How I applied AI classification, Benford's Law analysis, and pattern detection to 800+ corporate expense transactions — and delivered a risk report that changed how leadership thinks about expense governance.
The organisation used Pleo cards across 48 employees — a mix of department heads, operations staff, and senior management. Over the course of a quarter, 796 transactions were processed through the system, totalling a significant sum of corporate spend.
No systematic review had been done. Notes were inconsistent or missing. Some transactions had no business justification. And there were patterns that, to a trained eye, looked wrong — but nobody had looked.
Without systematic review, expense fraud, policy breaches, and wasteful spending go undetected. In an AIM-listed company, this is a governance failure with real consequences — both financial and reputational.
I designed a 10-dimension forensic audit framework — each dimension targeting a specific category of financial risk:
GPT-4o classified every transaction by type (hotel, meal, transport, ATM, gift, mystery merchant) and assigned a policy compliance score based on the company's expense policy rules.
Applied Benford's Law to the leading digits of all transaction amounts. Statistically significant deviation from the expected distribution flags potential manipulation — a standard forensic accounting technique.
Identified employees with unusual transaction frequency, clustering of spend near policy limits, or repeated transactions to the same merchant in short windows.
Mapped merchant usage across employees — looking for cases where multiple employees used the same unusual merchant, a known signal of coordinated fraud.
Scored every transaction note from 0–10 on specificity, business justification, and completeness. Identified cardholders with systemic poor documentation — itself a governance risk.
Cash withdrawals are the highest-risk expense category — untraceable and difficult to justify. All ATM transactions were profiled individually with manual flags for CFO review.
The following represents the type of findings the system surfaced (anonymised for this case study):
| Category | Finding | Risk | Action |
|---|---|---|---|
| ATM Withdrawals | 4 withdrawals in 9 days, total £840, no notes | HIGH | CFO review required |
| Velocity | 11 transactions to same restaurant in 3 weeks | HIGH | Manager investigation |
| Benford's Law | Significant deviation in £40–£50 range (near limit) | MED | Policy limit review |
| Note Quality | 3 cardholders averaging <2/10 note score | MED | Training required |
| Mystery Merchant | Unidentifiable merchant, £220, no category match | HIGH | Receipt requested |
| Cross-employee | 2 employees, same unusual vendor, different dates | MED | Monitoring flagged |
The final deliverable was a CFO and audit committee report — structured with an executive summary, risk heat map, individual cardholder profiles, and a recommended action list. Every high-risk item was traceable back to a specific transaction with evidence.
The audit directly informed changes to the company's expense policy — stricter note requirements, lower ATM limits, and a new approval layer for transactions above £150. These changes were implemented within 4 weeks of the report being delivered.