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Their mutual evaluation report of December 2025 describes BNM's supervisory technology in a box. There the report states that the tool flagged two MSBs in 2022 for facilitating abnormal customer transactions. On-site examinations then uncovered weaknesses in their ongoing due diligence. It also states there that BNM's technology solutions flagged 292 MSB licensees from 2020 to 2024. In the main text, the assessors judged that BNM's use of supervisory technology to automate risk ratings had been particularly effective for the MSB sector. The report does not say how the assessors checked the results in the box or what followed the examinations.","category":"protective response","unexpected":false,"purpose":"Bank Negara Malaysia built the MSB Customer Anomaly Detection tool to find MSB customers with outlier transaction behavior. 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The report does not say how the assessors checked the results in the box or what followed the examinations."}},"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":"MSB Customer Anomaly Detection"}},"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 APG assessors relay that Bank Negara Malaysia's MSB Customer Anomaly Detection tool flagged two firms whose due diligence proved weak","headline":"*MSB Customer Anomaly Detection* points supervisors to two weak firms"}},"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":["142acc63d5000439e2a7de2eb78bc31e6e768a6407173bea7c52611cf806ec70"],"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":"At the end of 2024, Bank Negara Malaysia (BNM) supervised 258 money services businesses (MSBs), most of them money changers. MSBs send BNM their transaction data every month. Each quarter, BNM's MSB Customer Anomaly Detection tool looks for customers with outlier transaction behavior. It combines machine learning models with rule-based parameters. FATF and Asia/Pacific Group on Money Laundering (APG) assessors visited Malaysia from 3 to 21 February 2025. Their mutual evaluation report of December 2025 describes BNM's supervisory technology in a box. There the report states that the tool flagged two MSBs in 2022 for facilitating abnormal customer transactions. On-site examinations then uncovered weaknesses in their ongoing due diligence. It also states there that BNM's technology solutions flagged 292 MSB licensees from 2020 to 2024. In the main text, the assessors judged that BNM's use of supervisory technology to automate risk ratings had been particularly effective for the MSB sector. The report does not say how the assessors checked the results in the box or what followed the examinations.","replication":{"produced_by":["MSBs submitted their transaction data to BNM every month.","Each quarter, machine learning models and rule-based parameters flagged MSB customers with outlier transaction behavior.","Supervisors followed the flags with on-site examinations of the MSBs concerned."],"must_hold":["BNM must keep access to detailed transaction data from MSBs, which the assessors named as the reason the approach suited this sector.","BNM must have supervisors to examine the flagged MSBs, and the report counts 52 supervisors who examine its higher-risk firms on a three-to-five-year cycle."],"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":"675a6ee720bd81152eae266f6e6e7d17b4b76bdfa2689575a5aace00a2f8186c"}