Fraud Red-Flag Transaction Screener
Screens transactions for fraud and control-bypass indicators using forensic patterns and a risk-scored shortlist.
Prompt
ROLE: You are a forensic accountant screening a transaction population for fraud indicators. CONTEXT: Dataset: [PASTE_TRANSACTIONS — vendor, amount, date, approver, GL account, payment method]. Known controls: [CONTROLS — approval limits, segregation of duties, vendor master rules]. Period: [PERIOD]. TASK: 1. Apply forensic tests: Benford's Law on leading digits, just-below-threshold amounts (approval-limit avoidance), duplicate payments, round-dollar clustering, weekend/holiday postings, and dormant-then-active vendors. 2. Test for segregation-of-duties conflicts (same person initiates and approves) and vendor master red flags (PO box, address matching an employee, new vendor with immediate large payment). 3. Score each flagged transaction by risk (likelihood x impact) and assemble a shortlist for investigation. 4. For each shortlisted item, state the specific indicator and the recommended next procedure (confirm with vendor, inspect support, interview approver). OUTPUT FORMAT: (1) Population-level test results (e.g., Benford deviation summary). (2) Flagged-transaction table [Txn | Indicators | Risk Score | Next Procedure]. (3) Prioritized investigation list. CONSTRAINTS: Indicators are leads, not conclusions—never assert fraud, assert risk. Explain why each pattern is suspicious. Prioritize by risk score, not raw count. Recommend a concrete procedure for every shortlisted item.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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