Exploration of AI-driven anomaly detection in forensic accounting reveals improved fraud detection, suggesting a shift from traditional methods.
The integration of Artificial Intelligence (AI) into forensic accounting is transforming the landscape of fraud detection and financial investigation. Traditional forensic accounting methods often rely on manual scrutiny, which can be time-consuming, error-prone, and insufficient to detect complex or subtle financial irregularities. This paper explores how AI, particularly machine learning and anomaly detection algorithms, enhances the ability to identify fraudulent patterns hidden within vast and complex financial datasets. By focusing on data anomalies—irregularities or deviations from expected patterns—AI-driven forensic tools can uncover indicators of fraud that may elude conventional analysis.
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Amol Kundalik Sathe (2020) studied this question.
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