AI-POWERED Internal Audit and Fraud Investigation

Overview

This is a 2-day, hands-on masterclass built for one purpose: putting Generative AI, analytics and automation directly into the hands of internal auditors who are responsible for catching what others miss. Day One is a complete AI-powered internal audit lifecycle, from planning through fieldwork, findings, reporting and follow-up. Day Two builds on that. Because the moment an internal auditor spots something that looks like fraud, the job changes, and this programme makes sure delegates know exactly what to do next: turning a raw allegation into evidence, analysis and a defensible investigation report. This isn’t a course about what AI can theoretically do. Every technique is demonstrated live, then applied immediately by delegates on their own laptop, to realistic audit and investigation scenarios. The room leaves with tools they’ve already used, not just notes about them.

Benefits of Attending

Apply AI throughout the internal audit lifecycle, from planning through follow-up.
Use AI to develop risk assessments, audit scopes, audit programs and testing procedures.
Apply AI to audit evidence, transaction analysis, control testing and working-paper preparation.
Generate stronger audit findings, root-cause analyses and executive reports.
Apply AI throughout the fraud investigation lifecycle, from allegation assessment through final reporting.
Use AI for document review, transaction analysis, evidence mapping and investigation planning.
Use AI to prepare and analyze investigative interviews and follow-up questions.
Develop AI-assisted fraud investigation reports supported by evidence.
Recognize hallucination, privacy, bias, confidentiality and evidential risks associated with AI.
Maintain human judgment, professional skepticism and human oversight when using AI in assurance and investigations.

Using AI across the complete internal audit engagement lifecycle: planning, fieldwork, findings, reporting and follow-up.

Module 1 — Foundations of AI-Powered Internal Audit

From traditional audit to AI-enabled, AI-powered and AI-driven auditing.
Generative AI, machine learning, analytics and automation in Internal Audit.
Where AI can support the internal audit lifecycle.
Human judgment versus AI-assisted decision-making.
Opportunities, limitations and risks of using AI in Internal Audit.
Data privacy, confidentiality and responsible AI use.
Maintaining professional skepticism when using AI.

Module 2 — AI-Powered Audit Planning

Using AI to understand the organization, process and audit universe.
AI-assisted preliminary research and background analysis.
Identifying emerging risks using AI.
AI-assisted risk assessment.
Developing risk-and-control matrices with AI.
Identifying key controls and control gaps.
Generating potential fraud-risk scenarios.
AI-assisted audit scoping and objective setting.
Creating audit programs and testing procedures.
Using AI to identify relevant regulations, policies and internal requirements.
Developing audit planning documents using Generative AI.

Module 3 — AI-Powered Audit Fieldwork and Testing

Using AI to analyze policies, procedures and business documents.
AI-assisted document review and information extraction from contracts, invoices and supporting documents.
Comparing policies against actual practices.
AI-assisted control testing and automated audit tests.
Using AI for transaction analysis, exception detection and anomaly identification.
Duplicate-payment and duplicate-record analysis.
Outlier identification and journal-entry analysis.
Procurement and expense analysis.
AI-assisted sampling and sample selection.
Continuous auditing and continuous monitoring.
Using AI to summarize interviews and meetings.
AI-assisted evidence analysis and working-paper preparation.
Cross-referencing evidence to audit findings.

Module 4 — AI-Powered Audit Findings and Root Cause Analysis

Using AI to identify patterns across audit observations.
Developing audit findings from multiple evidence sources.
AI-assisted root cause analysis and distinguishing symptoms from underlying causes.
Assessing risk and potential impact.
Developing practical recommendations.
Challenging AI-generated conclusions and validating findings against evidence.

Module 5 — AI-Powered Audit Reporting

Using Generative AI to draft audit reports.
Converting technical findings into executive-level language.
Developing executive summaries and prioritizing findings for management.
Improving clarity, consistency and tone.
Generating management insights from audit results.
Creating dashboards and visual summaries.
AI-assisted presentation of audit results.
Tailoring reports for the Audit Committee, senior management and process owners.
Verifying AI-generated reports for accuracy and unsupported conclusions.

Module 6 — AI-Powered Audit Follow-Up

Using AI to monitor management action plans.
Reviewing remediation evidence and comparing management responses against agreed actions.
Identifying overdue and incomplete actions.
AI-assisted assessment of remediation effectiveness.
Continuous monitoring of previously identified risks.
Automating follow-up communication.
Creating management action tracking dashboards.
Identifying recurring audit issues.

Using AI to support the investigation lifecycle from allegation assessment and planning through evidence analysis, interviews, conclusions and reporting.

Module 1 — Foundations of AI-Powered Fraud Investigation

How AI is transforming fraud examinations and investigations.
Generative AI, machine learning and analytics in investigations.
AI opportunities across the investigation lifecycle.
Human investigator versus AI-assisted investigation.
Professional skepticism when using AI.
Confidentiality and protection of investigation information.
Bias, hallucination and evidential reliability.
Maintaining human oversight of AI-generated conclusions.

Module 2 — AI-Powered Investigation Planning

Converting allegations into investigation hypotheses.
Using AI to analyze complaints, whistleblower reports and allegations.
Identifying initial fraud indicators and red flags.
Developing and testing fraud hypotheses.
Identifying potential fraud schemes.
AI-assisted fraud risk assessment.
Identifying persons, transactions, systems and documents of interest.
Developing investigation objectives and establishing scope.
Creating investigation plans, evidence request lists and timelines.
Developing investigation interview strategies.
Prioritizing investigative procedures using AI.

Module 3 — AI-Powered Evidence Collection and Document Analysis

Using AI to review large volumes of documents.
AI-powered document classification and extraction of names, dates, amounts and relationships.
Reviewing invoices, contracts, purchase orders and payments.
Identifying inconsistent, suspicious, duplicate or potentially altered documents.
Comparing documents across multiple sources and identifying missing documentation.
AI-powered email and communication analysis.
Searching for suspicious keywords and behavioral patterns.
Timeline reconstruction using AI.
Evidence summarization and creation of evidence matrices.

Module 4 — AI-Powered Fraud Data Analytics

Using AI to identify suspicious transactions and fraud patterns.
Outlier and anomaly detection.
Duplicate payment analysis.
Vendor and employee relationship analysis.
Round-number and unusual-value analysis.
Unusual timing and transaction sequencing.
Benford’s Law-supported analysis.
Journal-entry analysis.
Procurement fraud, expense reimbursement, payroll and revenue fraud indicators.
Financial statement fraud indicators.
Network and relationship analysis.
Using AI to prioritize investigative leads.

Module 5 — AI-Powered Investigation Interviews

Using AI to prepare for investigative interviews.
Developing subject-specific interview questions and interview sequences.
Identifying gaps and inconsistencies before interviews.
AI-assisted analysis of interview transcripts.
Comparing statements from multiple interviewees.
Identifying contradictions and follow-up questions.
Mapping interview statements against documentary evidence.
Limitations of AI in assessing credibility and deception.
Maintaining investigator judgment and professional skepticism.

Module 6 — AI-Powered Fraud Investigation Analysis

Connecting people, entities, transactions and documents.
Developing investigation timelines and relationship maps.
AI-assisted link analysis.
Testing investigation hypotheses.
Identifying evidence supporting and contradicting allegations.
Identifying additional investigative leads.
Quantifying potential fraud losses.
Separating facts, assumptions and unresolved issues.
Avoiding confirmation bias when using AI.
Maintaining an investigation evidence trail.

Module 7 — AI-Powered Fraud Investigation Reporting

Structuring professional investigation reports.
AI-assisted drafting of investigation findings.
Developing executive summaries.
Presenting the allegation, scope, methodology and procedures performed.
Separating facts from allegations and interpretations.
Linking findings to supporting evidence.
Presenting financial impact and quantified losses.
Developing investigation timelines, exhibits, tables and supporting schedules.
Drafting recommendations for remediation.
Quality review of AI-generated investigation reports.
Preventing unsupported or defamatory conclusions.
Maintaining factual, neutral and evidence-based language.

Practical Exercise: Use AI to transform investigation evidence into a structured professional fraud investigation report.

AI-POWERED Internal Audit and Fraud Investigation

Overview

Benefits of Attending

Join us at our events

Office

Menu