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In 2019 the local offices made 233,733 desk checks and 111,774 on-site checks of these files. Desk checks found overpayments in 26% of files and arrears owed to beneficiaries in 22%, against 6% and 5% in 2012. Data mining was the second source of detected overpayments, at €218.5 million, and of arrears, at €87.2 million. It started 29% of the 48,754 frauds that the branch established in 2019. The score chose the checks in both years compared. The Cour drew attention to the model's use of nationality and of fine-grained place of residence.","category":"aggregate result","unexpected":false,"purpose":"The Cnaf built the data-mining score to pick the beneficiary files most likely to contain overpayments or underpayments for checks.","system":"Data-mining risk score of the family branch of French social security (le datamining)","model_version":null,"developer":"Caisse nationale des allocations familiales (Cnaf)","deployer":"Caisses d'allocations familiales (Caf), nationwide","location":"France (national)","domain":"Social benefits (family and housing)","segment":null,"benefit_type":"Improved Task Accuracy","work_activities":["4.A.2.a.4","4.A.2.b.6"],"taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"organizational","depth":2,"reason":"Desk checks picked by the score found overpayments in 26% of files in 2019 against 6% in 2012, and data mining detected €218.5 million of overpayments that year."},{"group":"personal","depth":null,"reason":"The checks found €87.2 million in arrears owed to beneficiaries in 2019, but the Cour gives neither the number of households nor what each received."}],"ai_role":{"status":"unassessed","statement":"The Cour compares hit rates in 2012 and 2019, and the score chose the checks in both years. The Cour compares no checks chosen without the score. It writes that the score depends on each file's similarity to a model built from the yearly survey of payment accuracy."},"evidence":[{"kind":"audit report","citation":"Cour des comptes. La lutte contre les fraudes aux prestations sociales. Communication à la commission des affaires sociales de l’Assemblée nationale, September 2020, pp. 48-49.","title":"La lutte contre les fraudes aux prestations sociales","url":"https://www.ccomptes.fr/system/files/2020-09/20200908-rapport-Lutte-contre-fraudes-prestations-sociales_0.pdf","date":"2020-09-08","written_by":"independent","quote":"Les contrôles effectués par les Caf sur des situations d’allocataires sélectionnées par le datamining (soit 233 733 contrôles sur pièces et 111 774 contrôles sur place en 2019) conduisent à détecter des fréquences de plus en plus élevées d’indus et de rappels"},{"kind":"audit report","citation":"Cour des comptes (France). Sécurité sociale 2025: Rapport sur l'application des lois de financement de la sécurité sociale (RALFSS 2025), chapter on benefits paid in error (indus). Deuxième partie, chapter on indus in the benefit branches, passage on the family branch's controls (around pp. 300-312)","title":"Sécurité sociale 2025: Rapport sur l'application des lois de financement de la sécurité sociale (RALFSS 2025), chapter on benefits paid in error (indus)","url":"https://web.archive.org/web/2025id_/https://www.ccomptes.fr/sites/default/files/2025-05/20250526-RALFSS-2025_0.pdf","date":"2025-05-26","written_by":"independent","quote":"Par ailleurs, les contrôles liés à l'analyse des données de masse ont détecté 67 M€ d'indus (49 M€ en 2019)."}],"replication":{"produced_by":["The score ranked each beneficiary's file by its similarity to a model built from the yearly survey of payment accuracy.","Each Caf checked the files that the score selected, on paper or on site."],"must_hold":["The annual survey must keep supplying data to rebuild the model.","The variables must respect the rules on personal data, a point that the Cour raised for nationality and place of residence."],"echoes":[]},"paired_incident":null,"headline":"*Datamining* score finds more payment errors in French family benefits","metric":"found overpayments in 26% of files in 2019, up from 6% in 2012","comparator":{"against":"Desk checks picked by the score in 2012, its first year of use","before":"6 % et 5 % respectivement en 2012","after":"26 % de dossiers comportant des indus et 22 % des rappels détectés en 2019","basis":"measured","quote":"pour les contrôles sur pièces, 26 % de dossiers comportant des indus et 22 % des rappels détectés en 2019, contre 6 % et 5 % respectivement en 2012","url":"https://www.ccomptes.fr/system/files/2020-09/20200908-rapport-Lutte-contre-fraudes-prestations-sociales_0.pdf"},"later":null,"value":{"amount":"218,5 M€","what":"Overpayments detected in 2019 through checks that data mining selected","who":"The family benefits branch","estimate":false,"quote":"Après les contrôles RAC, le datamining est la deuxième source de détection d’indus (218,5 M€ en 2019), pour certains frauduleux, et de rappels (87,2 M€).","url":"https://www.ccomptes.fr/system/files/2020-09/20200908-rapport-Lutte-contre-fraudes-prestations-sociales_0.pdf"},"developer_country":"France","deployer_country":"France","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-10-31","change":"Added the record from the finding of the Cour des comptes.","source":"https://www.ccomptes.fr/system/files/2020-09/20200908-rapport-Lutte-contre-fraudes-prestations-sociales_0.pdf","editor":"AIBD"},{"date":"2026-10-31","change":"Added an independent audit report that supports the benefit.","source":null,"editor":"AIBD"}],"confidence":"found","shade":"high","claim_status":"Found by an auditor","claim_basis":"Sources: two independent audit reports. 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