration of AI enhances real-time monitoring and reduces human error. However, challenges such as data privacy, algorithmic bias, and ethical concerns emerge, necessitating comprehensive governance mechanisms. This paper uniquely synthesizes AI fraud detection technologies with financial transparency theories, contributing to interdisciplinary understanding and offering practical insights for policymakers and financial institutions. Financial institutions can leverage AI to improve fraud detection efficiency and transparency. Policymakers should develop ethical guidelines and regulatory frameworks to mitigate risks associated with AI implementation. Keywords: Artificial Intelligence, Fraud Detection, Financial Transparency, Machine Learning, Ethical Governance, Financial Accountability