How Funders Use AI to Detect Fraud Risks

AI surfaces unusual patterns and inconsistencies so officers can investigate early and proportionately.

• Cross‑checks data against registers and past applications.
• Flags duplicate bank details, identical narratives or sudden changes.
• Keeps a clear record of what was flagged and why.

Practical signals to monitor

Look for anomalies that justify a closer look, not automatic rejection.

• Mismatched registration details vs application data.
• Unusual spending patterns against milestones.
• Reused copy across unrelated applications.

Key takeaway: signals guide human review; they are not verdicts.

Implementing safely

Balance protection with fairness and proportionality.

• Tune sensitivity to limit false positives.
• Allow applicants to clarify discrepancies.
• Document outcomes and learning to improve rules.

Key takeaway: Plinth embeds transparent, reviewable checks.

Collaboration and information sharing

Where lawful, funders can share risk signals responsibly.

• Aggregate insights without exposing personal data.
• Common red‑flag libraries across programmes.
• Clear governance and data‑sharing agreements.

Key takeaway: collaboration improves protection across the sector.

FAQs

Does this criminalise applicants?

No. It helps spot errors and rare bad‑faith cases while keeping processes fair.

Can small teams use these tools?

Yes. Pre‑built checks make advanced detection accessible.

What about false positives?

Track them and iterate thresholds; keep humans in charge.