[{"data":1,"prerenderedAt":557},["ShallowReactive",2],{"uc-social-worker-case-note-drafting":3,"uc-regulations":332},{"useCase":4,"evidence":178,"blitsAiDeployments":238,"benchmarks":239,"indicative":246,"related":249,"indexability":330,"includeUnpublished":184},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":25,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":42,"macroEstimates":76,"feasibility":77,"implementation":88,"risk":132,"blitsAi":147,"faq":149,"related":162,"datePublished":166,"dateModified":167,"lastVerified":166,"changelog":168,"slug":177},"AI drafting of social work case notes and assessments","Social worker case note drafting","AI case note drafting for social workers","AI drafts the case note from a social work visit for review. Swindon Borough Council cut write up time by 63% in a trial.","published","A generative AI tool, often built on speech to text, that turns a social worker's account of a visit or assessment, whether a recorded conversation or their own dictated or typed prompt, into a first draft of the case note or statutory assessment in the format the case record needs, for the social worker to check, correct and sign before it becomes part of the record.",[12,13,14,15,16],"AI case notes for social work","social care ambient scribe","AI assessment write up tool","voice to case note AI","AI note taking for social workers",[18],"government",[20],"case-management",[22,23,24],"speech-analytics","summarization","content-generation",[26],"internal-tools","employee-facing","copilot","early-adopters","adult and children's social care","Statutory social work runs on paperwork. A Care Act assessment, a social circumstance report, a\nvisit record or a child protection case note each has a required structure, and most are required\nby the same statutory duty that requires the visit itself. Writing it up well takes real time: a\nfull assessment can take hours after the conversation itself, on top of the visit, and it competes\nwith the next family or person waiting for a visit.\n\nDictation software that turns speech into text has long helped with typing the words, but it still\nleaves the social worker to structure and write the note from a blank page. The newer generation\ngoes further: a captured or described account of the conversation feeds a language model that\ndrafts the note directly into the format the service uses, so the social worker's job becomes\nchecking and correcting rather than writing from nothing. That is also where the risk sits. A\nrecord that a court, a panel or another professional relies on later must say what actually\nhappened, not what a model guessed was likely to have been said, and a rushed sign off turns a\nhelpful draft into an unreliable record.\n\nThis is distinct from summarizing a meeting into general notes and actions: a statutory case note\nor assessment has to follow the case record's own required fields and format, not a generic\nsummary structure, and it becomes part of a legal record rather than an internal recap.",[],"1. **Get consent.** The social worker tells the person that the conversation will be recorded and\n   used to help write up the visit, and records that consent; the person can decline.\n2. **Record and transcribe, or prompt.** A phone, tablet or meeting app captures the conversation\n   and turns it into text, ideally with speakers separated; some deployments instead have the\n   social worker enter a prompt describing what they heard and saw.\n3. **Draft into the required format.** A language model turns the transcript or prompt into a\n   first draft of the specific document the service needs (a Care Act assessment, a social\n   circumstance report, a visit note), using the council's own template and headings.\n4. **Check, correct and own it.** The social worker reads the draft against what they heard and\n   saw, cross references anything from other professionals or relatives, corrects anything wrong\n   or missing, and is the person who signs and submits it.\n5. **File and audit.** The finished note goes into the case management system with a record of\n   who wrote and approved it; the audio and raw transcript follow the council's retention policy.",[35,36,37],"employee-productivity","cost-to-serve","customer-experience",[39,40,41],"handling-time-reduction","hours-saved","productivity-gain",{"referenceOrg":43,"inputs":44,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A council with 1,000 social workers across children's and adult social care",[45,51,58,65],{"key":46,"label":47,"low":48,"high":48,"unit":49,"note":50},"socialWorkers","Social workers",1000,"social workers","The reference council.",{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"documentsPerWorkerPerYear","Case notes and assessments per social worker per year",40,80,"documents per social worker per year","Editorial assumption, replace with your own caseload and documentation data.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"hoursSavedPerDocument","Hours saved per document",0.5,2,"hours saved per document","Swindon Borough Council's is the only precise per document saving that can be computed from a source on this page: its reported write up time for a Care Act assessment fell from four hours to one hour thirty minutes, a saving of two and a half hours (Swindon Borough Council, \"Council completes AI trial to enhance Adult Social Care services\"). Lancashire County Council also reported a write up time falling, for a social circumstance report, from up to two days to around three to four hours, but that range is too wide to turn into a single figure. The high end of this range is set at 2 hours, below Swindon's 2.5 hour saving, and the low end at 0.5 hours, so the range also covers shorter, routine case notes and visit records, which take less time to write up than a full assessment and should save less.",{"key":66,"label":67,"low":68,"high":54,"unit":69,"note":70},"costPerHour","Cost of a social worker hour, fully loaded",25,"USD per hour","Editorial assumption for a fully loaded social worker cost. Replace with your own.","socialWorkers * documentsPerWorkerPerYear * hoursSavedPerDocument * costPerHour","USD","per year","Social worker time cost avoided on case note and assessment drafting","Gross avoided admin time only. It leaves out the tool's licence cost, the time spent reviewing and correcting drafts (which is real and necessary, not zero), training time, and any change in the accuracy or consistency of the records produced.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"low","The speech to text and drafting problem itself is well understood. The real work is getting the output into the exact statutory format each document type requires, and building the habit, not just the rule, that a social worker always checks a draft before it is signed.",[81,82,83],"A template for each statutory document type with its required structure and headings","A written consent process and script for telling the person their conversation will be used","A clear rule on which conversations should never be recorded this way, such as one where an immediate safeguarding disclosure needs an instant response rather than a drafted note",[85,86,87],"Case management or client record system, to file the finished, signed document","The council's existing productivity or meeting platform, if the tool is built on top of one","Secure storage for audio and transcripts that meets the council's data residency policy",{"steps":89,"guardrails":108,"humanInTheLoop":113,"kpisToInstrument":114,"failureModes":119},[90,93,96,99,102,105],{"title":91,"detail":92},"Start with one document type and one team","Pick a single high volume, lower risk document (for example a routine visit note or a Care Act assessment write up) in one team, rather than every statutory form across the service.",{"title":94,"detail":95},"Encode the exact template","Build the prompt or template around the headings and structure the document already has to have, so the draft needs editing, not restructuring.",{"title":97,"detail":98},"Build in consent and an off switch","Give staff a simple, consistent way to ask for consent, to pause or stop recording, and to not record at all when that is the right call.",{"title":100,"detail":101},"Keep the social worker as the author","The draft populates an ordinary editable document; the social worker reviews it line by line, adds their own analysis and professional judgment, and is accountable for what is filed. Never auto file a draft.",{"title":103,"detail":104},"Measure quality alongside speed","Sample completed documents against the original recording or notes for anything invented, wrong or missing, not only for how much time was saved.",{"title":106,"detail":107},"Widen by team and document type","Once time and quality both hold up, add more teams and document types, and keep the same review discipline as volume grows.",[109,110,111,112],"No document is filed until the social worker has reviewed, corrected and signed it","Explicit, recorded consent before any conversation is captured, with the option to decline","An escalation path for urgent safeguarding concerns that does not wait for a drafted note","Audio and transcripts stored securely, kept only as long as policy requires, and access logged","The social worker stays the author and the accountable professional. They check the draft against what they actually heard and saw, add their own analysis, and are the person who signs and submits the final record; the tool's job is to produce a first draft in the right format, not a final document.",[115,116,117,118],"Time to complete each document type, before and after, by team","Sample audit rate and findings on drafts checked against the original recording","Social worker adoption, usage and satisfaction","Time spent in direct contact with people, versus time spent on admin",[120,123,126,129],{"title":121,"detail":122},"A fluent draft states something that was not said","A model can produce a confident sentence that is not supported by the recording, and a rushed reviewer can miss it. Prevent with mandatory review before filing and periodic audits of drafts against the source recording.",{"title":124,"detail":125},"Consent becomes a formality","Under time pressure, staff record without properly explaining what is happening. Build consent into the workflow as a required step, not an assumption, and check for it in audits.",{"title":127,"detail":128},"A safeguarding disclosure waits for a drafted note","A visit reveals an immediate risk, and staff treat the AI note as the next step instead of an immediate safeguarding referral. Train and test that the drafting tool is never the safeguarding process.",{"title":130,"detail":131},"Time saved becomes headcount cut, not more contact time","If released time is only used to reduce posts, the case for the tool weakens with the workforce that has to adopt it. Track and report time released against direct contact time and caseload, not only against cost.",{"euAiAct":133,"regulations":136,"guidance":140,"controls":141,"incidents":146},{"tier":134,"basis":135},"context-dependent","The tool drafts documentation for a social worker to check and sign rather than deciding on services or eligibility itself, but a Care Act assessment or similar eligibility document is what an adult social care eligibility decision rests on, which sits close to Annex III 5(a): AI systems used by a public authority to evaluate eligibility for essential public assistance benefits and services. A deployment designed as a preparatory drafting step, feeding into but not replacing that eligibility assessment, can rely on the Article 6(3)(d) derogation for AI performing a preparatory task to an assessment relevant for the purpose of the Annex III use case, rather than the assessment itself. Relying on that derogation carries its own duties, and they fall on the provider: a council that builds its own tool this way is the provider under the Regulation, and it must document why the system is judged non high risk (Article 6(4)) and register itself and the system in the EU database (Article 49(2)). That is different from Article 49(3), which requires a public authority to register its use of a system that actually is high risk, and does not apply once the derogation holds. Separately, the provider of the generative model may owe the Article 50(2) duty to mark the drafted text as AI generated. If a council reused the same transcripts or drafts to feed a scoring or triage model, that downstream use would need its own risk assessment (see the referral risk triage use case).",[137,138,139],"eu-ai-act","gdpr","uk-gdpr",[],[142,143,144,145],"Human review and sign off required before any AI drafted document is filed","The person being visited is told a recording will be used to help write up the visit, and can decline","Access controls and an audit trail on who viewed, edited or approved each AI assisted record","Regular sampling of AI drafted documents against the original recording for accuracy",[],{"howToBuild":148},"On Blits.ai this starts with **self hosted transcription and speaker diarization**, turning a\nrecorded visit into a timestamped, speaker separated transcript on Blits.ai's own infrastructure\nrather than sending raw audio to a third party. An **AI agent**, prompted with the exact\nstructure of each statutory document type and grounded in a **knowledge base** of the council's\nown documentation standards, drafts the note or assessment from that transcript, reachable\nthrough the **REST or WebSocket API channel** from the council's case management system or a\nmobile app used on visits.\n\nThe draft is never filed on its own: an **agentic workflow with human in the loop approval**\nholds the draft until the social worker approves it, **guardrails** and **PII masking** keep\nsensitive personal data out of logs and any model calls that do not need it, and **test suites**\ncan run sample transcripts through the agent before a prompt change goes live, to check for\ninvented or missing detail. **EU and UAE data residency** keeps recordings and drafts in region\nwhere that is a requirement, and because the platform is **model agnostic**, a council can\nchoose a model that meets its own data handling policy.",[150,153,156,159],{"question":151,"answer":152},"Does the AI decide what goes in the case note?","No. It produces a first draft in the required format. Lancashire County Council's own account says Copilot \"puts the information into the right format and is a great place to start,\" and that \"the social worker is still responsible for the report that's submitted\": they cross reference it and add details about the person and any professionals they have spoken to before it goes in. Swindon Borough Council's own account of its trial does not say whether that review step is mandatory before a document is submitted.",{"question":154,"answer":155},"How much time can this realistically save?","It depends heavily on the document type. Swindon Borough Council's own reported trial result was a 63% reduction in the time social workers spent compiling assessments and case notes between April and July 2024, with the write up time for a Care Act assessment falling from four hours to one hour thirty minutes. Lancashire County Council's social circumstance reports fell from up to two days to around three to four hours. Lancashire also gave an early estimate of more than 200,000 staff hours a year freed up from targeted use of AI on routine tasks, not case note drafting specifically and not a measured result, so plan conservatively for a first team against the like for like write up figures above rather than that estimate.",{"question":157,"answer":158},"What happens if a visit reveals something urgent, like a safeguarding risk?","That has to go through the normal safeguarding process immediately, not through the case note drafting tool. The drafting workflow should never be the route by which an urgent concern gets raised or actioned.",{"question":160,"answer":161},"Is this tool itself high risk under the EU AI Act?","Treat it as context dependent, not automatically minimal. Drafting a Care Act or other eligibility assessment sits close to Annex III 5(a), which covers AI used by a public authority to evaluate eligibility for essential public assistance benefits and services, so a deployment designed as a preparatory drafting step can rely on the Article 6(3) derogation for AI that performs a preparatory task to that assessment. That derogation brings its own documentation and registration duties, and they fall on the provider, which for a council that builds its own tool is the council itself. The provider may also owe the Article 50(2) duty to mark the generated text as AI produced. Reusing the same transcripts or drafts to feed a scoring or triage model is a separate, additional risk that needs its own assessment.",[163,164,165],"ambient-clinical-documentation","child-welfare-referral-risk-triage","meeting-summarization-and-action-items","2026-09-29","2026-09-30",[169,171,173,175],{"date":167,"note":170},"Published after review by an automated review workflow (independent skeptic review).",{"date":167,"note":172},"Editorial pass on the skeptic review blockers: corrected the EU AI Act basis and matching FAQ answer (Article 6(3)(d) covers a preparatory task, not a narrow one; the Article 6(4) documentation duty and Article 49(2) registration duty fall on the provider, which for a council that builds its own tool is the council, not \"the provider or deployer\"; Article 49(3) deployer registration only applies to an actual high risk Annex III system, not one covered by the derogation; dropped the unsourced \"most deployments\" claim for conditional wording), removed the mobile-app channel (neither evidence record shows an app of the organization's own), aligned \"social circumstances report\" to \"social circumstance report\" in howItWorks, and reworded the indicativeValue note and the FAQ time saving answer to stay closer to source wording. Set the Lancashire evidence record's outcomeDisclosed to true (the source describes a report time falling from up to two days to three to four hours; kept without a metric because the range is too wide for one figure), removed the unsourced \"statutory\" claim from its summary, split its training statement from its Copilot prompt statement (the source does not say the training covered the prompts), and renamed the evidence file from `lancashire-county-council-copilot-case-notes` to `lancashire-county-council-copilot-reports` (the source never mentions case notes).",{"date":167,"note":174},"Unpublished by an automated review workflow (independent skeptic review).",{"date":166,"note":176},"First published","social-worker-case-note-drafting",[179,210],{"title":180,"useCases":181,"organization":182,"vendors":187,"summary":191,"stage":192,"year":193,"channels":194,"languages":195,"metrics":197,"outcomeDisclosed":198,"sources":199,"verification":205,"grade":207,"id":208,"organizationSlug":209},"Lancashire County Council: Microsoft 365 Copilot prompts for social circumstance reports",[177],{"name":183,"anonymized":184,"country":185,"region":186,"industry":18},"Lancashire County Council",false,"GB","europe",[188],{"name":189,"role":190},"Microsoft","platform","Lancashire County Council trained more than 1,400 social workers, educational psychologists and support officers in Adult Services and Education and Children's Services to use responsible AI. Staff were also supported to use Microsoft 365 Copilot prompts, a set of simple instructions that draft a social circumstance report (a complex assessment describing a person's living situation and support system) in the council's format; the source does not say the responsible AI training itself covered these prompts. The social worker checks and amends the draft, and still meets the person and speaks to relevant professionals and relatives for the report; a social care lead and a councillor both state the social worker remains responsible for what is submitted and that the tool does not replace professional judgment. The source does not say whether the visit itself is recorded or dictated, only that a Copilot prompt generates the draft.","production",2026,[26],[196],"en",[],true,[200],{"url":201,"title":202,"publisher":203,"date":204},"https://news.lancashire.gov.uk/news/ai-tool-saves-time-and-puts-staff-back-on-the-frontline","AI tool saves time and puts staff back on the frontline","Lancashire County Council News","2026-03-23",{"level":206,"checkedAt":166},"source-verified","B","lancashire-county-council-copilot-reports",null,{"title":211,"useCases":212,"organization":213,"vendors":215,"summary":218,"stage":219,"year":220,"channels":221,"languages":222,"metrics":223,"outcomeDisclosed":198,"sources":232,"verification":236,"grade":207,"id":237,"organizationSlug":209},"Swindon Borough Council: Magic Notes trial in adult social care",[177],{"name":214,"anonymized":184,"country":185,"region":186,"industry":18},"Swindon Borough Council",[216],{"name":217,"role":190},"Beam","Swindon Borough Council trialled Magic Notes, a tool built by the social enterprise Beam, across 184 frontline meetings with 19 social workers and one leadership support officer in its Adult Social Care department between April and July 2024. The tool records conversations between social workers and their clients during Care Act assessments, mental capacity assessments and other supporting conversations, and automatically generates detailed, high quality assessments. Following the trial the council signed a new six month contract with Beam for the tool.","pilot",2024,[26],[196],[224],{"kpi":39,"value":225,"unit":226,"qualifier":227,"period":228,"claimant":229,"quote":230,"sourceUrl":231},63,"percent","exact","April to July 2024 trial","organization","The trial has been carried out in the Council's Adult Social Care department and has led to a 63 per cent reduction in the time social workers spend compiling their assessments and logging their case notes.","https://www.swindon.gov.uk/news/article/1071/council_completes_ai_trial_to_enhance_adult_social_care_services",[233],{"url":231,"title":234,"publisher":214,"date":235},"Council completes AI trial to enhance Adult Social Care services","2024-10-01",{"level":206,"checkedAt":166},"swindon-borough-council-magic-notes",0,[240],{"kpi":39,"label":241,"unit":226,"aggregate":198,"higherIsBetter":198,"n":242,"nUpTo":238,"median":225,"min":225,"max":225,"byClaimant":243,"vendorOnly":184,"points":244},"Handling time reduction",1,{"organization":242,"vendor":238,"regulator":238,"independent":238},[245],{"evidenceId":237,"organization":214,"value":225,"qualifier":227,"claimant":229,"grade":207,"pooled":198},{"low":247,"high":248},500000,6400000,[250,277,291,311],{"slug":163,"title":251,"shortTitle":252,"definition":253,"status":9,"industries":254,"functions":256,"patterns":259,"audience":27,"autonomy":28,"adoptionStage":260,"evidenceCount":261,"publicEvidenceCount":261,"organizations":262,"bestGrade":207,"headline":272,"lastVerified":276,"indexable":198},"AI ambient scribe for clinical documentation","Ambient clinical documentation","An AI scribe that listens, with the patient's consent, to the conversation between a clinician and a patient and drafts the clinical note, and often the letter or after visit summary, for the clinician to review, edit and sign in the health record. It documents; it does not diagnose or decide on treatment.",[255],"healthcare",[257,258],"operations","knowledge-management",[22,23,24],"mainstream",9,[263,264,265,266,267,268,269,270,271],"Beth Israel Lahey Health","BJC Health","Great Ormond Street Hospital for Children NHS Foundation Trust","Kaiser Permanente","Mount Sinai Medical Center","PureHealth","Reid Health","Sutter Health","US Department of Veterans Affairs, Veterans Health Administration",{"kpi":39,"label":241,"unit":226,"n":273,"nUpTo":238,"kind":274,"value":275,"qualifier":227,"claimant":209,"organization":209,"vendorReported":184},4,"median",22.5,"2026-09-27",{"slug":164,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":282,"patterns":283,"audience":27,"autonomy":285,"adoptionStage":286,"segment":287,"evidenceCount":62,"publicEvidenceCount":62,"organizations":288,"bestGrade":207,"headline":209,"lastVerified":166,"indexable":198},"AI risk scoring in child welfare intake and investigations","Child welfare risk scoring","A predictive model that scores a family's risk in the child welfare system, using case history and administrative data, so that staff see a consistent, data informed signal alongside their own judgment, whether that is a call screener and supervisor deciding at referral whether to open an investigation, or a supervisor and social worker deciding how to respond early in one that is already open. The model informs the decision; it does not make it.",[18],[20],[284],"prediction-and-scoring","assist","emerging","children's social care",[289,290],"Allegheny County Department of Human Services","Los Angeles County Department of Children and Family Services",{"slug":165,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":299,"patterns":300,"audience":27,"autonomy":28,"adoptionStage":260,"evidenceCount":301,"publicEvidenceCount":301,"organizations":302,"bestGrade":207,"headline":209,"lastVerified":276,"indexable":198},"AI meeting summarization and action items","Meeting summaries and action items","AI that summarizes internal and operational meetings, such as team, project, board and case meetings: it transcribes an online or in person meeting with the participants' knowledge and produces a summary, decisions and action items with owners and dates for the organizer to check and share. It is the general purpose tool; client advice meetings and sales calls, which feed a regulated record or a sales pipeline, have their own pages.",[296,18,297,298],"cross-industry","technology","professional-services",[258,257],[23,22],8,[303,304,305,306,307,308,309,310],"Cathay Pacific","U.S. Department of Labor","Localiza","Ministry of Justice","Softcat","Trace3","Government Digital Service","University of Manchester",{"slug":312,"title":313,"shortTitle":314,"definition":315,"status":9,"industries":316,"functions":317,"patterns":318,"audience":27,"autonomy":28,"adoptionStage":29,"evidenceCount":319,"publicEvidenceCount":319,"organizations":320,"bestGrade":207,"headline":327,"lastVerified":166,"indexable":198},"police-incident-report-drafting","AI for police incident report drafting from body worn camera audio","Police report drafting","An AI tool that turns the transcript of body worn camera audio into a first draft of a police incident report narrative, which the officer who was on the call must review, correct and sign before it becomes part of the official record.",[18],[20,257],[22,24,23],6,[321,322,323,324,325,326],"Alamosa Police Department","Fort Collins Police Services","Heber City Police Department","Hillsborough County Sheriff's Office","Royal Canadian Mounted Police","Rochester Police Department (Minnesota)",{"kpi":39,"label":241,"unit":226,"n":242,"nUpTo":238,"kind":328,"value":329,"qualifier":227,"claimant":229,"organization":322,"vendorReported":184},"reported",82,{"indexable":198,"reasons":331},[],[333,339,344,352,360,366,372,379,387,393,400,406,412,418,425,432,438,445,450,456,463,470,475,480,485,492,497,502,509,514,522,528,534,541,546,551],{"id":137,"label":334,"issuer":335,"region":186,"url":336,"description":337,"useCases":338,"indexable":198},"EU AI Act","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",250,{"id":138,"label":340,"issuer":335,"region":186,"url":341,"description":342,"useCases":343,"indexable":198},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",223,{"id":345,"label":346,"issuer":347,"region":348,"url":349,"description":350,"useCases":351,"indexable":198},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":353,"label":354,"issuer":355,"region":356,"url":357,"description":358,"useCases":359,"indexable":198},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",95,{"id":139,"label":361,"issuer":362,"region":186,"url":363,"description":364,"useCases":365,"indexable":198},"UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",73,{"id":367,"label":368,"issuer":335,"region":186,"url":369,"description":370,"useCases":371,"indexable":198},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",67,{"id":373,"label":374,"issuer":375,"region":186,"url":376,"description":377,"useCases":378,"indexable":198},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":380,"label":381,"issuer":382,"region":383,"url":384,"description":385,"useCases":386,"indexable":198},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":388,"label":389,"issuer":390,"region":383,"url":391,"description":392,"useCases":68,"indexable":198},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":394,"label":395,"issuer":396,"region":348,"url":397,"description":398,"useCases":399,"indexable":198},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":401,"label":402,"issuer":403,"region":356,"url":404,"description":405,"useCases":399,"indexable":198},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":407,"label":408,"issuer":335,"region":186,"url":409,"description":410,"useCases":411,"indexable":198},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":413,"label":414,"issuer":415,"region":186,"url":416,"description":417,"useCases":411,"indexable":198},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":419,"label":420,"issuer":421,"region":356,"url":422,"description":423,"useCases":424,"indexable":198},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":426,"label":427,"issuer":428,"region":348,"url":429,"description":430,"useCases":431,"indexable":198},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":433,"label":434,"issuer":335,"region":186,"url":435,"description":436,"useCases":437,"indexable":198},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":439,"label":440,"issuer":441,"region":356,"url":442,"description":443,"useCases":444,"indexable":198},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":446,"label":447,"issuer":335,"region":186,"url":448,"description":449,"useCases":444,"indexable":198},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":451,"label":452,"issuer":453,"region":356,"url":454,"description":455,"useCases":444,"indexable":198},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":457,"label":458,"issuer":459,"region":348,"url":460,"description":461,"useCases":462,"indexable":198},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",12,{"id":464,"label":465,"issuer":466,"region":356,"url":467,"description":468,"useCases":469,"indexable":198},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":471,"label":472,"issuer":335,"region":186,"url":473,"description":474,"useCases":469,"indexable":198},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":476,"label":477,"issuer":335,"region":186,"url":478,"description":479,"useCases":469,"indexable":198},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":481,"label":482,"issuer":335,"region":186,"url":483,"description":484,"useCases":469,"indexable":198},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":486,"label":487,"issuer":488,"region":186,"url":489,"description":490,"useCases":491,"indexable":198},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",10,{"id":493,"label":494,"issuer":382,"region":383,"url":495,"description":496,"useCases":491,"indexable":198},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":498,"label":499,"issuer":335,"region":186,"url":500,"description":501,"useCases":491,"indexable":198},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":503,"label":504,"issuer":505,"region":356,"url":506,"description":507,"useCases":508,"indexable":198},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":510,"label":511,"issuer":335,"region":186,"url":512,"description":513,"useCases":508,"indexable":198},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":515,"label":516,"issuer":517,"region":518,"url":519,"description":520,"useCases":521,"indexable":198},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":523,"label":524,"issuer":525,"region":186,"url":526,"description":527,"useCases":273,"indexable":198},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":529,"label":530,"issuer":531,"region":186,"url":532,"description":533,"useCases":273,"indexable":198},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":535,"label":536,"issuer":537,"region":383,"url":538,"description":539,"useCases":540,"indexable":198},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":542,"label":543,"issuer":335,"region":186,"url":544,"description":545,"useCases":540,"indexable":198},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":547,"label":548,"issuer":335,"region":186,"url":549,"description":550,"useCases":540,"indexable":198},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":552,"label":553,"issuer":554,"region":356,"url":555,"description":556,"useCases":540,"indexable":198},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790783076544]