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Finance·2–6 weeks·4–6 weeks

Payment Fraud Detector

Sophisticated fraud doesn't trip rule-based filters. A behavioural model learns normal patterns per vendor / employee / payment type, flags anomalies in real time. Cuts fraud losses by 20–40% and replaces the rule-set nobody updates.

Fraud losses
−25–40%
False positives
−45%
Mean time to detect
Days → minutes
The problem

What this fixes.

  • Rule-based filters miss sophisticated fraud

  • Annual rule reviews never happen

  • Refund and AP fraud go unnoticed for months

How it works

Three jobs, on rails.

Learn

Normal pattern

Per vendor / employee / type — behavioural baseline.

Flag

In real time

Anomalies surfaced before payment commits.

Adapt

Continuous

Model retrains weekly on new transactions.

From signed proposal to live

The path.

01

Feed two years of payment history; the model learns the baseline per vendor / employee / channel.

02

Define the action ladder: flag, hold-and-notify, block.

03

Pilot for a quarter on flag-only mode so finance trusts the calls.

04

Move high-confidence anomalies to auto-hold; retrain monthly on confirmed labels.

A real moment

One scenario, one outcome.

The scenario

A vendor that always invoices €4–9k suddenly bills €31,000 with new bank details on a Friday.

The outcome

Flagged within seconds with reasoning: 'amount > p99 historical + new IBAN + 3-day urgency'. Held for a call with the vendor; turns out to be a phishing redirection.

Engagement

Scoped on a call.

Delivery

4–6 weeks

Engagement model

Pilot → retainer

Scope confirmed in a 30-minute call. Fixed scope, fixed timeline before you sign. We'll send a one-page proposal within 48 hours.

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