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Smart Forensics: Tracing the Rise of Artificial Intelligence in Forensic Accounting, Fraud Detection and Risk Management

Author

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  • Zaiza Norsuriati Zainal Zakaria
  • Nor Haliza Che Hussain
  • Yunita Awang
  • Shazalina Mohamed Shuhidan

Abstract

This study examines the evolution of academic research at the intersection of artificial intelligence (AI), risk management, and forensic accounting by analysing publication trends, main themes, and knowledge gaps. A bibliometric analysis was conducted on 261 Scopus-indexed publications from 2002 to July 2025 using tools including Microsoft Excel, VOS viewer, and Publish or Perish to assess citation patterns, authorship networks, and keyword relationships. The results indicate a notable increase in publications after 2017. "Forensic accounting" and "fraud" appearing as the most recurrent keywords, along with growing academic attention on emerging technologies such as AI, blockchain, big data, and data analytics. Journal articles represent the predominant publication type, particularly in the Business, Management and Accounting subject area, while Jordan stands out as a leading contributor with several influential authors and institutions. The analysis identifies eight key research themes covering governance, fraud control, professional competency, financial crime, and technology-enhanced audit practices. Even though this study limited to English-language publications in the Scopus database, this research provides valuable insights into current developments and future directions, offering guidance for academic research, policy formulation, and practical applications in forensic accounting and fraud detection.

Suggested Citation

  • Zaiza Norsuriati Zainal Zakaria & Nor Haliza Che Hussain & Yunita Awang & Shazalina Mohamed Shuhidan, 2025. "Smart Forensics: Tracing the Rise of Artificial Intelligence in Forensic Accounting, Fraud Detection and Risk Management," Information Management and Business Review, AMH International, vol. 17(2), pages 51-68.
  • Handle: RePEc:rnd:arimbr:v:17:y:2025:i:2:p:51-68
    DOI: 10.22610/imbr.v17i2(I)S.4565
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