{"id":"AIBD-1038","title":"FATF and GAFILAT assessors find that COAF's AI predictive model allows rapid analysis of suspicious transaction reports but sends only 5% to analysts","summary":"FATF and GAFILAT assessors found that COAF's AI predictive model allows rapid analysis of the 4.8 million suspicious transaction reports received in 2018–2022, but that only 5% go to analysts.","date_observed":"2023-12-01","date_basis":"source publication","date_precision":"month","benefit_status":"mixed","source_collection":null,"source_entry":null,"account":"Brazil's financial intelligence unit, the Conselho de Controle de Atividades Financeiras (COAF), receives suspicious transaction reports through its SISCOAF system. A predictive model based on artificial intelligence, data mining and automated processes scores each report on arrival. It crosses the report with COAF's databases, including 23.6 million cash transaction reports from 2018 to 2022. Reports with a 30% likelihood of becoming a financial intelligence report go to individual analysis. COAF received 4.8 million suspicious transaction reports from 2018 to 2022, an average of 971 383 a year. FATF and GAFILAT assessors visited Brazil from 13 to 31 March 2023. In their report of December 2023, they judged the model an important tool that allows rapid analysis. They also found that only 5% of reports go to analysts and that only 2% scored medium risk and 1% high risk. They wrote that the low share may reveal an issue with report quality or the calibration of the risk matrix. Only 23 analysts handle individual analysis. The assessors rated Brazil's use of financial intelligence moderately effective. 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A predictive model based on artificial intelligence, data mining and automated processes scores each report on arrival. It crosses the report with COAF's databases, including 23.6 million cash transaction reports from 2018 to 2022. Reports with a 30% likelihood of becoming a financial intelligence report go to individual analysis. COAF received 4.8 million suspicious transaction reports from 2018 to 2022, an average of 971 383 a year. FATF and GAFILAT assessors visited Brazil from 13 to 31 March 2023. In their report of December 2023, they judged the model an important tool that allows rapid analysis. They also found that only 5% of reports go to analysts and that only 2% scored medium risk and 1% high risk. They wrote that the low share may reveal an issue with report quality or the calibration of the risk matrix. Only 23 analysts handle individual analysis. The assessors rated Brazil's use of financial intelligence moderately effective. They did not test the model's accuracy."}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["summary","account"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].autonomy_permissions":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].component_role":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].deployment_configuration":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].inputs_outputs":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_name":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].model_version":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"systems[].product_name":{"state":"value_present","value":{"kind":"record_fields","fields":{"system":"COAF predictive model for scoring suspicious transaction reports"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["system"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"systems[].provider":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"taxonomy_crosswalks[]":{"state":"unresolved","provenance":{"kind":"curatorial","basis":"Editorial assignment.","record_fields":[],"source_refs":[]},"reason":"This is an explicit unresolved assignment: the sources on record do not settle this question.","review_state":"not_assessed","coverage":"partial"},"title":{"state":"value_present","value":{"kind":"record_fields","fields":{"title":"FATF and GAFILAT assessors find that COAF's AI predictive model allows rapid analysis of suspicious transaction reports but sends only 5% to analysts","headline":"*COAF predictive model* speeds triage of Brazil's suspicious reports"}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["title","headline"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"},"version":{"state":"value_present","value":{"kind":"identifiers","identifiers":["e282928c21ac337a79a8307a136101e140bb8fd1c3175064dd241869d0d48d59"],"role":"SHA-256 of the complete public record and its assessments"},"provenance":{"kind":"curatorial","basis":"SHA-256 of the public record, its assessments, and its displayed answers and notes, excluding its own identifier and the derived coverage summary.","record_fields":[],"source_refs":[]},"review_state":"record_mapped","coverage":"complete"},"workflow":{"state":"value_present","value":{"kind":"record_fields","fields":{"account":"Brazil's financial intelligence unit, the Conselho de Controle de Atividades Financeiras (COAF), receives suspicious transaction reports through its SISCOAF system. A predictive model based on artificial intelligence, data mining and automated processes scores each report on arrival. It crosses the report with COAF's databases, including 23.6 million cash transaction reports from 2018 to 2022. Reports with a 30% likelihood of becoming a financial intelligence report go to individual analysis. COAF received 4.8 million suspicious transaction reports from 2018 to 2022, an average of 971 383 a year. FATF and GAFILAT assessors visited Brazil from 13 to 31 March 2023. In their report of December 2023, they judged the model an important tool that allows rapid analysis. They also found that only 5% of reports go to analysts and that only 2% scored medium risk and 1% high risk. They wrote that the low share may reveal an issue with report quality or the calibration of the risk matrix. Only 23 analysts handle individual analysis. The assessors rated Brazil's use of financial intelligence moderately effective. They did not test the model's accuracy.","replication":{"produced_by":["Reporting entities filed suspicious transaction reports through SISCOAF, which loaded them straight into COAF's systems.","The model scored each report after crossing it with COAF's databases, including its cash transaction reports.","Reports with a 30% likelihood of becoming a financial intelligence report went to individual analysis, with an automatic initial report."],"must_hold":["Reporting entities must file reports of good quality, and the assessors found that report quality was an issue.","COAF needs enough analysts to examine the reports that the model selects, and the assessors found that the unit was understaffed."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":28,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":26,"review_not_assessed":63,"not_assessed":0,"complete":false,"by_state":{"value_present":28,"not_reported":0,"unresolved":63,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"ba3952fa353f29ee610e707fd820f2ae2f6de451c8b485c1fc141ce72707a7dc"}