ACCT 3302 · Fraud Red Flags and the Fraud Triangle in Operation

Led by Felix Aubrey Sharpley Simulacrum

1. Module 2 ○ Open

   Fraud Red Flags and the Fraud Triangle in Operation

   Led by Felix Aubrey Sharpley Simulacrum

   The question

   Fraud red flags and the analytical techniques that surface them. The module covers the four-layer red-flag framework (transaction profile, counterparty profile, temporal pattern, statistical pattern), Benford's Law as a leading-digit anomaly test, the typical red-flag patterns by scheme type (expense fraud, billing fraud, payroll fraud, financial-statement fraud), modern data-analytics tools (IDEA, ACL, Power BI, Python pandas), and the integration with whistleblowing infrastructure as the single most consequential anti-fraud control.

   Outcome

   The student can identify the fraud-triangle conditions in a described scenario; can apply Benford's Law to a dataset of transactions and interpret the chi-square result; and can structure a red-flag analysis plan for a typical forensic engagement. (Fraud red flags and analytics)

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   Practice scenarios

   Benford's Law on Halberd Procurement Data

   You analyse a year of procurement transactions from a UK retail group's procurement subsidiary using Benford's Law and identify a leading-digit pattern consistent with threshold-avoidance kickback arrangements. The work tests whether you can interpret the chi-square result, recommend the next supplier-concentration analytical step, and resist a defensive head-of-procurement's challenge that *Benford is just one statistical test*.

   Your goals

   • Compare observed vs expected: digits 1–3 under-represented (24% vs 30%; 16% vs 18%; 13% vs 13% — first two materially low); digits 4 and 5 over-represented (11% vs 10%; 12% vs 8%); digits 6–9 in line with expectation.
   • Calculate the chi-square statistic: Σ ((observed − expected)² / expected) summed across the 9 digits; with 8 degrees of freedom; compare to critical value at 95% (15.51) and 99% (20.09); the result here would likely be significant (the digit-5 over-representation alone contributes substantially).
   • Frame the inference: the deviation pattern (low 1, elevated 4–5) is consistent with *threshold avoidance* — entries clustered just under £5,000 (a typical procurement-approval threshold) producing excess leading-4 and leading-5 amounts, with corresponding deficit in leading-1 (which would dominate amounts under £2,000 in a natural distribution).
   • Recommend the next analytical step: extract all transactions in the £4,000–£4,999 range; profile by supplier; identify suppliers concentrated in this range; cross-reference with employee expense-claim approvals; investigate suppliers with high concentration in the £4,500–£4,999 band (just-below-threshold pattern).
   • Frame as a 1,000-word analytical memo for the engagement partner.

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