Forensic Accounting and Fraud Detection
Red flags, investigation techniques and the fraud triangle.
Taught by Pooja Adhikari · Chartered Accountant
What you'll learn
- Explain the fraud triangle and classify schemes with the occupational fraud tree
- Identify red flags for billing, payroll, skimming and financial statement fraud
- Apply Benford's law in Python to flag anomalous transaction populations
- Run analytics for vendor-employee matches and high-risk journal entries
- Plan an investigation, preserve evidence and sequence interviews
- Write a factual forensic report and recommend preventive controls
About this course
Forensic Accounting and Fraud Detection is for accountants, auditors and compliance staff who want to spot fraud, investigate it methodically and report findings that stand up to scrutiny. You will learn the fraud triangle and the occupational fraud tree, the schemes behind asset misappropriation, corruption and financial statement fraud, and the red flags that expose them. The course is hands-on: you will apply Benford's law in Python, run data analytics for ghost vendors and risky journal entries, preserve evidence, plan interviews and write a clear forensic report. It is useful groundwork for the Certified Fraud Examiner (CFE) credential. Independent study material, not affiliated with or endorsed by the ACFE. You should know double-entry bookkeeping; some Python helps but is not required.
Course content
7 lessons- 1What Forensic Accountants DoThe forensic accounting role, how it differs from audit, and the three families of occupational fraud.Preview
- 2Why People Commit Fraud: The Fraud TrianglePressure, opportunity and rationalisation, the fraud diamond, and how to turn theory into a fraud risk assessment.
- 3Common Fraud Schemes and Their Red FlagsHow billing, payroll, expense, skimming and financial statement schemes work, and the warning signs each leaves.
- 4Benford's Law and Digit AnalysisWhy leading digits follow a predictable pattern, how to test a population in Python, and how to read the results.
- 5Data Analytics for Fraud DetectionFull-population tests for ghost vendors, duplicates, round amounts and risky journal entries using SQL and pandas.
- 6Evidence, Investigation and InterviewingPredication, planning, preserving evidence and conducting interviews that are fair, lawful and effective.
- 7Reporting Findings and Preventing FraudWriting a forensic report, giving expert evidence, and building controls that make fraud harder to commit and hide.