[{"data":1,"prerenderedAt":583},["ShallowReactive",2],{"uc-life-straight-through-underwriting-decisioning":3,"uc-regulations":358},{"useCase":4,"evidence":159,"blitsAiDeployments":258,"benchmarks":259,"indicative":271,"related":274,"indexability":356,"includeUnpublished":165},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":20,"channels":23,"audience":25,"autonomy":26,"adoptionStage":27,"problem":28,"problemStats":29,"howItWorks":30,"valueDrivers":31,"kpis":35,"indicativeValue":38,"macroEstimates":66,"feasibility":67,"implementation":79,"risk":112,"blitsAi":140,"faq":142,"related":152,"datePublished":154,"dateModified":154,"lastVerified":154,"changelog":155,"slug":158},"AI for straight through underwriting decisions on life insurance applications","Life straight through underwriting","AI straight through life insurance underwriting","AI that decides eligible life insurance applications in seconds and refers the rest to underwriters. Ping An reports 94% of life policies underwritten in seconds.","published","Straight through life underwriting uses AI models and data sources to approve or route life insurance applications automatically, so that eligible applicants get a decision in seconds or minutes without an underwriter touching the case. Complex or high value cases go to a human underwriter.",[12,13,14,15],"instant life insurance underwriting","accelerated underwriting","automated life underwriting","straight through processing of life applications",[17],"insurance",[19],"underwriting",[21,22],"prediction-and-scoring","classification-and-routing",[24],"api","back-office","supervised-agent","early-adopters","Underwriting a life insurance application has traditionally meant forms, medical questions, exams, lab\ntests and a human underwriter reading the file, which takes days or weeks. Applicants drop out while they wait,\nand underwriters spend time on healthy applicants whose cases would have been approved anyway.\n\nInsurers now use AI models, electronic health records and other data to decide the clean cases in seconds and\nkeep human underwriters for the rest. The limits are set by the insurer and its reinsurer: the amount of cover, the age and the\ncompleteness of the data. Manulife, for example, says in its 2025 annual report that it raised the amount of cover that qualifies for an instant decision in the US from US$3 million to US$5 million,\nworking with Munich Re Life US and its AI driven risk assessment platform alitheia.",[],"1. **Collect the application.** The applicant answers health and lifestyle questions online or through an agent, and consents to data\n   checks.\n2. **Enrich with data.** The system retrieves prescription history, motor vehicle and other records, and electronic health\n   records where the market allows.\n3. **Score and decide.** A risk model rates the application. Cases that fit the insurer's rules for cover amount, age and data\n   quality are approved or priced automatically, and the decision comes back in seconds.\n4. **Route the rest.** Cases outside those limits go to an underwriter with the data and the model's reasons.\n5. **Audit.** The insurer samples automated decisions, compares mortality and claims with the model's expectations and adjusts the limits.",[32,33,34],"speed","cost-to-serve","customer-experience",[36,37],"automation-rate","processing-time-reduction",{"referenceOrg":39,"inputs":40,"formula":61,"currency":62,"period":63,"resultLabel":64,"caveat":65},"A life insurer with 40,000 new individual life applications a year",[41,47,54],{"key":42,"label":43,"low":44,"high":44,"unit":45,"note":46},"applications","New individual life applications",40000,"applications per year","The reference insurer.",{"key":48,"label":49,"low":50,"high":51,"unit":52,"note":53},"automatedShare","Share of applications decided straight through",0.2,0.4,"fraction of applications","Editorial assumption, kept well below the figures on this page: Ping An reports 94% of life policies underwritten within seconds, which measures speed rather than the share with no human review, and Sun Life reports a 50% increase in straight through underwriting without the base rate. Replace with the share your reinsurer approved limits allow.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"costPerCase","Underwriting cost avoided per straight through case",50,150,"USD per application","Editorial assumption for underwriter time and medical evidence that is no longer ordered. Replace with your own fully loaded cost per underwritten case.","applications * automatedShare * costPerCase","USD","per year","Underwriting cost avoided","Gross underwriting cost only. It leaves out the data provider and model fees, the validation work, the revenue from applicants who no longer drop out while they wait, and any extra claims cost from anti selection in the automated group.",[],{"complexity":68,"complexityNote":69,"dataPrerequisites":70,"integrations":74},"high","It needs the policy administration system, reinsurer approved underwriting rules, external health and records data sources with consent, a risk model validated against the insurer's own mortality experience, and an audit process for automated decisions.",[71,72,73],"Historical applications with underwriting decisions and later claims and lapse outcomes","Reinsurer approved rules for the amounts and ages eligible for automatic decisions","Consent and legal basis for each external data source",[75,76,77,78],"Policy administration and new business system","Electronic health record, prescription and other third party data providers","Reinsurer platform or underwriting rules service","Underwriter workbench for referred cases",{"steps":80,"guardrails":93,"humanInTheLoop":99,"kpisToInstrument":100,"failureModes":105},[81,84,87,90],{"title":82,"detail":83},"Define the eligible population","Agree with the reinsurer which ages, cover amounts and products can be decided automatically, starting with the lowest risk segment.",{"title":85,"detail":86},"Validate the model against your own experience","Run it in shadow mode against human underwriting decisions and later claims, and measure how often it would have approved cases an underwriter declined.",{"title":88,"detail":89},"Launch for a narrow segment","Switch on automatic decisions for one segment, with every referral and a sample of automated decisions reviewed by an underwriter.",{"title":91,"detail":92},"Widen the limits on mortality results","Raise cover amounts or ages only when claims and mortality in the automated group match the pricing assumptions.",[94,95,96,97,98],"Automatic approvals only inside limits agreed with the reinsurer","A path for the applicant to request human review of any decline or rating","Sampling of automated decisions by senior underwriters every month","Documented explanations for adverse decisions","Consent recorded for each external data source","Underwriters own every referred case and every decline, and review a sample of automated approvals. The chief underwriter and the reinsurer own the eligibility limits.",[101,102,103,104],"Share of applications decided automatically","Median time from complete application to decision","Mortality and claims in the automated group against pricing assumptions","Referral rate and decline rate by segment",[106,109],{"title":107,"detail":108},"Anti selection","Applicants who know they are ill learn which answers pass. Monitor mortality in the automated group and tighten limits when it exceeds pricing.",{"title":110,"detail":111},"Unfair outcomes","A model trained on past decisions or on data that proxies for protected traits disadvantages some groups. Test results by group and keep adverse decisions explainable.",{"euAiAct":113,"regulations":115,"guidance":120,"controls":133,"incidents":139},{"tier":68,"basis":114},"Annex III point 5(c) lists AI used for risk assessment and pricing in relation to natural persons in life and health insurance as high risk, and Article 27 requires the insurer, as deployer, to carry out a fundamental rights impact assessment before first use. Because eligible applicants receive a decision with no human review, GDPR Article 22 on decisions based solely on automated processing also applies: the insurer needs one of the grounds in Article 22(2) and must offer human intervention and a way to contest the decision. The decision uses health data, so Article 22(4) in practice requires the applicant's explicit consent.",[116,117,118,119],"eu-ai-act","gdpr","solvency-ii","eu-idd",[121,127],{"title":122,"issuer":123,"region":124,"url":125,"note":126},"Regulation (EU) 2024\u002F1689 (AI Act), Article 6 and Annex III point 5(c)","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Annex III point 5(c) names AI systems for risk assessment and pricing in life and health insurance as high risk.",{"title":128,"issuer":129,"region":130,"url":131,"note":132},"SB21-169, Protecting Consumers from Unfair Discrimination in Insurance Practices","Colorado Division of Insurance","north-america","https:\u002F\u002Fdoi.colorado.gov\u002Ffor-consumers\u002Fsb21-169-protecting-consumers-from-unfair-discrimination-in-insurance-practices","Requires insurers to test external consumer data, algorithms and predictive models for unfair discrimination; Regulation 10-1-1 sets governance requirements for life insurers that use them, as accelerated underwriting does.",[134,135,136,137,138],"Model validation against the insurer's own experience before each limit increase","Audit trail of the data, the score and the decision per application","Monthly sampling review of automated decisions","Fairness testing of approval and decline rates by group","Fundamental rights impact assessment under Article 27 of the AI Act before first use",[],{"howToBuild":141},"The underwriting model and the reinsurer's rules engine make the decision, and Blits.ai does not replace them. Blits.ai covers the applicant\nand underwriter conversations around them. An **agent** guides the applicant through the health questions on **web chat, WhatsApp or voice**, with **flows** for the\nfixed questions and **custom functions** that send the answers to the underwriting service as REST calls and return the decision.\n\nA **knowledge base** with the insurer's approved product and underwriting FAQ answers applicant questions, and **human handover**\npasses a referred case to an underwriter with the conversation. **Guardrails** and **PII masking** protect health data in prompts, and **test suites** check the question\nflow after every change. Run Blits.ai in our cloud in the EU, Qatar, Singapore, the UAE, the US or Australia, in your private cloud, or fully on premises.",[143,146,149],{"question":144,"answer":145},"What share of life applications can be decided automatically?","Ping An reports that 94% of its life insurance policies were underwritten within seconds in 2025. Sun Life says AI tools cut median response times for individual insurance applications in a target segment by close to half in 2025, including a 50% increase in straight through underwriting. Both figures are the insurers' own, neither says how many cases had no human review at all, and both depend on the product mix and cover amounts.",{"question":147,"answer":148},"Is automated life underwriting high risk under the EU AI Act?","Yes. Annex III point 5(c) lists AI for risk assessment and pricing in life and health insurance for natural persons as high risk, so the insurer needs risk management, logging, data governance and human oversight.",{"question":150,"answer":151},"Which applications still go to an underwriter?","Large amounts of cover, older applicants, incomplete data and any case the model cannot rate with confidence. Insurers keep underwriters for these referred cases and for review of declines.",[153],"life-underwriting-medical-record-summarization","2026-11-12",[156],{"date":154,"note":157},"First published","life-straight-through-underwriting-decisioning",[160,197,228],{"title":161,"useCases":162,"organization":163,"vendors":169,"summary":172,"stage":173,"year":174,"channels":175,"languages":177,"metrics":179,"outcomeDisclosed":188,"sources":189,"verification":192,"grade":194,"id":195,"organizationSlug":196},"Ping An: AI digital underwriting of life insurance policies",[158],{"name":164,"anonymized":165,"country":166,"region":167,"industry":17,"wikidata":168},"Ping An Insurance",false,"CN","asia-pacific","Q1256188",[170],{"name":164,"role":171},"in-house","Ping An says on its technology page that it applies big data, AI and machine learning to digital underwriting of life insurance. For 2025 it reports that 94% of life insurance policies were underwritten within seconds. The page does not say what share of those policies were decided with no human review, which rules were applied, or how the figure is counted, so the number shows speed of a decision rather than a confirmed rate of fully automated decisions.","scaled",2025,[176],"internal-tools",[178],"zh",[180],{"kpi":36,"value":181,"unit":182,"qualifier":183,"period":184,"claimant":185,"quote":186,"sourceUrl":187},94,"percent","exact","Life insurance policies underwritten within seconds, 2025","organization","In 2025, 94% of life insurance policies were underwritten within seconds.","https:\u002F\u002Fgroup.pingan.com\u002Fabout_us\u002Four_businesses\u002Ftechnology.html",true,[190],{"url":187,"title":191,"publisher":164},"Ping An technology, AI enabled underwriting",{"level":193,"checkedAt":154},"source-verified","B","ping-an-life-insurance-ai-digital-underwriting",null,{"title":198,"useCases":199,"organization":202,"vendors":206,"summary":208,"stage":209,"year":174,"channels":210,"languages":212,"metrics":214,"outcomeDisclosed":188,"sources":220,"verification":225,"grade":194,"id":227,"organizationSlug":196},"Sun Life: AI in individual insurance underwriting, contact centre chat and advisor tools",[200,201,158],"insurance-policy-servicing-agent","insurance-broker-and-agent-assistant",{"name":203,"anonymized":165,"country":204,"region":130,"industry":17,"wikidata":205},"Sun Life","CA","Q835456",[207],{"name":203,"role":171},"Sun Life's 2025 annual report says AI tools cut median response times for individual insurance applications in a target segment by close to half, including a 50% increase in straight through underwriting. In its Client Contact Centre, generative AI raised chatbot containment by 16 percentage points year over year, and advisors use a generative AI Notes Assistant Tool; in Hong Kong it launched Advisory Buddy, a GenAI chatbot inside its Advisor Workbench. In Malaysia almost two thirds of clients received automated underwriting decisions within two hours.","production",[176,211],"web-chat",[213],"en",[215],{"kpi":37,"value":57,"unit":182,"qualifier":216,"period":217,"claimant":185,"quote":218,"sourceUrl":219},"approximately","median response time for individual insurance applications, target segment, 2025","Leveraged AI tools to do more for our Clients including reduced median response times for individual insurance applications for a target segment by close to half (including increasing straight-through underwriting by 50%), increased chatbot containment rate (up 16 percentage points year-over-year) with generative AI tools in the Client Contact Centre, and enhanced advisor productivity using a generative AI-powered Notes Assistant Tool.","https:\u002F\u002Fwww.sec.gov\u002FArchives\u002Fedgar\u002Fdata\u002F1097362\u002F000109736226000010\u002Fa2025q4slfmdalive.htm",[221],{"url":219,"title":222,"publisher":223,"date":224},"Sun Life Financial Inc. Management's Discussion and Analysis for 2025 (Form 40-F, Exhibit 99.1)","Sun Life via SEC EDGAR","2026-02-12",{"level":193,"checkedAt":226},"2026-09-26","sun-life-ai-underwriting-and-client-service",{"title":229,"useCases":230,"organization":231,"vendors":235,"summary":240,"stage":209,"year":241,"channels":242,"languages":243,"metrics":244,"outcomeDisclosed":188,"sources":245,"verification":255,"grade":194,"id":256,"organizationSlug":257},"Manulife: generative AI document summarization and preliminary assessments in life underwriting",[153,158],{"name":232,"anonymized":165,"country":204,"region":233,"industry":17,"wikidata":234},"Manulife","global","Q1652574",[236,237],{"name":232,"role":171},{"name":238,"role":239},"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,[176],[213],[],[246,251],{"url":247,"title":248,"publisher":249,"date":250},"https:\u002F\u002Fwww.sec.gov\u002FArchives\u002Fedgar\u002Fdata\u002F1086888\u002F000108688825000054\u002Fa2024annualmdareport.htm","Manulife 2024 Annual Management's Discussion and Analysis (Form 40-F, Exhibit 99.2)","Manulife via SEC EDGAR","2025-02-19",{"url":252,"title":253,"publisher":249,"date":254},"https:\u002F\u002Fwww.sec.gov\u002FArchives\u002Fedgar\u002Fdata\u002F1086888\u002F000108688826000003\u002Fa2025annualmdareport.htm","Manulife 2025 Annual Management's Discussion and Analysis (Form 40-F, Exhibit 99.2)","2026-02-11",{"level":193,"checkedAt":226},"manulife-generative-ai-life-underwriting","manulife",0,[260,266],{"kpi":36,"label":261,"unit":182,"aggregate":188,"higherIsBetter":188,"n":262,"nUpTo":258,"median":181,"min":181,"max":181,"byClaimant":263,"vendorOnly":165,"points":264},"Automation rate",1,{"organization":262,"vendor":258,"regulator":258,"independent":258},[265],{"evidenceId":195,"organization":164,"value":181,"qualifier":183,"claimant":185,"grade":194,"pooled":188},{"kpi":37,"label":267,"unit":182,"aggregate":188,"higherIsBetter":188,"n":262,"nUpTo":258,"median":57,"min":57,"max":57,"byClaimant":268,"vendorOnly":165,"points":269},"Cycle time reduction",{"organization":262,"vendor":258,"regulator":258,"independent":258},[270],{"evidenceId":227,"organization":203,"value":57,"qualifier":216,"claimant":185,"grade":194,"pooled":188},{"low":272,"high":273},400000,2400000,[275,297,320,336],{"slug":153,"title":276,"shortTitle":277,"definition":278,"status":9,"industries":279,"functions":280,"patterns":281,"audience":285,"autonomy":286,"adoptionStage":27,"segment":19,"evidenceCount":287,"publicEvidenceCount":287,"organizations":288,"bestGrade":194,"headline":292,"lastVerified":296,"indexable":188},"AI summarization of medical evidence for life and health underwriting","Life underwriting medical summaries","AI medical evidence summarization reads the medical records behind a life or health insurance application and turns them into a structured, cited summary for the underwriter. The records include attending physician statements, electronic health records, lab results and disclosures, and the summary covers conditions, treatments and dates, mapped to the insurer's underwriting manual so the underwriter can decide faster and more consistently.",[17],[19],[282,283,284],"document-processing","summarization","rag-knowledge-assistant","employee-facing","copilot",4,[289,232,290,291],"Aviva","Prudential plc","SBLI",{"kpi":293,"label":294,"unit":182,"n":262,"nUpTo":258,"kind":295,"value":57,"qualifier":216,"claimant":185,"organization":289,"vendorReported":165},"handling-time-reduction","Handling time reduction","reported","2026-09-27",{"slug":298,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":303,"patterns":305,"audience":25,"autonomy":26,"adoptionStage":27,"segment":19,"evidenceCount":306,"publicEvidenceCount":306,"organizations":307,"bestGrade":194,"headline":316,"lastVerified":296,"indexable":188},"commercial-underwriting-submission-triage","AI for commercial underwriting submission intake and triage","Underwriting submission triage","AI that reads incoming broker submissions for commercial insurance so underwriters open a complete, prioritized file instead of an inbox. It reads emails, applications, schedules of values, loss runs and supplements, extracts the risk data into a structured record, checks clearance and appetite, and enriches and ranks the risk with internal and third party data. Its job ends when a cleared, in appetite and ranked submission reaches an underwriter's queue.",[17],[19,304],"operations",[282,22,21],8,[308,309,310,311,312,313,314,315],"American International Group","AXIS Capital","CNA Financial","Hiscox","Kinsale Capital Group","Markel","Paragon Insurance Group","Skyward Specialty Insurance Group",{"kpi":317,"label":318,"unit":182,"n":262,"nUpTo":258,"kind":295,"value":319,"qualifier":216,"claimant":185,"organization":314,"vendorReported":165},"accuracy","Accuracy",98,{"slug":321,"title":322,"shortTitle":323,"definition":324,"status":9,"industries":325,"functions":326,"patterns":328,"audience":25,"autonomy":26,"adoptionStage":27,"segment":19,"evidenceCount":287,"publicEvidenceCount":287,"organizations":329,"bestGrade":194,"headline":334,"lastVerified":335,"indexable":188},"straight-through-small-business-cyber-underwriting","AI straight through underwriting for small business cyber insurance","Straight through cyber underwriting","Straight through cyber underwriting is AI that scans a small business's external footprint, scores its cyber risk and issues a bindable quote without an underwriter touching it. Flagged or large risks are referred to an underwriter, and the corrections underwriters make can be used to retrain the model.",[17],[19,327],"risk-management",[21,22],[330,331,332,333],"At-Bay","Coalition","Cowbell","Zurich Australia",{"kpi":37,"label":267,"unit":182,"n":262,"nUpTo":258,"kind":295,"value":57,"qualifier":183,"claimant":185,"organization":331,"vendorReported":165},"2026-10-13",{"slug":337,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":342,"patterns":343,"audience":25,"autonomy":345,"adoptionStage":27,"segment":346,"evidenceCount":287,"publicEvidenceCount":287,"organizations":347,"bestGrade":352,"headline":353,"lastVerified":335,"indexable":188},"aerial-imagery-property-risk-scoring","AI aerial imagery risk scoring for property insurance underwriting","Aerial imagery property risk scoring","AI property risk scoring reads aerial imagery to rate roof condition, wildfire exposure and other building features at quote, so underwriters inspect fewer homes. It turns an address into a score and a set of flags that feed the insurer's eligibility, inspection and pricing rules, and the underwriter or the rules engine keeps the decision.",[17],[19,327],[344,21],"computer-vision","assist","property-underwriting",[348,349,350,351],"California FAIR Plan Association","CSAA Insurance Group","Florida Peninsula Insurance Company","Zurich North America","C",{"kpi":317,"label":318,"unit":182,"n":262,"nUpTo":258,"kind":295,"value":354,"qualifier":355,"claimant":185,"organization":350,"vendorReported":165},95,"at-least",{"indexable":188,"reasons":357},[],[359,363,368,375,382,389,395,401,408,415,421,427,434,440,447,454,460,466,470,476,482,487,494,499,505,512,519,523,528,535,541,547,553,560,565,571,577],{"id":116,"label":360,"issuer":123,"region":124,"url":125,"description":361,"useCases":362,"indexable":188},"EU AI Act","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",306,{"id":117,"label":364,"issuer":123,"region":124,"url":365,"description":366,"useCases":367,"indexable":188},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",276,{"id":369,"label":370,"issuer":371,"region":233,"url":372,"description":373,"useCases":374,"indexable":188},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",149,{"id":376,"label":377,"issuer":378,"region":130,"url":379,"description":380,"useCases":381,"indexable":188},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",114,{"id":383,"label":384,"issuer":385,"region":124,"url":386,"description":387,"useCases":388,"indexable":188},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",87,{"id":390,"label":391,"issuer":123,"region":124,"url":392,"description":393,"useCases":394,"indexable":188},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":396,"label":397,"issuer":398,"region":124,"url":399,"description":400,"useCases":57,"indexable":188},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",{"id":402,"label":403,"issuer":404,"region":167,"url":405,"description":406,"useCases":407,"indexable":188},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":409,"label":410,"issuer":411,"region":167,"url":412,"description":413,"useCases":414,"indexable":188},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":416,"label":417,"issuer":123,"region":124,"url":418,"description":419,"useCases":420,"indexable":188},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",23,{"id":422,"label":423,"issuer":424,"region":233,"url":425,"description":426,"useCases":420,"indexable":188},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":428,"label":429,"issuer":430,"region":130,"url":431,"description":432,"useCases":433,"indexable":188},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",21,{"id":435,"label":436,"issuer":437,"region":130,"url":438,"description":439,"useCases":433,"indexable":188},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":441,"label":442,"issuer":443,"region":124,"url":444,"description":445,"useCases":446,"indexable":188},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":448,"label":449,"issuer":450,"region":130,"url":451,"description":452,"useCases":453,"indexable":188},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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.",17,{"id":455,"label":456,"issuer":123,"region":124,"url":457,"description":458,"useCases":459,"indexable":188},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",14,{"id":461,"label":462,"issuer":463,"region":233,"url":464,"description":465,"useCases":459,"indexable":188},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":118,"label":467,"issuer":123,"region":124,"url":468,"description":469,"useCases":459,"indexable":188},"Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":471,"label":472,"issuer":473,"region":233,"url":474,"description":475,"useCases":459,"indexable":188},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":477,"label":478,"issuer":123,"region":124,"url":479,"description":480,"useCases":481,"indexable":188},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",13,{"id":483,"label":484,"issuer":123,"region":124,"url":485,"description":486,"useCases":481,"indexable":188},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":488,"label":489,"issuer":490,"region":130,"url":491,"description":492,"useCases":493,"indexable":188},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",12,{"id":495,"label":496,"issuer":123,"region":124,"url":497,"description":498,"useCases":493,"indexable":188},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":500,"label":501,"issuer":123,"region":124,"url":502,"description":503,"useCases":504,"indexable":188},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",11,{"id":506,"label":507,"issuer":508,"region":130,"url":509,"description":510,"useCases":511,"indexable":188},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",10,{"id":513,"label":514,"issuer":515,"region":124,"url":516,"description":517,"useCases":518,"indexable":188},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":119,"label":520,"issuer":123,"region":124,"url":521,"description":522,"useCases":518,"indexable":188},"Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":524,"label":525,"issuer":404,"region":167,"url":526,"description":527,"useCases":518,"indexable":188},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":529,"label":530,"issuer":531,"region":130,"url":532,"description":533,"useCases":534,"indexable":188},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",7,{"id":536,"label":537,"issuer":123,"region":124,"url":538,"description":539,"useCases":540,"indexable":188},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",5,{"id":542,"label":543,"issuer":544,"region":124,"url":545,"description":546,"useCases":540,"indexable":188},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":548,"label":549,"issuer":550,"region":167,"url":551,"description":552,"useCases":287,"indexable":188},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":554,"label":555,"issuer":556,"region":557,"url":558,"description":559,"useCases":287,"indexable":188},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",{"id":561,"label":562,"issuer":404,"region":167,"url":563,"description":564,"useCases":287,"indexable":188},"mas-shared-responsibility-framework","MAS Shared Responsibility Framework","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fguidelines\u002Fguidelines-on-shared-responsibility-framework","Singapore's framework that assigns banks and telcos duties and loss sharing for phishing scams.",{"id":566,"label":567,"issuer":568,"region":124,"url":569,"description":570,"useCases":287,"indexable":188},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":572,"label":573,"issuer":123,"region":124,"url":574,"description":575,"useCases":576,"indexable":188},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",3,{"id":578,"label":579,"issuer":580,"region":130,"url":581,"description":582,"useCases":576,"indexable":188},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1794572845588]