[{"data":1,"prerenderedAt":581},["ShallowReactive",2],{"uc-life-underwriting-medical-record-summarization":3,"uc-regulations":374},{"useCase":4,"evidence":203,"blitsAiDeployments":268,"benchmarks":269,"indicative":270,"related":273,"indexability":372,"includeUnpublished":209},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":19,"channels":23,"audience":25,"autonomy":26,"adoptionStage":27,"segment":18,"problem":28,"problemStats":29,"howItWorks":30,"valueDrivers":31,"kpis":36,"indicativeValue":42,"macroEstimates":77,"feasibility":78,"implementation":91,"risk":134,"blitsAi":181,"faq":183,"related":193,"datePublished":198,"dateModified":198,"lastVerified":198,"changelog":199,"slug":202},"AI summarization of medical evidence for life and health underwriting","Life underwriting medical summaries","AI medical record summaries for life underwriting","AI turns medical records into cited summaries for life underwriters. Manulife uses generative AI to digitize and summarize underwriting documents in Singapore.","published","AI that reads the medical evidence behind a life or health insurance application (attending physician statements, electronic health records, lab results and disclosures), turns it into a structured, cited summary of conditions, treatments and dates, and maps it to the insurer's underwriting manual so an underwriter can decide faster and more consistently.",[12,13,14],"attending physician statement summarization","APS summarization","medical evidence review for underwriting",[16],"insurance",[18],"underwriting",[20,21,22],"document-processing","summarization","rag-knowledge-assistant",[24],"internal-tools","employee-facing","copilot","early-adopters","For fully underwritten life and health cover, much of the work is reading medical evidence. An\nattending physician statement or a set of health records can be long, mixing handwritten notes,\nscans and lab printouts in no particular order. Underwriters or nurse reviewers read it to find\nthe handful of facts that matter (diagnoses, dates, medications, test values, smoking status) and\nthen look them up in the insurer's underwriting manual.\n\nThat reading is slow, expensive and can differ between reviewers, and applicants wait while it\nhappens. Electronic health records can make evidence available sooner, but someone still has to\nread it.",[],"1. **Collect and digitize.** Medical records arrive from providers, labs and record retrieval\n   vendors; the system converts scans and handwriting to text and splits the file into encounters.\n2. **Extract clinical facts.** A model extracts diagnoses, procedures, medications, vitals and lab\n   values with dates, and codes them to a standard vocabulary, each linked to its source page.\n3. **Build the timeline.** Facts are ordered into a timeline and checked against the applicant's\n   own disclosures to highlight differences.\n4. **Map to the manual.** Retrieval over the underwriting manual suggests the relevant impairment\n   guidance and any further evidence needed, without setting the final rating.\n5. **Underwriter decides.** The underwriter reviews the summary, opens the source pages where it\n   matters and makes the decision; simple, clean cases can go to straight through rules that the\n   insurer already governs.",[32,33,34,35],"speed","employee-productivity","customer-experience","compliance",[37,38,39,40,41],"processing-time-reduction","time-saved-per-task","automation-rate","cycle-time-days","accuracy",{"referenceOrg":43,"inputs":44,"formula":72,"currency":73,"period":74,"resultLabel":75,"caveat":76},"A life insurer that fully underwrites 20,000 applications a year with medical records",[45,51,58,65],{"key":46,"label":47,"low":48,"high":48,"unit":49,"note":50},"applications","Applications with medical records reviewed per year",20000,"applications per year","The reference insurer.",{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"reviewHours","Underwriter or nurse reading time per file",1,2,"hours per file","Editorial assumption. Replace with a time study of your own medical evidence review.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"timeSavedShare","Share of reading time the summary removes",0.3,0.5,"fraction of reading time","Editorial assumption. No insurer on this page publishes a reading time figure, so replace it with the result of your own parallel run.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"costPerHour","Fully loaded cost of an underwriting hour",50,80,"USD per hour","Editorial assumption. Replace with your own fully loaded cost.","applications * reviewHours * timeSavedShare * costPerHour","USD","per year","Underwriting review time released","Time released only. It leaves out the usually larger effect of faster decisions on placement rates (fewer applicants dropping out), record retrieval costs, platform costs and the effort of validating the model for a high risk use.",[],{"complexity":79,"complexityNote":80,"dataPrerequisites":81,"integrations":86},"high","Clinical extraction from poor scans is hard, errors carry real consequences for applicants, and in the EU the use is high risk under the AI Act. Special category health data raises the bar on data protection, residency and access control. Expect formal model validation and a long parallel run.",[82,83,84,85],"Historical medical files with the underwriting decisions made on them, for testing","The underwriting manual (the insurer's own or a reinsurer's) in a form the insurer may use for retrieval","A clinical vocabulary and mapping for impairments used in the manual","Consent and authorization records for every medical record used",[87,88,89,90],"New business and underwriting workbench","Medical record retrieval vendors and electronic health record sources","Document management with page level retention","Rules engine for straight through decisions",{"steps":92,"guardrails":108,"humanInTheLoop":114,"kpisToInstrument":115,"failureModes":121},[93,96,99,102,105],{"title":94,"detail":95},"Start with summarization, not decisions","The first release produces a cited summary and timeline for the underwriter. Straight through decisions stay with existing, validated rules until the summary is proven.",{"title":97,"detail":98},"Build a gold standard set","Have senior underwriters and medical officers annotate a few hundred real files so recall of critical facts (for example a cancer history or abnormal lab value) can be measured, not guessed.",{"title":100,"detail":101},"Measure what is missed, not just what is right","A missed impairment is far worse than an extra one. Track recall on critical conditions separately and set release thresholds with the chief underwriter and medical officer.",{"title":103,"detail":104},"Run in parallel","For a period, underwriters review files the usual way and compare with the summary. Only reduce reading once differences are understood and documented.",{"title":106,"detail":107},"Govern it as a high risk system where applicable","In the EU, meet the AI Act requirements for high risk systems (risk management, data governance, logging and human oversight) and, as the deploying insurer, carry out the fundamental rights impact assessment that Article 27 requires. Elsewhere follow the insurer's AI governance framework and local rules, such as Colorado's Regulation 10-1-1 where external consumer data or predictive models are involved.",[109,110,111,112,113],"Every extracted fact links to the page and passage it came from","The summary never sets the rating or declines an applicant on its own","Health data processed only in approved regions, with access limited to underwriting roles","Explicit consent or legal basis recorded for every record processed","No inference of protected characteristics or genetic information beyond what law permits","Underwriters make every decision and review source pages for any material fact. Medical officers own the clinical vocabulary and the gold standard set, and the chief underwriter signs off release thresholds and reviews a monthly sample of summaries against full reads.",[116,117,118,119,120],"Median days from application to decision, before and after","Reading time per file from workbench logs","Recall of critical impairments on the audited sample","Share of summaries corrected by underwriters, by error type","Placement rate (offers accepted) for summarized versus non summarized cases",[122,125,128,131],{"title":123,"detail":124},"Missed conditions in poor scans","Handwritten notes and faxed pages drop out of extraction and the summary looks complete. Flag low quality pages and require a human to read them.",{"title":126,"detail":127},"Automation bias in the underwriter","A clean summary is trusted and the source is never opened. Sample decisions and show the pages behind each material fact.",{"title":129,"detail":130},"Unfair outcomes through proxies","Summaries emphasise facts that correlate with protected characteristics. Test outcomes by group and keep the underwriting manual, not the model, in charge of rating.",{"title":132,"detail":133},"Consent and residency gaps","Records are sent to a model outside the approved region or without a valid basis. Route health data through controlled infrastructure only.",{"euAiAct":135,"regulations":137,"guidance":145,"controls":172,"incidents":180},{"tier":79,"basis":136},"Annex III point 5(c): AI intended for risk assessment and pricing in relation to natural persons in life and health insurance. Article 6(3) exempts some purely preparatory tasks, but never a system that profiles natural persons. Extracting an applicant's health conditions and mapping them to the underwriting manual evaluates their health, which is profiling, so treat the system as high risk. Under the timeline as amended, the obligations for Annex III high risk systems apply from 2 December 2027, and Article 27 requires deployers of point 5(c) systems to assess the impact on fundamental rights before first use.",[138,139,140,141,142,143,144],"eu-ai-act","gdpr","hipaa","nist-ai-rmf","iso-42001","dora","solvency-ii",[146,152,156,161,166],{"title":147,"issuer":148,"region":149,"url":150,"note":151},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(c) lists life and health insurance risk assessment and pricing of natural persons as high risk.",{"title":153,"issuer":148,"region":149,"url":154,"note":155},"Article 27, fundamental rights impact assessment for high risk AI systems","https://artificialintelligenceact.eu/article/27/","Deployers of high risk systems referred to in Annex III points 5(b) and (c), which includes life and health insurance risk assessment and pricing, must assess the impact on fundamental rights before deploying the system.",{"title":157,"issuer":158,"region":149,"url":159,"note":160},"Implementation timeline of the EU AI Act","Future of Life Institute (artificialintelligenceact.eu)","https://artificialintelligenceact.eu/implementation-timeline/","Lists 2 December 2027 as the date from which Chapter III, Sections 1 to 3, apply to high risk systems classified under Article 6(2) and Annex III.",{"title":162,"issuer":163,"region":149,"url":164,"note":165},"Opinion on Artificial Intelligence governance and risk management","European Insurance and Occupational Pensions Authority","https://www.eiopa.europa.eu/publications/opinion-artificial-intelligence-governance-and-risk-management_en","Supervisory expectations for AI in insurance, including data governance, fairness, record keeping and human oversight.",{"title":167,"issuer":168,"region":169,"url":170,"note":171},"SB21-169, Protecting Consumers from Unfair Discrimination in Insurance Practices","Colorado Division of Insurance","north-america","https://doi.colorado.gov/for-consumers/sb21-169-protecting-consumers-from-unfair-discrimination-in-insurance-practices","Amended Regulation 10-1-1, effective 15 October 2025, sets governance and risk management requirements for life insurers' and health benefit plan insurers' use of external consumer data and information sources, algorithms and predictive models. The Division has also held stakeholder meetings on life insurance underwriting.",[173,174,175,176,177,178,179],"Registration as a high risk system in the EU and a conformity assessment before use","Fundamental rights impact assessment under Article 27 of the AI Act before the insurer first uses the system, including when it is bought from a vendor","Documented data governance for training and test medical data, including consent","Logging of every summary, its sources and the underwriter's decision","Periodic fairness testing of decisions made with the summary","Data protection impact assessment for special category health data","In the US, a valid HIPAA authorization from the applicant for every record requested from a provider: life insurers are generally not HIPAA covered entities themselves, while health insurers acting as health plans are and must meet the Privacy and Security Rules directly",[],{"howToBuild":182},"On Blits.ai this is an **agentic workflow** started from the new business system through the API.\nThe medical file is loaded through **document ingestion** (PDF, Office documents and images), an **agent with structured output**\nextracts clinical facts with page references into a fixed schema, and a **knowledge base** holding\nthe underwriting manual is searched with hybrid retrieval to suggest the relevant guidance. The\nresult is returned to the workbench for the underwriter; nothing is decided by the workflow.\n\nBecause this is health data and, in the EU, a high risk use, it runs in the **EU or UAE data\nresidency regions** with **PII masking** configured for identifiers that the model does not need,\n**tenant isolation** and **role based access**. Every run keeps a full **audit trail**,\nand **test suites** replay an annotated gold standard set on every prompt or model change so recall\non critical conditions is measured before release. The platform is model agnostic, so the insurer\ncan choose a model it has validated and switch without rebuilding.",[184,187,190],{"question":185,"answer":186},"Can AI decide life insurance applications from medical records?","For some applications, AI does decide. Manulife says its partnership with Munich Re Life US on alitheia, an AI driven risk assessment platform, raised instant underwriting decision eligibility from US$3 million to US$5 million. Summarization tools, such as Manulife's generative AI in Singapore, support an underwriter instead, and Prudential launched MedScreen+ to provide a faster, simpler and more transparent process for its underwriters. In the EU, AI used for risk assessment and pricing of individual life or health cover is high risk under the AI Act, so keep an underwriter accountable for decisions the summary informs.",{"question":188,"answer":189},"How much faster does underwriting get?","No insurer on this page publishes a figure for medical record summarization. Manulife says its generative AI in Singapore reduces processing time for policy applications but gives no number, and Prudential discloses no outcome for MedScreen+. Measure it yourself in a parallel run, and separate reading time from time spent waiting for records.",{"question":191,"answer":192},"What is the biggest risk?","Missing a material condition. Measure recall on critical impairments against a gold standard, keep source pages one click away and sample decisions, because a fluent summary is easy to trust too much.",[194,195,196,197],"underwriting-risk-assessment-copilot","health-prior-authorization-and-claims-adjudication","intelligent-document-processing","insurance-pricing-and-actuarial-copilot","2026-09-27",[200],{"date":198,"note":201},"First published","life-underwriting-medical-record-summarization",[204,238],{"title":205,"useCases":206,"organization":207,"vendors":212,"summary":215,"stage":216,"year":217,"channels":218,"languages":219,"metrics":221,"outcomeDisclosed":209,"sources":222,"verification":232,"grade":235,"id":236,"organizationSlug":237},"Prudential plc: MedScreen+ AI underwriting tool for health cover in Hong Kong",[202],{"name":208,"anonymized":209,"country":210,"region":211,"industry":16},"Prudential plc",false,"HK","asia-pacific",[213],{"name":208,"role":214},"in-house","In its 2025 full year results, Prudential said it continues to enhance its health underwriting with AI powered solutions designed to increase underwriting automation and efficiency, and that it launched MedScreen+ in Hong Kong, an AI underwriting tool intended to provide underwriters with a faster, simpler and more transparent process and to support its financial consultants with instant, indicative underwriting results for customers. In its 2024 results it reported that around 74 per cent of new business policies were processed through auto underwriting capabilities. No outcome figures are given for MedScreen+ itself.","production",2025,[24],[220],"en",[],[223,228],{"url":224,"title":225,"publisher":226,"date":227},"https://www.sec.gov/Archives/edgar/data/1116578/000162828026019027/fullyearprelimreport.htm","Prudential plc 2025 full year results (Form 6-K)","Prudential plc via SEC EDGAR","2026-03-18",{"url":229,"title":230,"publisher":226,"date":231},"https://www.sec.gov/Archives/edgar/data/1116578/000110465925025883/tm2429688d4_6k.htm","Prudential plc 2024 full year results (Form 6-K)","2025-03-20",{"level":233,"checkedAt":234},"source-verified","2026-09-26","B","prudential-plc-medscreen-ai-underwriting",null,{"title":239,"useCases":240,"organization":241,"vendors":245,"summary":250,"stage":216,"year":251,"channels":252,"languages":253,"metrics":254,"outcomeDisclosed":209,"sources":255,"verification":265,"grade":235,"id":266,"organizationSlug":267},"Manulife: generative AI document summarization and preliminary assessments in life underwriting",[202],{"name":242,"anonymized":209,"country":243,"region":244,"industry":16},"Manulife","CA","global",[246,247],{"name":242,"role":214},{"name":248,"role":249},"Munich Re","platform","Manulife's 2024 annual report says generative AI in Singapore automates document digitization and summarization in underwriting, improving the accuracy of underwriting decisions and reducing processing time for policy applications, and that in the US it expanded the use of electronic health records and used generative AI to automate preliminary underwriting assessments. In 2025 it partnered with Munich Re Life US on alitheia, an AI driven risk assessment platform, raising the instant underwriting decision eligibility limit from US$3 million to US$5 million. No time or accuracy figures are disclosed.",2024,[24],[220],[],[256,261],{"url":257,"title":258,"publisher":259,"date":260},"https://www.sec.gov/Archives/edgar/data/1086888/000108688825000054/a2024annualmdareport.htm","Manulife 2024 Annual Management's Discussion and Analysis (Form 40-F, Exhibit 99.2)","Manulife via SEC EDGAR","2025-02-19",{"url":262,"title":263,"publisher":259,"date":264},"https://www.sec.gov/Archives/edgar/data/1086888/000108688826000003/a2025annualmdareport.htm","Manulife 2025 Annual Management's Discussion and Analysis (Form 40-F, Exhibit 99.2)","2026-02-11",{"level":233,"checkedAt":234},"manulife-generative-ai-life-underwriting","manulife",0,[],{"low":271,"high":272},300000,1600000,[274,301,323,350],{"slug":194,"title":275,"shortTitle":276,"definition":277,"status":9,"industries":278,"functions":279,"patterns":281,"audience":25,"autonomy":26,"adoptionStage":27,"segment":18,"evidenceCount":284,"publicEvidenceCount":284,"organizations":285,"bestGrade":235,"headline":294,"lastVerified":234,"indexable":300},"AI copilot for underwriting risk assessment","Underwriting risk assessment copilot","A copilot that assembles everything relevant to a risk (the submission, loss history, internal guidelines, third party data and public information), highlights exposures and gaps against the insurer's underwriting guidelines and drafts the underwriting narrative or referral note, while the underwriter makes and signs every decision.",[16],[18,280],"risk-management",[22,21,282,283],"content-generation","agentic-workflow",8,[286,287,288,289,290,291,292,293],"Accelerant Holdings","American International Group","Arch Capital Group","Bowhead Specialty","Generali Global Corporate & Commercial","Hiscox","Skyward Specialty Insurance Group","Zurich North America",{"kpi":37,"label":295,"unit":296,"n":54,"nUpTo":268,"kind":297,"value":68,"qualifier":298,"claimant":299,"organization":290,"vendorReported":300},"Cycle time reduction","percent","reported","exact","vendor",true,{"slug":195,"title":302,"shortTitle":303,"definition":304,"status":9,"industries":305,"functions":307,"patterns":311,"audience":25,"autonomy":26,"adoptionStage":27,"segment":308,"evidenceCount":313,"publicEvidenceCount":313,"organizations":314,"bestGrade":235,"headline":319,"lastVerified":198,"indexable":300},"AI for health insurance prior authorization and claims adjudication support","Health prior authorization and adjudication","AI that reads prior authorization requests, medical claims and appeals with their clinical and billing documents, extracts diagnoses, treatments and costs, checks them against the policy and published clinical criteria, and prepares a summary and recommendation for a clinician or adjudicator, who makes every adverse decision.",[16,306],"healthcare",[308,309,310],"claims","case-management","operations",[20,21,22,312,282],"classification-and-routing",5,[315,316,317,318,242],"Acentra Health","AdvanceCare","Centers for Medicare and Medicaid Services","ICICI Lombard",{"kpi":320,"label":321,"unit":296,"n":55,"nUpTo":268,"kind":297,"value":68,"qualifier":322,"claimant":299,"organization":315,"vendorReported":300},"handling-time-reduction","Handling time reduction","approximately",{"slug":196,"title":324,"shortTitle":325,"definition":326,"status":9,"industries":327,"functions":332,"patterns":334,"audience":336,"autonomy":337,"adoptionStage":338,"evidenceCount":339,"publicEvidenceCount":313,"organizations":340,"bestGrade":235,"headline":346,"lastVerified":198,"indexable":300},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[328,329,330,331],"cross-industry","government","automotive","manufacturing",[310,309,333],"finance-and-accounting",[20,335,312],"computer-vision","back-office","supervised-agent","mainstream",7,[341,342,343,344,345],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":41,"label":347,"unit":296,"n":54,"nUpTo":268,"kind":297,"value":348,"qualifier":349,"claimant":299,"organization":341,"vendorReported":300},"Accuracy",90,"at-least",{"slug":197,"title":351,"shortTitle":352,"definition":353,"status":9,"industries":354,"functions":355,"patterns":358,"audience":25,"autonomy":26,"adoptionStage":27,"segment":361,"evidenceCount":313,"publicEvidenceCount":313,"organizations":362,"bestGrade":235,"headline":367,"lastVerified":234,"indexable":300},"AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[16],[356,280,357],"product-and-pricing","analytics-and-reporting",[359,360,283,21],"prediction-and-scoring","code-generation","pricing",[286,363,364,365,366],"Europ Assistance","Generali France","Kinsale Capital Group","MAIF",{"kpi":368,"label":369,"unit":370,"n":54,"nUpTo":268,"kind":297,"value":313,"qualifier":298,"claimant":371,"organization":364,"vendorReported":209},"productivity-gain","Productivity gain","multiplier","organization",{"indexable":300,"reasons":373},[],[375,380,385,391,397,402,409,416,423,430,437,443,450,457,463,468,475,481,486,492,498,504,510,515,520,527,533,537,543,550,557,563,570,575],{"id":138,"label":376,"issuer":148,"region":149,"url":377,"description":378,"useCases":379,"indexable":300},"EU AI Act","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.",197,{"id":139,"label":381,"issuer":148,"region":149,"url":382,"description":383,"useCases":384,"indexable":300},"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.",180,{"id":142,"label":386,"issuer":387,"region":244,"url":388,"description":389,"useCases":390,"indexable":300},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":141,"label":392,"issuer":393,"region":169,"url":394,"description":395,"useCases":396,"indexable":300},"NIST AI Risk Management Framework","NIST","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.",83,{"id":143,"label":398,"issuer":148,"region":149,"url":399,"description":400,"useCases":401,"indexable":300},"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.",66,{"id":403,"label":404,"issuer":405,"region":149,"url":406,"description":407,"useCases":408,"indexable":300},"uk-gdpr","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.",64,{"id":410,"label":411,"issuer":412,"region":149,"url":413,"description":414,"useCases":415,"indexable":300},"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.",47,{"id":417,"label":418,"issuer":419,"region":211,"url":420,"description":421,"useCases":422,"indexable":300},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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.",36,{"id":424,"label":425,"issuer":426,"region":211,"url":427,"description":428,"useCases":429,"indexable":300},"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.",25,{"id":431,"label":432,"issuer":433,"region":244,"url":434,"description":435,"useCases":436,"indexable":300},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":438,"label":439,"issuer":440,"region":169,"url":441,"description":442,"useCases":436,"indexable":300},"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":444,"label":445,"issuer":446,"region":149,"url":447,"description":448,"useCases":449,"indexable":300},"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.",16,{"id":451,"label":452,"issuer":453,"region":244,"url":454,"description":455,"useCases":456,"indexable":300},"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":458,"label":459,"issuer":148,"region":149,"url":460,"description":461,"useCases":462,"indexable":300},"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":464,"label":465,"issuer":148,"region":149,"url":466,"description":467,"useCases":462,"indexable":300},"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.",{"id":469,"label":470,"issuer":471,"region":169,"url":472,"description":473,"useCases":474,"indexable":300},"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":476,"label":477,"issuer":148,"region":149,"url":478,"description":479,"useCases":480,"indexable":300},"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.",12,{"id":140,"label":482,"issuer":483,"region":169,"url":484,"description":485,"useCases":480,"indexable":300},"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.",{"id":487,"label":488,"issuer":489,"region":244,"url":490,"description":491,"useCases":480,"indexable":300},"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.",{"id":493,"label":494,"issuer":148,"region":149,"url":495,"description":496,"useCases":497,"indexable":300},"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.",11,{"id":499,"label":500,"issuer":501,"region":169,"url":502,"description":503,"useCases":497,"indexable":300},"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":505,"label":506,"issuer":419,"region":211,"url":507,"description":508,"useCases":509,"indexable":300},"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.",10,{"id":511,"label":512,"issuer":148,"region":149,"url":513,"description":514,"useCases":509,"indexable":300},"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":516,"label":517,"issuer":148,"region":149,"url":518,"description":519,"useCases":509,"indexable":300},"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":521,"label":522,"issuer":523,"region":149,"url":524,"description":525,"useCases":526,"indexable":300},"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.",9,{"id":528,"label":529,"issuer":530,"region":169,"url":531,"description":532,"useCases":284,"indexable":300},"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.",{"id":144,"label":534,"issuer":148,"region":149,"url":535,"description":536,"useCases":284,"indexable":300},"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":538,"label":539,"issuer":148,"region":149,"url":540,"description":541,"useCases":542,"indexable":300},"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.",6,{"id":544,"label":545,"issuer":546,"region":547,"url":548,"description":549,"useCases":313,"indexable":300},"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.",{"id":551,"label":552,"issuer":553,"region":149,"url":554,"description":555,"useCases":556,"indexable":300},"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.",4,{"id":558,"label":559,"issuer":560,"region":149,"url":561,"description":562,"useCases":556,"indexable":300},"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":564,"label":565,"issuer":566,"region":211,"url":567,"description":568,"useCases":569,"indexable":300},"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":571,"label":572,"issuer":148,"region":149,"url":573,"description":574,"useCases":569,"indexable":300},"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":576,"label":577,"issuer":578,"region":169,"url":579,"description":580,"useCases":569,"indexable":300},"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.",1790598306413]