Implementasi Artificial Intelligence dalam Audit Forensik untuk Pengembangan Model Deteksi Fraud Berbasis Anomali Transaksi

Authors

Ulumiddin Universitas 17 Agustus 1945 Surabaya
Eka Hadiwijaya Universitas 17 Agustus 1945 Surabaya
Amiartuti Kusmaningtyas Universitas 17 Agustus 1945 Surabaya
Maria Yovita R. Pandin Universitas 17 Agustus 1945 Surabaya

DOI:

Abstract

This study aims to analyze the implementation of Artificial Intelligence (AI) in forensic auditing as a foundation for developing an anomaly-based fraud detection model. Using the Systematic Literature Review (SLR) method under the PRISMA framework, this research examines 53 peer-reviewed articles indexed in Scopus, ScienceDirect, and Sinta to identify global research trends on AI applications in fraud detection. Bibliometric analysis using VOSviewer reveals four dominant clusters: AI-Driven Fraud Detection, Forensic Accounting Integration, Machine Learning Anomaly Recognition, and Ethical Transparency and Explainable AI. The findings indicate that integrating machine learning and deep learning significantly enhances fraud detection accuracy, accelerates auditing processes, and strengthens algorithmic transparency. The study concludes that AI implementation serves as a strategic foundation for developing adaptive, predictive, and accountable forensic audit systems in the digital era, while also expanding the theoretical basis of fraud theory toward a technology-driven audit framework.

Keywords: Artificial Intelligence, Forensic Audit, Fraud Detection.

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