{ "id": "AIBD-0174", "title": "A third company linked to suspected money laundering", "summary": "Bank F’s network tool found phone numbers and email addresses shared by three companies. Further account checks indicated possible money laundering.", "date_observed": "2021-01-21", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Hong Kong Monetary Authority's report “AML/CFT Regtech: Case Studies and Insights” (January 2021; press release 21 January 2021) presents anonymized bank case studies. Bank F is a subsidiary and regional head office of a large global bank in Asia-Pacific, per the report. It implemented a network (graph) analytics solution in Hong Kong in 2013. It identified high-risk customers and transactions from wholesale client, correspondent banking, trade, payments and external registry data. In a 2015 example, a relationship manager gathered intelligence about trade-based money laundering (TBML) related to a drug cartel in one account. The manager linked it to a second account via the CRM tool. The tool then identified additional companies linked to the first two parties through linked phone numbers and email addresses. Account reviews for Companies A, B and C revealed potential money laundering concerns. The report states that by 2016 Bank F covered its top 20 commercial banking sites and about 80% of its trade lending book. The tool has led to review of around 100 customer relationships holding around USD 500 million in clean or unsecured exposures, it says. The report’s USD 50 million figure assumes that 10% of those exposures would otherwise be unrecoverable. It does not document USD 50 million in realized savings. The foreword adds that such techniques are already neutralizing mule account networks linked to face mask scams. The 2015 example carries no day.", "category": "protective response", "unexpected": false, "purpose": "Identify high-risk customers, transactions and hidden relationships for AML/CFT and trade-based money laundering investigations.", "system": "Bank F network (graph) analytics solution implemented in Hong Kong in 2013", "model_version": null, "developer": "Bank F (anonymized subsidiary and regional head office of a large global bank); the report names no vendor", "deployer": "Bank F, Hong Kong", "location": "Hong Kong, China", "domain": "Anti-money laundering", "segment": "Trade finance and trade-based money laundering", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The report records reviews of around 100 customer relationships. It also records a third linked company whose review revealed potential money laundering concerns. Losses avoided are estimated, not observed." }, { "group": "societal", "depth": null, "reason": "The foreword's statement about neutralizing mule account networks is general and not tied to a documented outcome." } ], "ai_role": { "status": "assessed", "statement": "The HKMA report attributes the identification of Company C to the network analytics tool's matching of linked phone numbers and email addresses. A human relationship manager supplied the initial lead. Whether the tool uses machine learning is not stated. The bank built and deployed the tool itself. The HKMA, which wrote the report, is the regulator." }, "evidence": [ { "kind": "descriptive report", "citation": "Hong Kong Monetary Authority and Deloitte, AML/CFT Regtech: Case Studies and Insights (January 2021), Bank F, 24–25.", "title": "AML/CFT Regtech: Case Studies and Insights", "url": "https://www.hkma.gov.hk/media/eng/doc/key-information/guidelines-and-circular/2021/20210121e2a1.pdf", "date": "2021-01-21", "written_by": "independent", "quote": "Since deployment, the tool has led to the review of around 100 customer relationships for AML/CFT reasons." }, { "kind": "press release", "citation": "Hong Kong Monetary Authority, press release, 21 January 2021", "title": "AML/CFT Regtech: Case Studies and Insights", "url": "https://www.hkma.gov.hk/eng/news-and-media/press-releases/2021/01/20210121-3/", "date": "2021-01-21", "written_by": "independent", "quote": "AML/CFT Regtech: Case Studies and Insights", "supports_benefit": false } ], "replication": { "produced_by": [ "Integration of wholesale client, correspondent banking, trade, payments and external registry data in one network model", "A human intelligence lead (the relationship manager's TBML suspicion) used as the seed for network expansion" ], "must_hold": [ "Data of sufficient quality across sources, which the report says Bank F stressed", "Governance that lets analytic findings trigger account reviews and STR filings" ], "echoes": [ { "date": null, "setting": "Bank F, top 20 commercial banking sites globally (2016)", "model_version": null, "outcome": "recurred", "source": "HKMA report: coverage of about 80% of the trade lending book; around 100 relationships reviewed since deployment" }, { "date": null, "setting": "Bank A, Hong Kong, pilot using IP addresses and login data for about 50 customers (2020)", "model_version": null, "outcome": "recurred", "source": "HKMA report: undisclosed clusters of relationships identified from shared IP addresses and simultaneous logins" }, { "date": "2021-01-21", "setting": "Fraud and Money Laundering Intelligence Taskforce member banks", "model_version": null, "outcome": "unknown", "source": "HKMA foreword: techniques 'neutralising mule account networks linked to face mask scams', no figures" } ] }, "paired_incident": null, "headline": "A third company linked to suspected money laundering", "metric": "even if 10% were unrecoverable the tool contributed around USD 50 million in savings", "comparator": null, "later": { "status": "unknown", "date": null, "note": "Nothing dated after the January 2021 HKMA report was found for Bank F's tool; the report's own scale-up figures predate it.", "quote": null, "url": null }, "value": null, "developer_country": null, "deployer_country": null, "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Knowledge Discovery among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Reviewed the specified original sources; corrected their authorship, document types, and support for the reported benefit.", "source": "https://www.hkma.gov.hk/media/eng/doc/key-information/guidelines-and-circular/2021/20210121e2a1.pdf", "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent descriptive report and one independent press release. For the benefit itself, the cited sources include no independent study of the benefit and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0174", "catalog_version": "1.0.0", "source_fingerprint": "f71e252dd0e9ecdf3e46dff9d0e825723825b89435967072388b85b57705a63e", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0174-source-0001", "AIBD-0174-source-0002" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Bank F (anonymized subsidiary and regional head office of a large global bank); the report names no vendor", "deployer": "Bank F, Hong Kong" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The HKMA report attributes the identification of Company C to the network analytics tool's matching of linked phone numbers and email addresses. A human relationship manager supplied the initial lead. Whether the tool uses machine learning is not stated. The bank built and deployed the tool itself. The HKMA, which wrote the report, is the regulator." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "protective response", "ai_role": { "status": "assessed", "statement": "The HKMA report attributes the identification of Company C to the network analytics tool's matching of linked phone numbers and email addresses. A human relationship manager supplied the initial lead. Whether the tool uses machine learning is not stated. The bank built and deployed the tool itself. 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Bank F is a subsidiary and regional head office of a large global bank in Asia-Pacific, per the report. It implemented a network (graph) analytics solution in Hong Kong in 2013. It identified high-risk customers and transactions from wholesale client, correspondent banking, trade, payments and external registry data. In a 2015 example, a relationship manager gathered intelligence about trade-based money laundering (TBML) related to a drug cartel in one account. The manager linked it to a second account via the CRM tool. The tool then identified additional companies linked to the first two parties through linked phone numbers and email addresses. Account reviews for Companies A, B and C revealed potential money laundering concerns. The report states that by 2016 Bank F covered its top 20 commercial banking sites and about 80% of its trade lending book. The tool has led to review of around 100 customer relationships holding around USD 500 million in clean or unsecured exposures, it says. Even if 10% were unrecoverable, the tool contributed around USD 50 million in savings, it says. The foreword adds that such techniques are already neutralizing mule account networks linked to face mask scams. 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Losses avoided are estimated, not observed." }, { "group": "societal", "reason": "The foreword's statement about neutralizing mule account networks is general and not tied to a documented outcome." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Identify high-risk customers, transactions and hidden relationships for AML/CFT and trade-based money laundering investigations." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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The report states that by 2016 Bank F covered its top 20 commercial banking sites and about 80% of its trade lending book. The tool has led to review of around 100 customer relationships holding around USD 500 million in clean or unsecured exposures, it says. The report’s USD 50 million figure assumes that 10% of those exposures would otherwise be unrecoverable. It does not document USD 50 million in realized savings. The foreword adds that such techniques are already neutralizing mule account networks linked to face mask scams. 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Bank F is a subsidiary and regional head office of a large global bank in Asia-Pacific, per the report. It implemented a network (graph) analytics solution in Hong Kong in 2013. It identified high-risk customers and transactions from wholesale client, correspondent banking, trade, payments and external registry data. In a 2015 example, a relationship manager gathered intelligence about trade-based money laundering (TBML) related to a drug cartel in one account. The manager linked it to a second account via the CRM tool. The tool then identified additional companies linked to the first two parties through linked phone numbers and email addresses. Account reviews for Companies A, B and C revealed potential money laundering concerns. The report states that by 2016 Bank F covered its top 20 commercial banking sites and about 80% of its trade lending book. The tool has led to review of around 100 customer relationships holding around USD 500 million in clean or unsecured exposures, it says. Even if 10% were unrecoverable, the tool contributed around USD 50 million in savings, it says. The foreword adds that such techniques are already neutralizing mule account networks linked to face mask scams. The 2015 example carries no day.", "replication": { "produced_by": [ "Integration of wholesale client, correspondent banking, trade, payments and external registry data in one network model", "A human intelligence lead (the relationship manager's TBML suspicion) used as the seed for network expansion" ], "must_hold": [ "Data of sufficient quality across sources, which the report says Bank F stressed", "Governance that lets analytic findings trigger account reviews and STR filings" ], "echoes": [ { "setting": "Bank F, top 20 commercial banking sites globally (2016)", "outcome": "recurred", "source": "HKMA report: coverage of about 80% of the trade lending book; around 100 relationships reviewed since deployment" }, { "setting": "Bank A, Hong Kong, pilot using IP addresses and login data for about 50 customers (2020)", "outcome": "recurred", "source": "HKMA report: undisclosed clusters of relationships identified from shared IP addresses and simultaneous logins" }, { "date": "2021-01-21", "setting": "Fraud and Money Laundering Intelligence Taskforce member banks", "outcome": "unknown", "source": "HKMA foreword: techniques 'neutralising mule account networks linked to face mask scams', no figures" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 30, "reviewed": 2, "record_mapped": 28, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }