AI Internal Audit M10: AI Data Analytics – PDF

VIDEO DEMO

BENEFITS OF THIS DOWNLOADABLE PDF DOCUMENT

1. Equips auditors to apply anomaly detection and pattern recognition across full data populations.
2. Moves audit testing beyond rule-based sampling toward intelligent, comprehensive analysis.
3. Provides transaction analysis templates and analytics prompts ready for immediate engagement use.

AUDIT MANAGEMENT PDF DESCRIPTION

AI-Powered Data Analytics for Auditors PDF: Learn anomaly detection, pattern recognition & transaction analysis to enhance internal audits. Download the expert guide. AI Internal Audit M10: AI Data Analytics is a 40-page PDF document available for immediate download upon purchase.

AI-powered analytics allow auditors to examine entire populations rather than samples – and to find what traditional testing misses. This is Module 10 of the AI-Powered Internal Audit Professional Series, a 15-module program developed and published by Business Excellence to build practical, applied AI capability in audit teams.
This module shows internal auditors how AI and advanced analytics transform audit execution. It traces the evolution from traditional rule-based sampling to comprehensive, intelligent data analysis, and then equips auditors with the techniques that matter most in practice: anomaly detection to automatically flag transactions, behaviors, and data points that deviate from established norms across whole populations; and pattern recognition to uncover hidden relationships, systematic issues, and recurring patterns that may indicate fraud schemes or control failures.
The module is built around application. It provides a transaction analysis template structured to identify anomalies and outliers, flag patterns suggesting control weaknesses such as segregation-of-duties violations or authorization bypass, isolate specific transactions requiring follow-up, and produce relevant summary statistics. Common applications are addressed directly – journal entry testing, procurement transaction analysis, payroll testing for ghost employees, expense report review, and duplicate payment detection.
Reflecting Deloitte research indicating that a substantial majority of audit functions plan to invest in data analytics, the module positions analytics not as a specialist niche but as a core competence the modern audit function must build. It also reinforces responsible practice: AI-surfaced findings are leads for audit testing, not conclusions, and require professional follow-up and verification.
The content is aligned with the IIA Global Internal Audit Standards 2024 and the NIST AI Risk Management Framework, and reflects current ISACA guidance on data analytics in audit. It is written in executive-level language for auditors, audit managers, and Chief Audit Executives.
Delivered as a professional PowerPoint presentation with full speaker notes, the module includes transaction analysis templates, analytics prompt templates, a data request checklist, real-world examples from cited sources, and a 10-question knowledge assessment quiz – giving audit teams a practical analytics capability they can deploy immediately.

Source: Best Practices in Audit Management PDF: AI Internal Audit M10: AI Data Analytics PDF (PDF) Document, Amer Morgan