Finance & Reporting

Duplicate payment and payment fraud detection

Detect duplicate invoices, near-duplicate submissions, and payment fraud indicators — changed bank details, unusual supplier patterns, invoices just below approval thresholds — before payment runs.

Decision supportPattern-matchingCost reductionAccuracy

30–60%

reduction in duplicate and fraudulent payments

Opportunity assessment

Business Impact
3

Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.

Feasibility
4

Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.

Data Readiness
3

Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.

Risk Exposure
3

Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.

Change Complexity
4

Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.

Tooling required

Specialist AI tool

Things to consider

  • Invoice redirection fraud, where a criminal impersonates a supplier and changes bank details, is one of the most common losses reported by UK businesses. Bank detail changes should always trigger an out-of-band verification regardless of the model's confidence.

  • Fuzzy duplicate detection catches what exact matching misses — the same invoice submitted with a different reference, a transposed digit, or via a different entity.

  • Invoices clustered just under an approval threshold are a classic internal control signal and easy to monitor once you look for it.

  • Experiment starter: Run detection across two years of paid invoices. Any duplicates it finds are recoverable cash, and the recovery usually funds the implementation several times over.

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