{"id":"AIBD-0713","title":"Sweden's Social Insurance Inspectorate finds that claims picked by Försäkringskassan's risk model had errors in 55% of checks, against 34% at random","summary":"Sweden's Social Insurance Inspectorate found that 55% of parental-benefit claims picked by a risk model in 2017 had errors, against 34% of claims checked at random.","date_observed":"2017-01-01","date_basis":"event","date_precision":"year","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Försäkringskassan, the Swedish Social Insurance Agency, checks claims for temporary parental benefit, which parents receive when they stay home with a sick child. It picks claims for checks at random and with a risk model. The model computes each claim's probability of error from register data, reported data and the errors uncovered in earlier random checks. The Social Insurance Inspectorate (ISF) obtained the agency's data for 2017, with 1,047 random and 5,086 risk-selected claims. In a report of March 2018, ISF found that 34% of randomly checked claims had errors. The share was 55% among risk-selected claims. ISF concluded that choosing whom to check more systematically raises the hit rate markedly. It also found that the selection, as designed, failed its test for equal treatment. In 2025 Försäkringskassan told the Swedish Authority for Privacy Protection (IMY) that it had used a machine-learning risk profile for these checks. It said that it had taken the profile out of use about a month before 5 September 2025.","category":"aggregate result","unexpected":false,"purpose":"Försäkringskassan built the risk-based selection profile to pick the temporary parental benefit claims most likely to contain errors for checks.","system":"Försäkringskassan's risk-based selection profile (urvalsprofil) for checks of temporary parental benefit, run through the RaKuR infrastructure","model_version":null,"developer":"Försäkringskassan (Swedish Social Insurance Agency), Urvalsanalys unit","deployer":"Försäkringskassan (Swedish Social Insurance Agency)","location":"Sweden","domain":"Social insurance control","segment":null,"benefit_type":"Improved Task Accuracy","work_activities":["4.A.2.a.3","4.A.2.b.6"],"taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"organizational","depth":2,"reason":"ISF measured errors in 55% of the claims that the model picked, against 34% of claims checked at random."}],"ai_role":{"status":"assessed","statement":"ISF compared claims that the model picked with claims picked at random in the same year, which isolates the model's effect on the hit rate. The model was built from register data and the errors found in random checks, and ISF writes that the agency builds such profiles with machine-learning methods."},"evidence":[{"kind":"audit report","citation":"Inspektionen för socialförsäkringen (ISF). Profilering som urvalsmetod för riktade kontroller: En granskning av träffsäkerheten, effektiviteten och rättssäkerheten i Försäkringskassans modeller för riskbaserade kontroller. ISF Rapport 2018:5, March 2018. Chapter 6, printed pp. 109-110, Tabell 7.","title":"Profilering som urvalsmetod för riktade kontroller (ISF Rapport 2018:5)","url":"https://isf.se/download/18.6e75aae16a591304896b99/1565330421562/Profilering%20som%20urvalsmetod%20fo%CC%88r%20riktade%20kontroller-ISF-Rapport%202018-05.pdf","date":"2018-03-01","written_by":"independent","quote":"Genom att välja ut vilka som ska kontrolleras mer systematiskt ökar alltså träffsäkerheten väsentligt."},{"kind":"regulatory document","citation":"Integritetsskyddsmyndigheten (IMY). Beslut efter tillsyn enligt dataskyddsförordningen – Försäkringskassan. IMY-2025-11828, 18 November 2025.","title":"Beslut efter tillsyn enligt dataskyddsförordningen – Försäkringskassan","url":"https://www.imy.se/globalassets/dokument/beslut/2025/beslut-tillsyn-forsakringskassan.pdf","date":"2025-11-18","written_by":"independent","quote":"I Försäkringskassans svar till IMY, som inkom den 5 september 2025, uppgavs att Försäkringskassan har använt en maskininlärningsbaserad riskprofil inom ramen för det aktuella arbetet men att denna togs ur bruk en dryg månad före tidpunkten för Försäkringskassans svar.","supports_benefit":false}],"replication":{"produced_by":["The model computed each claim's probability of error from register data, reported data and the errors uncovered in random checks.","The agency picked claims for checks by risk value, amount and expected future use of the benefit.","Random checks continued beside the model and supplied errors to train it."],"must_hold":["Random checks must continue to show the true distribution of errors.","Selection must pass a test of equal treatment across groups."],"echoes":[]},"paired_incident":null,"headline":"Swedish risk model finds more errors in parental-benefit claims","metric":"55% of parental-benefit claims picked by a risk model","comparator":{"against":"Claims picked at random for checks in 2017","before":"34 procent","after":"55 procent","basis":"measured","quote":"Träffsäkerheten i det slumpmässiga urvalet är 34 procent medan det är 55 procent i den riskbaserade urvalet.","url":"https://isf.se/download/18.6e75aae16a591304896b99/1565330421562/Profilering%20som%20urvalsmetod%20fo%CC%88r%20riktade%20kontroller-ISF-Rapport%202018-05.pdf"},"later":{"status":"withdrawn","date":null,"note":"Försäkringskassan told the Authority for Privacy Protection that it had taken its machine-learning risk profile for these checks out of use. 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