AI-Powered Control Testing

What if you could test more internal controls, identify exceptions faster, and produce audit evidence that stands up to professional scrutiny? AI-Powered Control Testing is a practical guide for internal auditors who want to move beyond traditional sampling and discover how artificial intelligence and data analytics can transform control testing. Written by experienced internal audit professional Salih Ahmed Islam, MBA, CIA, GRCP, this book combines practical methodologies, structured testing procedures, and realistic business scenarios to help auditors work more efficiently without compromising audit quality. WHAT YOU WILL LEARN Discover 30 hands-on internal audit tests covering eight essential business areas: Procurement and Accounts Payable Supplier Master Data and Vendor Risk Sales, Pricing, and Accounts Receivable Travel and Expense Management Payroll and Human Resources Inventory and Warehouse Operations Finance and General Ledger Controls IT General Controls and Access Management FROM DATA TO DEFENSIBLE AUDIT EVIDENCE Each practical testing exercise provides a business scenario, control objectives, relevant data fields, screening rules, AI-assisted analysis prompts, validation procedures, and guidance on documenting exceptions. Learn how to identify duplicate invoices, suspicious purchasing patterns, approval violations, unusual expenses, unauthorized access, and other potential control weaknesses. BEYOND TRADITIONAL AUDITING The book also explores continuous controls monitoring, anomaly detection, full-population testing, data reliability, and the responsible use of AI in audit engagements. A practical 30-day implementation roadmap helps readers introduce AI-assisted control testing into their audit activities. WHO SHOULD READ THIS BOOK? Ideal for internal auditors, audit managers, risk management professionals, compliance officers, finance professionals, and anyone interested in modernizing internal control testing. No expensive AI subscription is required to understand and apply the methodologies. Turn AI-generated insights into reliable, evidence-based audit conclusions. Note: The book references a separate synthetic practice dataset and Python testing engine. These companion files are not included in the ebook.