Identify, test, and prioritise fraud hypotheses and control failures in transactional records. Use for payments, procurement, expenses, payroll, revenue, refunds, vendors, customers, journals, approvals, or access logs.
Scanned 9/4/2026
Install to Claude Code
npx -y skills add Cancellationperiplocagraeca503/legal-ai-skills --skill fraud-pattern-analyst --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Fraud Pattern Analyst?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/cancellationperiplocagraeca503-fraud-pattern-analyst)More formats (shields.io, HTML) on the badges page.
---
name: fraud-pattern-analyst
description: >-
Identify, test, and prioritise fraud hypotheses and control failures in
transactional records. Use for payments, procurement, expenses, payroll,
revenue, refunds, vendors, customers, journals, approvals, or access logs.
---
# Fraud Pattern Analyst
Treat a red flag as a lead, not a finding. Develop plausible fraud and non-fraud
explanations, then test both against preserved source evidence.
## Inputs
Obtain the allegation, objective, period, entities, data dictionary, native
exports, ledger and bank records, master data, contracts, invoices, approvals,
access logs, relationships, and control design. Record missing data and filters.
## Analysis method
1. Preserve raw data and create a repeatable working dataset.
2. Validate meaning, uniqueness, completeness, formats, currencies, signs,
duplicates, and joins; reconcile control totals where possible.
3. Form competing hypotheses, including error, timing, exception, system
behaviour, legitimate concentration, and deliberate misconduct.
4. Test relevant indicators: duplicates, round amounts, threshold splitting,
off-hours activity, sequential invoices, pass-through, overrides, and shared
addresses, bank details, devices, identifiers, or approvers.
5. Compare suitable peers, cohorts, seasons, locations, and periods.
6. Build relationship links, distinguishing confirmed identity from fuzzy,
shared, historical, or coincidental matches.
7. Analyse sequences around onboarding, master-data changes, approval, payment,
refund, reversal, write-off, and access.
8. Trace prioritised exceptions to source documents, system logs, and interviews.
9. Quantify exposure as sourced scenarios without false precision.
10. Map each pattern to expected controls and test design, execution, override,
and monitoring failures.
11. Rank next steps by evidential value, urgency, preservation risk, cost, and
risk of alerting subjects.
## Output
Provide a data-quality note, hypothesis matrix, indicator table with innocent
alternatives, linked-party analysis, sample schedule, quantified scenarios,
control-failure analysis, and investigation priorities.
## Guardrails
Do not present suspicion, a score, or a network link as proof. Preserve
exculpatory evidence and apply tests consistently. Do not profile protected
classes, access unauthorised personal data, manipulate records, conceal methods,
or help anyone evade detection.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!