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AI Internal Audit M12: AI Fraud Detection – PDF
PDF document 42 Pages
VIDEO DEMO
BENEFITS OF THIS DOWNLOADABLE PDF DOCUMENT
- Equips auditors to apply AI techniques to fraud red-flag detection across large transaction populations.
- Connects AI-surfaced indicators to structured, defensible investigation procedures.
- Provides fraud analytics prompts and investigation templates ready for immediate use.
FRAUD PDF DESCRIPTION
Fraud detection is one of the highest-value applications of AI in the audit and investigation function. This is Module 12 of the AI-Powered Internal Audit Professional Series, a 15-module program developed and published by Business Excellence to build practical, applied AI capability.
This module shows internal auditors and investigators how AI strengthens both the detection and the investigation of fraud. On the detection side, it applies the anomaly detection and pattern recognition techniques developed earlier in the series specifically to fraud indicators – flagging transactions and behaviors that deviate from established norms, and surfacing patterns consistent with known fraud schemes such as segregation-of-duties violations, authorization bypass, ghost employees, duplicate payments, and process non-compliance.
On the investigation side, the module addresses how AI supports the investigative process: rapidly summarizing large volumes of evidence, organizing transaction trails, identifying relationships across data sources, and helping structure investigative documentation. Throughout, the module is disciplined about the boundary between an AI-generated lead and an audit or investigation conclusion: AI surfaces indicators that warrant professional follow-up; it does not establish that fraud occurred.
The module provides ready-to-use fraud analytics prompts and a transaction analysis structure adapted for fraud work, covering common applications including journal entry testing, procurement analysis, payroll testing, and expense review. It also addresses the responsible-use considerations specific to fraud work – evidence integrity, confidentiality, and the need for verifiable, defensible analysis that can withstand scrutiny.
The content is grounded in current professional guidance, aligned with the IIA Global Internal Audit Standards 2024 and the NIST AI Risk Management Framework, and reflects ISACA resources on AI-enabled audit and investigation. It is written in executive-level language suitable for internal auditors, fraud examiners, and Chief Audit Executives.
Delivered as a professional PowerPoint presentation with full speaker notes, the module includes fraud analytics prompt templates, an investigation documentation template, a fraud red-flag checklist, real-world examples from cited sources, and a 10-question knowledge assessment quiz – giving audit and investigation teams a practical AI capability for one of their most important mandates.
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Source: Best Practices in Fraud PDF: AI Internal Audit M12: AI Fraud Detection PDF (PDF) Document, Amer Morgan
ABOUT THE AUTHOR
Amer Morgan is the founder of Business Excellence, Audit Consultant and Governance Advisor with over 30 years of practice in internal audit, enterprise risk management, and corporate governance across Qatar, Egypt, the UAE, and the USA. He currently leads the internal audit function for a sovereign-wealth-fund-owned food security group in Doha, with responsibility for board-level assurance across ... [read more]
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