AI-Powered Audit & Assurance
Transform audit sampling into continuous AI monitoring — analysing 100% of transactions instead of statistical samples to improve risk detection.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Auditors review statistical samples of transactions (typically 5-10% of total volume). Testing relies on manual vouching, recalculation, and spreadsheet-based analytics. Risk assessment is largely judgmental. Audit findings are reactive — issues are found after they occur. An annual audit cycle takes 3-4 months of fieldwork.
After
AI analyses 100% of transactions continuously throughout the year, flagging anomalies and potential misstatements in real-time. Predictive risk models guide audit focus to highest-risk areas. Auditors spend time investigating AI-flagged exceptions rather than performing routine testing. Continuous monitoring replaces point-in-time testing.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Design AI Audit Approach
3 weeksMap current audit procedures and identify which tests can be AI-augmented or automated. Define anomaly detection criteria aligned with auditing standards (ISA). Design AI analytics for each material account (revenue, expenses, payroll, inventory). Get methodology approved by firm leadership and quality team.
Build Data Pipelines
4 weeksEstablish secure data connections to client ERP/accounting systems. Build ETL pipelines that normalise data from different systems into standardised formats. Implement data quality checks and validation rules. Address data privacy and confidentiality requirements.
Develop AI Audit Analytics
6 weeksBuild anomaly detection models for: unusual journal entries, revenue recognition patterns, expense outliers, related party transactions, and segregation of duties violations. Train models on historical audit data and known misstatements. Validate against completed audits.
Pilot on Audit Engagement
6 weeksDeploy AI analytics on a live audit engagement alongside traditional procedures. Compare AI-flagged exceptions against traditional testing results. Measure: additional issues found, false positive rate, time savings, and audit quality indicators.
Scale to Continuous Monitoring
3 weeks + ongoingMove from point-in-time analysis to continuous monitoring for recurring engagements. Build client dashboards showing real-time risk indicators. Establish AI audit methodology standards for the firm. Train audit teams on AI-assisted procedures.
Tools Required
Expected Outcomes
Analyse 100% of transactions vs. 5-10% statistical sample
Detect 25-40% more anomalies and potential misstatements
Reduce routine testing time by 50-60%
Shift auditor focus from data gathering to judgment and investigation
Enable continuous monitoring vs. annual point-in-time testing
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
Yes, when properly designed. ISA 520 (Analytical Procedures) and ISA 530 (Audit Sampling) allow for computer-assisted audit techniques. AI analytics can serve as substantive analytical procedures or risk assessment procedures. The key is documenting the AI methodology, validating results, and maintaining professional skepticism.
Audit firms already handle sensitive client data under strict confidentiality requirements. AI analytics should use the same security infrastructure — encrypted connections, access controls, data retention policies, and client-specific segregation. Many firms process data in isolated environments that are destroyed after each engagement.
Ready to Implement This Workflow?
Our team can help you go from guide to production — with hands-on implementation support.