AI in Fraud Analytics and Audit: Probabilistic Machines in Deterministic Systems

An ACFE-Aligned Perspective

De: Alessio Faccia

Narrado por: Virtual Voice

This book challenges the illusion of precision that artificial intelligence often presents in financial investigation. It scrutinises the structural limitations of probabilistic models in audit and fraud detection, particularly when juxtaposed with forensic standards set by the ACFE. The result is a highly technical exploration of where algorithmic logic meets legal accountability.

Designed for forensic accountants, auditors, compliance officers, and AI professionals, this text dissects the assumptions embedded in machine-generated outputs. Readers are guided through model behaviours, detection patterns, language model vulnerabilities, and data integrity concerns—without falling into the trap of overhyping automation.

The author draws upon interdisciplinary research to expose areas where AI may misclassify, overlook anomalies, or reinforce bias. The text also develops a unique risk taxonomy tailored for forensic audit applications, drawing parallels with legal standards of proof and audit trail requirements. It offers practical frameworks for incorporating probabilistic tools in a way that enhances, rather than undermines, evidentiary rigour.

Whether reviewing neural networks, unsupervised anomaly detection, or XAI (Explainable AI), the book retains a critical stance on over-reliance on opaque systems. Each chapter advances specific criteria for evaluating the credibility, traceability, and forensic admissibility of AI-generated conclusions.

Rather than assuming machines are neutral, this volume interrogates what gets coded in and what is systematically filtered out. A must-read for professionals unwilling to compromise the rigour of audit and fraud examination for the sake of automation.