evidoria

← Back to browse

Good practice Imported

UWV's Risicoscan Verwijtbare Werkloosheid — the Netherlands' Audited Unemployment Fraud-Risk Score

Netherlands · Amsterdam · See the Netherlands profile · See the Amsterdam profile

Evidence: Quasi-experimental Top 66% 53/100 · Ask Evidence Copilot about this practice

UWV has screened unemployment-benefit claims with a risk-scoring algorithm since 2022. The Netherlands Court of Audit's 2025 review found flagged cases yield three times more confirmed fraud than random checks, but flagged unresolved IT-management risks.

~70 %
Cases selected by the algorithm
~30 %
Cases selected at random as control sample
3x
Confirmed culpable-unemployment rate, flagged cases vs. random sample (2024 review, published May 2025)
UWV's Risicoscan Verwijtbare Werkloosheid — the Netherlands' Audited Unemployment Fraud-Risk Score

Details

Maturity
Established
Promoter
UWV (Uitvoeringsinstituut Werknemersverzekeringen)
Period
2022–present (audited 2024, published May 2025)
Keywords
social security, unemployment insurance, fraud detection, benefits administration

Context

UWV, the Dutch employee-insurance agency administering unemployment (WW) benefits, has since 2022 applied an algorithmic 'Risicoscan Verwijtbare Werkloosheid' (culpable-unemployment risk score) to flag applications where a claimant may bear responsibility for their own job loss for closer manual review by a caseworker.

Activities

Around 70% of reviewed cases are selected by the algorithm; the remaining 30% are chosen at random specifically to test the algorithm's own accuracy and guard against bias — a genuine randomised control sample built into the operating design.

Results

The Netherlands Court of Audit (Algemene Rekenkamer) evaluated the algorithm as part of its 2024 annual review of government algorithms, publishing results in May 2025. It found that cases flagged by the risk score yield three times as many confirmed instances of culpable unemployment as the random control sample. The same review found UWV had mitigated most privacy and bias risks but flagged outstanding general IT-control weaknesses requiring remediation in 2025.

Conclusions

Context: the government's SyRI welfare-fraud detection system was struck down by Dutch courts in 2020 over rights violations, and the Tax Administration's own algorithms were separately flagged for GDPR non-compliance in the same audit cycle — making UWV's comparatively better-managed, but still imperfect, oversight a useful reference point.

Implementation

Indicative cost
Medium (€50k–€500k)
Time to results
Long (> 3 years)
Staffing & skills
UWV caseworkers conducting manual review of flagged and randomly-sampled cases, UWV algorithm/IT operations team, Netherlands Court of Audit (Algemene Rekenkamer) as independent auditor

Conditions for success

  • Built-in randomised control sample (30% of cases) enabling ongoing measurement of algorithm accuracy and bias
  • Independent government audit cycle providing external oversight
  • Mitigation of privacy and bias risks per the 2024/25 audit

Common failure modes

  • Audit flagged outstanding general IT-control weaknesses requiring remediation in 2025
  • Sector context includes prior failures (SyRI system struck down by Dutch courts in 2020; Tax Administration algorithms flagged for GDPR non-compliance in the same audit cycle), underscoring the risk profile of algorithmic welfare-fraud detection generally

Commonly funded by

National / regional programmes

Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.

Do you run this practice? Claim it — verified implementers get a public contact pathway and can propose corrections.

Data sources

Where this practice's information was retrieved from, and when.

Attachments

Similar practices you may find useful