[{"data":1,"prerenderedAt":620},["ShallowReactive",2],{"uc-insurance-renewal-and-retention":3,"uc-regulations":413},{"useCase":4,"evidence":203,"blitsAiDeployments":293,"benchmarks":294,"indicative":295,"related":298,"indexability":411,"includeUnpublished":210},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":21,"channels":26,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":37,"valueDrivers":38,"kpis":43,"indicativeValue":49,"macroEstimates":84,"feasibility":85,"implementation":98,"risk":141,"blitsAi":179,"faq":181,"related":191,"datePublished":197,"dateModified":197,"lastVerified":198,"changelog":199,"slug":202},"AI for insurance renewal processing and customer retention","Renewal and retention","AI for insurance renewals and policy retention","AI can speed up insurance renewals and flag customers likely to lapse, while prices stay under pricing rules. Hiscox began its Gemini underwriting model on renewals.","published","AI that prepares and runs the renewal cycle: it digitizes renewal submissions and changes in risk for underwriters, flags policies at risk of lapsing or leaving, prepares the renewal conversation and answers customers' renewal questions, while renewal prices stay governed by the insurer's pricing rules and fair value obligations.",[12,13,14],"renewal automation","lapse and churn prevention","policy retention assistant",[16],"insurance",[18,19,20],"underwriting","customer-service","sales",[22,23,24,25],"prediction-and-scoring","document-processing","conversational-agent","recommendation-and-personalization",[27,28,29,30],"email","web-chat","voice","internal-tools","back-office","copilot","emerging","distribution","In general insurance, most policies come up for renewal every year, so renewals decide how much of\nthe book an insurer keeps. In commercial lines, renewal submissions arrive in the same unstructured formats as new business,\nand underwriters must spot what changed in the risk before the renewal date, which is hard to do for\nevery account when volumes peak. In personal lines, customers can leave quietly at renewal when a\npremium rises, and renewal peaks put pressure on contact centres. Life and protection policies\nusually stay in force while premiums are paid, so there the risk is a lapse after a missed or\nfailed payment rather than a renewal decision.\n\nRetention is also regulated. In the UK, home and motor insurers may not offer renewing customers a\nprice above the equivalent new business price, and in the EU, AI used for risk assessment and\npricing of individuals in life and health insurance is high risk under the AI Act. So the\nopportunity is in better renewal processing, earlier outreach and clearer explanations, not in using\nAI to find customers who will tolerate higher prices.",[],"1. **Digitize the renewal.** Renewal submissions and updated schedules are read and compared with\n   the expiring policy, and changes in exposure are highlighted for the underwriter.\n2. **Triage the renewal book.** Low complexity renewals within appetite are prepared for automated\n   or light touch processing under existing rules; complex or deteriorating risks go to an\n   underwriter early.\n3. **Spot lapse risk.** A model flags customers likely to lapse or leave (failed payments,\n   engagement changes, large premium changes) so service teams can reach out in time.\n4. **Prepare the conversation.** The assistant drafts renewal explanations and answers customers'\n   questions about what changed and why, from the renewal documents.\n5. **Keep price decisions governed.** Renewal prices come from the insurer's pricing rules, checked\n   against fair value and renewal pricing rules; the AI does not set them.",[39,40,41,42],"revenue-growth","employee-productivity","customer-experience","compliance",[44,45,46,47,48],"churn-reduction","automation-rate","processing-time-reduction","revenue-uplift","customer-satisfaction",{"referenceOrg":50,"inputs":51,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A personal lines insurer with 500,000 policies up for renewal each year",[52,58,65,72],{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"renewals","Policies up for renewal per year",500000,"policies per year","The reference insurer.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"lapseRate","Baseline share of policies not renewed",0.15,0.2,"fraction of renewals","Editorial assumption. Replace with your own lapse and cancellation data.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"relativeReduction","Relative reduction in avoidable lapses",0.02,0.05,"fraction of lapses","Editorial assumption; no insurer on this page publishes a measured retention effect. Measure it against a control group.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"averagePremium","Average annual premium",400,600,"USD per policy","Editorial assumption. Replace with your own average premium.","renewals * lapseRate * relativeReduction * averagePremium","USD","per year","Premium retained","Premium retained, not profit, and not all lapses are worth preventing. It leaves out underwriting time saved on renewal processing, the cost of outreach and the platform, and any customer who would have renewed anyway.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Renewal digitization reuses submission intake technology; lapse models are standard machine learning. The difficult part is governance: keeping retention work separate from price optimization that regulators restrict, and proving the effect with a control group.",[89,90,91,92],"Expiring policy data and renewal submissions per account","Payment, contact and engagement history for lapse prediction","Renewal pricing rules and fair value assessments","Consent and preference data for outreach",[94,95,96,97],"Policy administration and renewal processing","Underwriting workbench","Billing and payment systems","CRM and contact centre for outreach and handover",{"steps":99,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[100,103,106,109,112],{"title":101,"detail":102},"Separate the two problems","Treat commercial renewal processing (underwriting efficiency) and personal lines retention (customer outreach) as separate projects with different owners and controls.",{"title":104,"detail":105},"Start with renewal intake in commercial lines","Reuse submission extraction on renewal documents and show underwriters what changed against the expiring terms, starting with lines that have high renewal volumes.",{"title":107,"detail":108},"Build lapse prediction with outreach, not pricing","Use lapse scores only to decide who gets a service call, a payment reminder or a policy review, never to adjust the renewal price.",{"title":110,"detail":111},"Test with a control group","Hold out a random share of flagged customers to measure the true retention effect before scaling outreach.",{"title":113,"detail":114},"Review for fair value","Have pricing and compliance confirm that nothing in the retention process changes renewal price by tenure or propensity to shop around where rules forbid it.",[116,117,118,119,120],"Lapse and churn scores never feed renewal pricing","Renewal prices only from governed pricing rules, checked against renewal pricing requirements","Outreach limited to customers who consented to contact, with vulnerability checks","Automated renewals only for low complexity risks within written rules","Explanations of premium changes drawn from the actual renewal documents","Underwriters review every renewal outside the automated cohort and every material change in risk. Service staff make retention calls with AI prepared context, and pricing and compliance own the rules that separate retention outreach from price setting.",[123,124,125,126,127],"Share of renewals reviewed before the renewal date","Retention rate for flagged customers versus a random control group","Underwriting time per renewal","Complaints about renewal prices and communications","Fair value and renewal pricing test results",[129,132,135,138],{"title":130,"detail":131},"Retention models become price optimization","Scores that predict who will not shop around drift into pricing decisions. Keep the systems and teams separate and audit the data flows.",{"title":133,"detail":134},"Rolled over risks nobody looked at","Automated renewal processes a risk that changed materially. Compare against the expiring policy and route changes to an underwriter.",{"title":136,"detail":137},"Retention effect that was never there","Outreach goes to customers who would have renewed anyway. Measure against a control group.",{"title":139,"detail":140},"Pushy outreach to vulnerable customers","Retention calls pressure customers in financial difficulty. Check vulnerability signals first.",{"euAiAct":142,"regulations":145,"guidance":151,"controls":168,"incidents":174},{"tier":143,"basis":144},"context-dependent","Renewal intake for commercial lines and outreach are not listed in Annex III. Renewal risk assessment or pricing for life or health insurance of natural persons is high risk under point 5(c), and so is a lapse score that feeds those decisions; a lapse score used only to decide who gets a service call is not listed. Customer facing renewal assistants carry the Article 50(1) duty to tell people they are interacting with an AI system, unless that is obvious from the context.",[146,147,148,149,150],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","eu-idd",[152,158,163],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"PS21/5: General insurance pricing practices market study, feedback to CP20/19 and final rules","Financial Conduct Authority","europe","https://www.fca.org.uk/publication/policy/ps21-5.pdf","Final rules requiring firms to offer UK home and motor insurance renewal prices no greater than the equivalent new business price, however the price is set.",{"title":159,"issuer":160,"region":155,"url":161,"note":162},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","https://artificialintelligenceact.eu/annex/3/","Point 5(c) covers AI systems for risk assessment and pricing in relation to natural persons in life and health insurance, which includes those decisions when they are made at renewal.",{"title":164,"issuer":165,"region":155,"url":166,"note":167},"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","Published August 2025 for national supervisors. Asks insurers to treat customers fairly across the AI lifecycle and value chain, monitor outcomes with fairness and non discrimination metrics, and points to EIOPA's 2023 Supervisory Statement on Differential Pricing Practices.",[169,170,171,172,173],"Documented separation between retention scoring and pricing","Control group design and results for every retention programme","Fair value and renewal pricing tests signed off by pricing and compliance","Vulnerability screening before outreach","Audit trail of automated renewals and the rules applied",[175],{"title":176,"url":177,"note":178},"Suckers List: How Allstate's Secret Auto Insurance Algorithm Squeezes Big Spenders","https://themarkup.org/allstates-algorithm/2020/02/25/car-insurance-suckers-list","The Markup and Consumer Reports analysed a \"retention model\" Allstate proposed in Maryland and found it would have charged big spenders more rather than following risk; Maryland rejected it as discriminatory and Allstate called the reporting inaccurate. It shows why retention signals must stay out of pricing.",{"howToBuild":180},"On Blits.ai commercial renewal intake is an **agentic workflow** that reads renewal documents from\nthe **email channel** or an API, compares them with the expiring policy retrieved through a **SQL\nknowledge base** or **custom functions**, and returns a change summary as **structured output** for\nthe underwriter. **Human in the loop confirmation** keeps every non routine renewal with an\nunderwriter, who approves or rejects it.\n\nFor personal lines, **agentic tasks** can watch for conditions such as a failed payment before\nrenewal and send an outbound message through the **email channel**, with consent checks in the\nflow. Customers who reply or get in touch on **web chat, WhatsApp, SMS or voice** reach a renewal\nassistant that retrieves their renewal documents through **custom functions** and answers what\nchanged and why, with **human handover** to retention staff. The platform does not set prices:\nthe **tool execution policy** keeps pricing tools out of the agent's reach. Each workflow's\n**run data** can be downloaded, so you can compare treated and control groups in your own\nanalysis outside the platform.",[182,185,188],{"question":183,"answer":184},"Can AI improve insurance retention without breaking pricing rules?","Yes, if it works on service rather than price: earlier outreach, fixing payment problems, explaining changes clearly and processing renewals on time. In the UK, renewal prices for home and motor may not exceed the equivalent new business price, so retention models must stay out of pricing.",{"question":186,"answer":187},"Are there published results?","Few so far. Hiscox's generative AI lead underwriting model started with renewals of existing sabotage and terrorism risks, Nsure.com's copilot helps customers review renewal offers, and Microsoft's customer story on Zurich says user feedback shows its sales copilot improves sales and retention ratios, but none publish a measured retention effect.",{"question":189,"answer":190},"Is AI in renewals high risk under the EU AI Act?","Only where it assesses risk or sets prices for life or health insurance of individuals, which is high risk under Annex III point 5(c). Commercial renewal processing and service outreach are not listed, but a customer facing renewal assistant must tell people they are talking to an AI unless that is obvious from the context (Article 50(1)).",[192,193,194,195,196],"insurance-policy-servicing-agent","commercial-underwriting-submission-triage","insurance-pricing-and-actuarial-copilot","churn-prediction-and-retention-offers","insurance-broker-and-agent-assistant","2026-09-27","2026-09-26",[200],{"date":197,"note":201},"First published","insurance-renewal-and-retention",[204,238,270],{"title":205,"useCases":206,"organization":208,"vendors":212,"summary":216,"stage":217,"year":218,"channels":219,"languages":220,"metrics":222,"outcomeDisclosed":210,"sources":223,"verification":233,"grade":235,"id":236,"organizationSlug":237},"Hiscox: generative AI lead underwriting model for sabotage and terrorism risks",[193,207,202],"underwriting-risk-assessment-copilot",{"name":209,"anonymized":210,"country":211,"region":155,"industry":16},"Hiscox",false,"GB",[213],{"name":214,"role":215},"Google Cloud","platform","Hiscox London Market combined its own Hiscox AI Laboratories (Hailo) with Google Cloud's Gemini model to automate lead underwriting from email submission to quote in its sabotage and terrorism line. In scope risks are assessed by the model and the process generates an email to the broker with pricing and other data completed, ready for underwriter review. After a December 2023 proof of concept, in which Hiscox said the manual extraction step can take up to three days and quotes could be produced within three minutes, the model went live in August 2024. It initially covers renewals of existing US and Canadian sabotage and terrorism risks, excluding the New York and Chicago metro areas.","production",2024,[27,30],[221],"en",[],[224,229],{"url":225,"title":226,"publisher":227,"date":228},"https://www.hiscoxgroup.com/news/press-releases/2024/12-08-24","Hiscox's generative AI-enhanced lead underwriting model enabled by Google Cloud goes live","Hiscox Group","2024-08-12",{"url":230,"title":231,"publisher":227,"date":232},"https://www.hiscoxgroup.com/news/press-releases/2023/12-12-23","Hiscox and Google Cloud Collaborate on AI in lead underwriting for the London Market","2023-12-12",{"level":234,"checkedAt":197},"source-verified","B","hiscox-generative-ai-lead-underwriting",null,{"title":239,"useCases":240,"organization":242,"vendors":245,"summary":248,"stage":217,"year":249,"channels":250,"languages":252,"metrics":253,"outcomeDisclosed":262,"sources":263,"verification":267,"grade":268,"id":269,"organizationSlug":237},"Zurich Insurance Group: Copilot for Sales to keep commercial insurance CRM data current",[241,196,202],"sales-call-coaching-and-crm-update",{"name":243,"anonymized":210,"country":244,"region":155,"industry":16},"Zurich Insurance Group","CH",[246],{"name":247,"role":215},"Microsoft","Zurich's commercial insurance teams manage more than 100,000 active opportunities in Dynamics 365, and switching applications to copy updates from email into the CRM left data at risk of going stale. With Microsoft 365 Copilot for Sales, 300 users create and update contacts and link emails to opportunities from Outlook, get summaries of relationships and long email threads, and draft emails. Zurich estimates about 14,000 hours saved over the next year; that is an estimate, not a measured result. The story also says user feedback indicates the tool improves Zurich's sales and retention ratios, without figures.",2025,[27,251,30],"microsoft-teams",[221],[254],{"kpi":255,"value":256,"unit":257,"qualifier":258,"claimant":259,"quote":260,"sourceUrl":261},"users-served",300,"count","exact","vendor","Copilot for Sales has quickly become a critical productivity tool for Zurich’s 300 Copilot users, who find even more benefits as they continue to work with it in Outlook.","https://www.microsoft.com/en/customers/story/23452-zurich-schweiz-microsoft-365-copilot-for-sales",true,[264],{"url":261,"title":265,"publisher":266},"Zurich Insurance enhances customer relationships with Dynamics 365 and Microsoft 365 Copilot for Sales","Microsoft Customer Stories",{"level":234,"checkedAt":198},"C","zurich-copilot-for-sales-crm-updates",{"title":271,"useCases":272,"organization":273,"vendors":277,"summary":282,"stage":217,"year":218,"channels":283,"languages":285,"metrics":286,"outcomeDisclosed":262,"sources":287,"verification":291,"grade":268,"id":292,"organizationSlug":237},"Nsure.com: Friendly John copilot for payments, renewal offers and discount requests",[192,202],{"name":274,"anonymized":210,"country":275,"region":276,"industry":16},"Nsure.com","US","north-america",[278,279],{"name":247,"role":215},{"name":280,"role":281},"Netwise","integrator","Nsure.com is a Florida based digital insurance agency that lets consumers compare home and auto quotes from more than 50 insurers and buy online. Generative AI in Power Automate cut its service representatives' manual processing time by more than 60%, for example by triaging the shared inboxes and either preparing an automated response or routing each email to an agent. It replaced a third party chatbot with a Copilot Studio copilot, Friendly John, that helps customers submit payments, review renewal offers and request discounts, with an interactive voice response option and after hours support. Its VP of AI and Automation says it handles around 60% of customer questions, and the company plans to use copilots for new policy sales and cross selling.",[28,29,27,284],"sms",[221],[],[288],{"url":289,"title":290,"publisher":266},"https://www.microsoft.com/en/customers/story/1728829430186194098-nsure-power-platform-insurance-usa","Digital insurance agency, Nsure.com, reduces manual processing time by 60% using generative AI and Power Automate",{"level":234,"checkedAt":198},"nsure-friendly-john-copilot",0,[],{"low":296,"high":297},600000,3000000,[299,330,354,378,398],{"slug":192,"title":300,"shortTitle":301,"definition":302,"status":9,"industries":303,"functions":304,"patterns":306,"audience":310,"autonomy":311,"adoptionStage":312,"segment":313,"evidenceCount":314,"publicEvidenceCount":314,"organizations":315,"bestGrade":235,"headline":321,"lastVerified":197,"indexable":262},"AI agent for insurance policy servicing","Policy servicing agent","An AI agent that answers policyholders' coverage questions from their own policy documents and completes routine policy changes and document requests (address and vehicle changes, adding a named driver or item, payment method updates, certificates and proof of cover) across chat, messaging and phone, and hands anything complex or sensitive to a human with the context.",[16],[19,305],"operations",[24,307,308,309],"voice-agent","rag-knowledge-assistant","agentic-workflow","customer-facing","supervised-agent","early-adopters","policy-administration",6,[316,317,274,318,319,320],"LAQO","Lemonade","Sun Life","Waterdrop","Zurich Insurance (Hong Kong)",{"kpi":322,"label":323,"unit":324,"n":325,"nUpTo":293,"kind":326,"value":327,"qualifier":328,"claimant":329,"organization":317,"vendorReported":210},"containment-rate","Containment rate","percent",2,"reported",50,"at-least","organization",{"slug":193,"title":331,"shortTitle":332,"definition":333,"status":9,"industries":334,"functions":335,"patterns":336,"audience":31,"autonomy":311,"adoptionStage":312,"segment":18,"evidenceCount":338,"publicEvidenceCount":338,"organizations":339,"bestGrade":235,"headline":348,"lastVerified":197,"indexable":262},"AI for commercial underwriting submission intake and triage","Underwriting submission triage","AI that reads incoming broker submissions for commercial insurance (emails, applications, schedules of values, loss runs and supplements), extracts the risk data into a structured record, checks clearance and appetite, enriches the risk with internal and third party data and ranks it, so underwriters open a complete, prioritized file instead of an inbox.",[16],[18,305],[23,337,22,309],"classification-and-routing",9,[340,341,342,343,209,344,345,346,347],"American International Group","AXIS Capital","CNA Financial","Generali Global Corporate & Commercial","Kinsale Capital Group","Markel","Paragon Insurance Group","Skyward Specialty Insurance Group",{"kpi":349,"label":350,"unit":324,"n":351,"nUpTo":293,"kind":326,"value":352,"qualifier":353,"claimant":329,"organization":346,"vendorReported":210},"accuracy","Accuracy",1,98,"approximately",{"slug":194,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":359,"patterns":363,"audience":366,"autonomy":32,"adoptionStage":312,"segment":367,"evidenceCount":368,"publicEvidenceCount":368,"organizations":369,"bestGrade":235,"headline":374,"lastVerified":198,"indexable":262},"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],[360,361,362],"product-and-pricing","risk-management","analytics-and-reporting",[22,364,309,365],"code-generation","summarization","employee-facing","pricing",5,[370,371,372,344,373],"Accelerant Holdings","Europ Assistance","Generali France","MAIF",{"kpi":375,"label":376,"unit":377,"n":351,"nUpTo":293,"kind":326,"value":368,"qualifier":258,"claimant":329,"organization":372,"vendorReported":210},"productivity-gain","Productivity gain","multiplier",{"slug":195,"title":379,"shortTitle":380,"definition":381,"status":9,"industries":382,"functions":384,"patterns":386,"audience":31,"autonomy":311,"adoptionStage":387,"segment":388,"evidenceCount":389,"publicEvidenceCount":389,"organizations":390,"bestGrade":235,"headline":395,"lastVerified":197,"indexable":262},"AI for telecom churn prediction and retention offers","Churn prediction and retention","AI for telecom operators that scores each subscriber's risk of leaving from usage, service, billing and contact signals, explains the likely reason, and chooses the next best retention action, such as fixing a problem, adjusting a plan or making an offer, delivered through the app, messaging, an agent or an advisor within approved offer budgets.",[383],"telecommunications",[385,19,20,362],"marketing",[22,25,24],"mainstream","middle-office",4,[391,392,393,394],"Etisalat","Telenet","Virgin Media O2","Vodafone UK",{"kpi":44,"label":396,"unit":324,"n":325,"nUpTo":293,"kind":326,"value":397,"qualifier":258,"claimant":259,"organization":392,"vendorReported":262},"Churn reduction",20,{"slug":196,"title":399,"shortTitle":400,"definition":401,"status":9,"industries":402,"functions":403,"patterns":405,"audience":366,"autonomy":407,"adoptionStage":312,"segment":34,"evidenceCount":368,"publicEvidenceCount":368,"organizations":408,"bestGrade":235,"headline":237,"lastVerified":197,"indexable":262},"AI assistant for insurance brokers and agents","Broker and agent assistant","An AI assistant for tied agents, independent brokers, advisors and the insurer's own distribution staff that answers product, underwriting and process questions from approved sources, prepares personalized customer engagement and follow ups, validates and prioritizes leads, and drafts meeting notes and emails, so producers spend more time with customers.",[16],[20,404],"knowledge-management",[308,25,406,365],"content-generation","assist",[409,410,318,319,243],"Manulife","Prudential plc",{"indexable":262,"reasons":412},[],[414,419,424,432,439,445,451,456,464,471,477,483,490,497,503,508,515,521,527,533,539,545,551,556,561,567,574,579,583,590,596,602,609,614],{"id":146,"label":415,"issuer":160,"region":155,"url":416,"description":417,"useCases":418,"indexable":262},"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":147,"label":420,"issuer":160,"region":155,"url":421,"description":422,"useCases":423,"indexable":262},"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":425,"label":426,"issuer":427,"region":428,"url":429,"description":430,"useCases":431,"indexable":262},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":433,"label":434,"issuer":435,"region":276,"url":436,"description":437,"useCases":438,"indexable":262},"nist-ai-rmf","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":440,"label":441,"issuer":160,"region":155,"url":442,"description":443,"useCases":444,"indexable":262},"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.",66,{"id":148,"label":446,"issuer":447,"region":155,"url":448,"description":449,"useCases":450,"indexable":262},"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":149,"label":452,"issuer":154,"region":155,"url":453,"description":454,"useCases":455,"indexable":262},"FCA Consumer Duty","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":457,"label":458,"issuer":459,"region":460,"url":461,"description":462,"useCases":463,"indexable":262},"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.",36,{"id":465,"label":466,"issuer":467,"region":460,"url":468,"description":469,"useCases":470,"indexable":262},"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":472,"label":473,"issuer":474,"region":428,"url":475,"description":476,"useCases":397,"indexable":262},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":478,"label":479,"issuer":480,"region":276,"url":481,"description":482,"useCases":397,"indexable":262},"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":484,"label":485,"issuer":486,"region":155,"url":487,"description":488,"useCases":489,"indexable":262},"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":491,"label":492,"issuer":493,"region":428,"url":494,"description":495,"useCases":496,"indexable":262},"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":498,"label":499,"issuer":160,"region":155,"url":500,"description":501,"useCases":502,"indexable":262},"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":504,"label":505,"issuer":160,"region":155,"url":506,"description":507,"useCases":502,"indexable":262},"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":509,"label":510,"issuer":511,"region":276,"url":512,"description":513,"useCases":514,"indexable":262},"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":516,"label":517,"issuer":160,"region":155,"url":518,"description":519,"useCases":520,"indexable":262},"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":522,"label":523,"issuer":524,"region":276,"url":525,"description":526,"useCases":520,"indexable":262},"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.",{"id":528,"label":529,"issuer":530,"region":428,"url":531,"description":532,"useCases":520,"indexable":262},"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":534,"label":535,"issuer":160,"region":155,"url":536,"description":537,"useCases":538,"indexable":262},"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":540,"label":541,"issuer":542,"region":276,"url":543,"description":544,"useCases":538,"indexable":262},"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":546,"label":547,"issuer":459,"region":460,"url":548,"description":549,"useCases":550,"indexable":262},"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":552,"label":553,"issuer":160,"region":155,"url":554,"description":555,"useCases":550,"indexable":262},"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":557,"label":558,"issuer":160,"region":155,"url":559,"description":560,"useCases":550,"indexable":262},"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":562,"label":563,"issuer":564,"region":155,"url":565,"description":566,"useCases":338,"indexable":262},"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.",{"id":568,"label":569,"issuer":570,"region":276,"url":571,"description":572,"useCases":573,"indexable":262},"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.",8,{"id":575,"label":576,"issuer":160,"region":155,"url":577,"description":578,"useCases":573,"indexable":262},"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":150,"label":580,"issuer":160,"region":155,"url":581,"description":582,"useCases":314,"indexable":262},"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":584,"label":585,"issuer":586,"region":587,"url":588,"description":589,"useCases":368,"indexable":262},"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":591,"label":592,"issuer":593,"region":155,"url":594,"description":595,"useCases":389,"indexable":262},"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":597,"label":598,"issuer":599,"region":155,"url":600,"description":601,"useCases":389,"indexable":262},"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":603,"label":604,"issuer":605,"region":460,"url":606,"description":607,"useCases":608,"indexable":262},"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":610,"label":611,"issuer":160,"region":155,"url":612,"description":613,"useCases":608,"indexable":262},"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":615,"label":616,"issuer":617,"region":276,"url":618,"description":619,"useCases":608,"indexable":262},"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.",1790598300217]