UK claims AI helped block £480m in fraud as new tool rolls out
UK ramps up AI to block fraud, protecting £480m and clawing back £186m from Covid-era schemes. A new policy scanner flags risks 80% faster, with cross-department rollout next.
UK government deploys AI to cut fraud: what departments need to do next
The government reports record fraud savings of £480 million in the year to April 2025. Of that, £186 million relates to recovering Covid-era losses, including action on Bounce Back Loans and companies attempting to dissolve before repayment.
The programme pairs AI with cross-government data to block ineligible claims and recover funds. The aim is wider productivity gains, contributing to a stated £45 billion in tech-enabled savings across the public sector.
What's new
A central AI tool has been built to scan draft policies and processes for fraud risk before launch. Early tests suggest it can cut fraud risk identification time by 80%, while keeping human oversight in the loop.
The tool is planned for use across departments and may be licensed to Five Eyes partners. Activity to date also includes stopping companies with suspect Covid loans from dissolving, and tackling false claims on council tax discounts and social housing lists.
What this means for your team
- Shift fraud prevention left. Run new schemes, guidance, and operational processes through policy risk scans before approvals.
- Stand up a cross-functional fraud design review (policy, ops, data, legal, security, commercial) for every material change.
- Pair AI risk scoring with caseworker judgment. Define clear triage, escalation, and redress for false positives.
- Instrument everything. Track prevention per £ spent, time-to-detection, false positive rate, complaint volumes, and recovery rate.
Data, privacy, and fairness
- Use data minimisation: only the attributes needed for a defined fraud risk.
- Confirm legal basis, data-sharing agreements, retention, and DPIAs. Publish privacy notices in plain language.
- Test for bias. Monitor differential impacts and meet Public Sector Equality Duty obligations.
- Log model versions, prompts, rule changes, overrides, and outcomes for audit.
Controls to build into policies and services
- Identity: identity proofing and re-checks at key points; dual verification for high-value transactions.
- Eligibility: real-time data matching (e.g., tax, company, or property records) prior to award or discount.
- Payment: bank account verification, velocity checks, and staged disbursement for risky cases.
- Abuse prevention: agent pattern analysis, device intelligence, IP geo rules, and limits on changes per period.
- Recovery: clawback workflows, debt prioritisation, and fast-track legal routes for organised fraud.
Operating model: five-step workflow
- Map fraud vectors for the policy or service (apply attack trees or user story abuse cases).
- Rate impact and likelihood; set control objectives and thresholds.
- Run the AI risk scan on drafts and process maps; capture flagged gaps.
- Hold a design review to agree mitigations and test plans; document decisions.
- Ship with monitoring, alerts, and a 30/60/90-day post-launch review.
Governance and assurance
- Assign accountable owners for models, data pipelines, and business rules.
- Schedule model and rule refreshes; test drift and performance monthly.
- Red-team the highest-risk schemes; rotate reviewers to avoid blind spots.
- Keep an approval trail linking risks, mitigations, sign-offs, and changes.
Key metrics to track
- £ prevented and recovered per £ spent on detection and enforcement.
- Time from signal to intervention; caseworker throughput and backlog.
- False positive/negative rates; appeals upheld rate; customer harm incidents.
- Proportion of policies risk-scanned pre-launch; audit issues closed on time.
Open items where clarity will help delivery
- Case volumes: totals for council tax discount investigations and social housing waitlist removals.
- Enforcement: prosecutions and civil recovery counts, by scheme and quarter.
- Companies House linkage: scale of dissolution blocks and cross-check coverage for Bounce Back Loans.
- Departmental adoption timeline: when the AI policy scanner becomes mandatory at each stage gate.
Where to go deeper
Skills and team readiness
If your unit is scaling AI for policy assurance or fraud analytics, align training for policy, ops, and data staff. A concise catalogue by job role can speed setup: AI courses by job.
The headline is strong: £480m protected, faster risk reviews, and wider adoption planned. The next win comes from standardising the workflow above and publishing regular delivery metrics so every department can compound results without adding friction for legitimate users.
