{ "id": "AIBD-0179", "title": "Singapore's ScamShield flags about 5 million SMSes and blocks about 80,000 scam numbers by October 2023", "summary": "MHA told Parliament in November 2023 that ScamShield, built by Open Government Products with the NCPC and the Police, had about 750,000 downloads, about 80,000 numbers blacklisted and about 5 milli...", "date_observed": "2023-11-22", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "ScamShield is a Singapore government app by Open Government Products, a unit of GovTech. It was developed with the National Crime Prevention Council (NCPC) and the Singapore Police Force. GovInsider (June 2021) described an on-device machine-learning model that files suspected scam SMS into a junk folder. It also blocks calls from numbers on a police blocklist. It reported 722,865 SMS reported and 5,537 numbers blocked since launch, with a 95% detection accuracy claim. It quoted product manager Lennard Lim on the model's need to adapt as scammers change their attacks. Minister of State Sun Xueling gave an oral reply to parliamentary questions on 22 November 2023. She said ScamShield had about 750,000 downloads as of October 2023. About 80,000 unique scam-tainted phone numbers had been blacklisted and blocked. About 5 million SMSes had been flagged to users as potential scams. She said it has effectively protected users. She also cited about 180 million suspected scam calls blocked at network level in the first half of 2023. That is a telco measure separate from the app. No victim-level evaluation appears in the sources.", "category": "general benefit", "unexpected": false, "purpose": "Filter scam SMS on-device and block calls from police-blacklisted numbers to protect users from scams.", "system": "ScamShield app", "model_version": null, "developer": "Open Government Products (GovTech) with the National Crime Prevention Council and the Singapore Police Force", "deployer": "Singapore Police Force and National Crime Prevention Council", "location": "Singapore, Singapore", "domain": "Scam prevention", "segment": "Consumer scam calls and SMS", "benefit_type": "Reduced Risk", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Flagged-SMS and blocked-number counts are aggregates. No loss avoided by an identifiable user is documented." }, { "group": "societal", "depth": null, "reason": "No measured change in scam losses is attributed to the app in the sources." } ], "ai_role": { "status": "assessed", "statement": "GovInsider attributes SMS filtering to an on-device machine-learning model and call blocking to a police-maintained blocklist. So only the SMS figures involve the model. The parliamentary reply gives counts without attributing them to AI. Developer (Open Government Products, a public body) and deployer (the Police and NCPC) are both government bodies. The reply was given by their ministry." }, "evidence": [ { "kind": "report", "citation": "Ministry of Home Affairs, Singapore, oral reply by Ms Sun Xueling, Minister of State, to parliamentary questions, 22 November 2023", "title": "Oral Reply to Parliamentary Questions on Effectiveness of ScamShield in Preventing Scams", "url": "https://www.mha.gov.sg/media-room/newsroom/oral-reply-to-pq-on-effectiveness-of-scamshield-in-preventing-scams/", "date": "2023-11-22", "written_by": "deployer", "quote": "ScamShield has been effective in protecting users from scam calls and SMSes" }, { "kind": "news report", "citation": "Shirley Tay, GovInsider, June 2021 (date from Google News RSS)", "title": "Meet Singapore's latest weapon against scams", "url": "https://govinsider.asia/intl-en/article/meet-singapores-latest-weapon-against-scams-open-government-products-scamshield", "date": "2021-06-06", "written_by": "independent", "quote": "Meet Singapore's latest weapon against scams" }, { "kind": "other", "citation": "Ministry of Digital Development and Information (2026). Anti-scam efforts by MDDI. MDDI web page, last updated 18 August 2026.", "title": "Anti-scam efforts by MDDI", "url": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "date": "2026-08-18", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "On-device SMS classification combined with a police-maintained blocklist of scam numbers", "User reports feeding the blocklist (722,865 SMS reported by mid-2021)" ], "must_hold": [ "Adoption at scale (about 750,000 downloads by October 2023)", "Police intelligence to keep the blocklist current", "Model updates as scam wording changes" ], "echoes": [ { "date": "2021-06-06", "setting": "Singapore, first months after launch", "model_version": null, "outcome": "recurred", "source": "GovInsider: 5,537 numbers blocked, 722,865 SMS reported" }, { "date": "2026-02-02", "setting": "Straits Times forum letter on ScamShield's multi-layered approach", "model_version": null, "outcome": "unknown", "source": "Google News RSS headline only" } ] }, "paired_incident": null, "headline": "ScamShield flags about 5 million SMSes by October 2023", "metric": "about 5 million SMSes had been flagged to users as potential scams", "comparator": null, "later": { "status": "scaled", "date": "2026-08-18", "note": "MDDI's anti-scam page, updated 18 August 2026, says the enhanced ScamShield app launched in August 2024 grew its user base from 944,000 to 1.53 million.", "quote": "Since the launch of the enhanced ScamShield app in August 2024, the app’s user base has grown by 61.8%, from 944,000 to 1.53 million users.", "url": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/" }, "value": null, "developer_country": "Singapore", "deployer_country": "Singapore", "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": "Headline 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 Organizational Efficiency 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": "Added what happened since from the cited sources.", "source": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Open_Government_Products; https://en.wikipedia.org/wiki/Singapore_Police_Force", "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": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: one independent news report, the deployer's report and the deployer's other. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0179", "catalog_version": "1.0.0", "source_fingerprint": "e18a9e6a97e4e84c7230cc2cedcf3c3d96e6cedbadbeead0db7ce282981d4728", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0179-source-0001", "AIBD-0179-source-0002", "AIBD-0179-source-0003" ], "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": "Open Government Products (GovTech) with the National Crime Prevention Council and the Singapore Police Force", "deployer": "Singapore Police Force and National Crime Prevention Council" } }, "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": "GovInsider attributes SMS filtering to an on-device machine-learning model and call blocking to a police-maintained blocklist. So only the SMS figures involve the model. The parliamentary reply gives counts without attributing them to AI. Developer (Open Government Products, a public body) and deployer (the Police and NCPC) are both government bodies. The reply was given by their ministry." } } }, "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": "general benefit", "ai_role": { "status": "assessed", "statement": "GovInsider attributes SMS filtering to an on-device machine-learning model and call blocking to a police-maintained blocklist. So only the SMS figures involve the model. The parliamentary reply gives counts without attributing them to AI. Developer (Open Government Products, a public body) and deployer (the Police and NCPC) are both government bodies. 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million SMSes had been flagged to users as potential scams", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2026-08-18", "note": "MDDI's anti-scam page, updated 18 August 2026, says the enhanced ScamShield app launched in August 2024 grew its user base from 944,000 to 1.53 million.", "quote": "Since the launch of the enhanced ScamShield app in August 2024, the app’s user base has grown by 61.8%, from 944,000 to 1.53 million users.", "url": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0179" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "ScamShield is a Singapore government app by Open Government Products, a unit of GovTech. It was developed with the National Crime Prevention Council (NCPC) and the Singapore Police Force. GovInsider (June 2021) described an on-device machine-learning model that files suspected scam SMS into a junk folder. It also blocks calls from numbers on a police blocklist. It reported 722,865 SMS reported and 5,537 numbers blocked since launch, with a 95% detection accuracy claim. It quoted product manager Lennard Lim on the model's need to adapt as scammers change their attacks. Minister of State Sun Xueling gave an oral reply to parliamentary questions on 22 November 2023. She said ScamShield had about 750,000 downloads as of October 2023. About 80,000 unique scam-tainted phone numbers had been blacklisted and blocked. About 5 million SMSes had been flagged to users as potential scams. She said it has effectively protected users. She also cited about 180 million suspected scam calls blocked at network level in the first half of 2023. That is a telco measure separate from the app. No victim-level evaluation appears in the sources.", "benefit_type": "Reduced Risk" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "Flagged-SMS and blocked-number counts are aggregates. No loss avoided by an identifiable user is documented." }, { "group": "societal", "reason": "No measured change in scam losses is attributed to the app in the sources." } ] } }, "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": "Filter scam SMS on-device and block calls from police-blacklisted numbers to protect users from scams." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Singapore, Singapore", "developer_country": "Singapore", "deployer_country": "Singapore" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "Filter scam SMS on-device and block calls from police-blacklisted numbers to protect users from scams." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2023-11-22", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency 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": "Added what happened since from the cited sources.", "source": "https://www.mddi.gov.sg/other-pages/anti-scam-efforts/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Open_Government_Products; https://en.wikipedia.org/wiki/Singapore_Police_Force", "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.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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That is a telco measure separate from the app. No victim-level evaluation appears in the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "ScamShield app" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Open Government Products (GovTech) with the National Crime Prevention Council and the Singapore Police Force" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Singapore's ScamShield flags about 5 million SMSes and blocks about 80,000 scam numbers by October 2023", "headline": "ScamShield flags about 5 million SMSes by October 2023" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "e18a9e6a97e4e84c7230cc2cedcf3c3d96e6cedbadbeead0db7ce282981d4728" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "ScamShield is a Singapore government app by Open Government Products, a unit of GovTech. It was developed with the National Crime Prevention Council (NCPC) and the Singapore Police Force. GovInsider (June 2021) described an on-device machine-learning model that files suspected scam SMS into a junk folder. It also blocks calls from numbers on a police blocklist. It reported 722,865 SMS reported and 5,537 numbers blocked since launch, with a 95% detection accuracy claim. It quoted product manager Lennard Lim on the model's need to adapt as scammers change their attacks. Minister of State Sun Xueling gave an oral reply to parliamentary questions on 22 November 2023. She said ScamShield had about 750,000 downloads as of October 2023. About 80,000 unique scam-tainted phone numbers had been blacklisted and blocked. About 5 million SMSes had been flagged to users as potential scams. She said it has effectively protected users. She also cited about 180 million suspected scam calls blocked at network level in the first half of 2023. That is a telco measure separate from the app. No victim-level evaluation appears in the sources.", "replication": { "produced_by": [ "On-device SMS classification combined with a police-maintained blocklist of scam numbers", "User reports feeding the blocklist (722,865 SMS reported by mid-2021)" ], "must_hold": [ "Adoption at scale (about 750,000 downloads by October 2023)", "Police intelligence to keep the blocklist current", "Model updates as scam wording changes" ], "echoes": [ { "date": "2021-06-06", "setting": "Singapore, first months after launch", "outcome": "recurred", "source": "GovInsider: 5,537 numbers blocked, 722,865 SMS reported" }, { "date": "2026-02-02", "setting": "Straits Times forum letter on ScamShield's multi-layered approach", "outcome": "unknown", "source": "Google News RSS headline only" } ] } } }, "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": 28, "reviewed": 0, "record_mapped": 28, "not_assessed": 63, "complete": false, "by_state": { "value_present": 28, "not_reported": 0, "unresolved": 0, "not_assessed": 63, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }