[{"data":1,"prerenderedAt":616},["ShallowReactive",2],{"uc-collections-and-hardship-agent":3,"uc-regulations":415},{"useCase":4,"evidence":221,"blitsAiDeployments":289,"benchmarks":290,"indicative":307,"related":310,"indexability":413,"includeUnpublished":227},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":24,"patterns":27,"channels":32,"audience":38,"autonomy":39,"adoptionStage":40,"segment":41,"problem":42,"problemStats":43,"howItWorks":44,"valueDrivers":45,"kpis":50,"indicativeValue":56,"macroEstimates":91,"feasibility":92,"implementation":106,"risk":150,"blitsAi":197,"faq":199,"related":209,"datePublished":216,"dateModified":216,"lastVerified":216,"changelog":217,"slug":220},"AI agent for early collections and hardship support","Collections and hardship agent","AI debt collection agent with hardship routing","AI collections agents call early arrears, take payments within set rules and pass hardship to people. SameDay Auto Finance's vendor reports 43% higher collections.","published","A voice and messaging agent that contacts customers in early arrears and answers their inbound calls, takes payments and sets up payment arrangements within preapproved rules, and recognises signs of hardship or vulnerability so those customers go straight to a trained person.",[12,13,14,15],"AI debt collection agent","voice AI for collections","arrears and hardship assistant","digital collections agent",[17,18,19,20,21,22,23],"cross-industry","banking","payments","telecommunications","energy-and-utilities","automotive","professional-services",[25,26],"collections-and-recovery","customer-service",[28,29,30,31],"voice-agent","conversational-agent","agentic-workflow","classification-and-routing",[33,34,35,36,37],"voice","sms","whatsapp","email","web-chat","customer-facing","supervised-agent","early-adopters","lending","Many customers who miss a payment are not refusing to pay: their payday moved, a payment failed or\ntheir circumstances changed. A short, timely conversation in the first days of arrears resolves\nmany of these cases, but collections teams often lack the capacity to reach every account in that\nwindow, and contact outside office hours is limited. Accounts then roll into later buckets where\nrecovery is harder and more expensive.\n\nAt the same time collections is a heavily regulated conversation. Contact\nfrequency, tone, disclosures and the treatment of customers in financial difficulty are all set out\nin rules, and getting it wrong with a vulnerable customer causes real harm. Automation that only\npushes for payment makes this worse; automation that listens and routes well can make it better.",[],"1. **Reach out at the right time.** The agent contacts accounts in early arrears on the channel and\n   at the time each customer is most likely to respond, within contact frequency rules.\n2. **Verify and disclose.** It confirms identity before discussing the debt and gives the required\n   disclosures, including that it is an AI agent.\n3. **Understand the reason.** It asks why the payment was missed and classifies the answer, such as\n   a failed payment, a changed pay date or a change in circumstances.\n4. **Resolve within rules.** It takes a payment, sends a secure payment link, moves a due date or\n   sets up a short arrangement, but only within limits the lender has approved.\n5. **Route hardship and vulnerability to people.** Mentions of job loss, illness, bereavement,\n   domestic abuse or distress, or any request for help, go to a trained specialist with a summary,\n   and collection activity pauses.\n6. **Record everything.** Every contact, disclosure, promise to pay and arrangement is logged with\n   its basis for audit and complaint handling.",[46,47,48,49],"cost-to-serve","risk-reduction","customer-experience","compliance",[51,52,53,54,55],"recovery-rate-uplift","cost-reduction","containment-rate","interactions-handled","customer-satisfaction",{"referenceOrg":57,"inputs":58,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A lender with 50,000 accounts entering early arrears each year",[59,65,72,79],{"key":60,"label":61,"low":62,"high":62,"unit":63,"note":64},"accounts","Accounts entering early arrears per year",50000,"accounts per year","The reference lender.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"contactsPerAccount","Outbound and inbound contacts per account in early arrears",4,8,"contacts per account","Editorial assumption. Replace with your own contact data.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"automatedShare","Share of those contacts the agent completes without a person",0.4,0.7,"fraction of contacts","Editorial assumption, deliberately below full automation because hardship cases must reach people.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"costPerContact","Cost of a human handled collections contact",3,6,"USD per contact","Editorial assumption. For comparison, SameDay Auto Finance's vendor reports 75% lower collection call costs in early delinquency.","accounts * contactsPerAccount * automatedShare * costPerContact","USD","per year","Collections contact cost avoided","Contact cost only. It leaves out the usually larger effect of fewer accounts rolling into later arrears and charge off, the cost of running the AI and the payment integration, and the effort of compliance review.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":100},"medium","Conversation design and integrations (arrears data, payments, arrangement rules, dialler and consent) are manageable. The effort is in conduct rules per market, vulnerability detection and a clean handover to specialists.",[96,97,98,99],"Arrears data per account with contact history, consent and preferred channel","Written arrangement rules the agent may offer, with limits per product","Contact frequency and time of day rules per market","Vulnerability and hardship triggers agreed with the specialist team",[101,102,103,104,105],"Collections or loan servicing system","Payment gateway for card and bank payments, or payment links","Telephony and messaging channels, including consent management","Case management or CRM for hardship referrals","Complaint handling",{"steps":107,"guardrails":123,"humanInTheLoop":130,"kpisToInstrument":131,"failureModes":137},[108,111,114,117,120],{"title":109,"detail":110},"Start in the first days past due","Reminder and resolution conversations in the first bucket are high volume, low risk and the cheapest place to prevent roll forward.",{"title":112,"detail":113},"Write the arrangement rules down","List exactly which arrangements the agent may offer (date change, short plan, split payment), with limits, and what it says when a request is outside them.",{"title":115,"detail":116},"Design hardship routing with the specialists","Agree the phrases and situations that pause collection and route to a person, test them on real transcripts, and err on the side of routing.",{"title":118,"detail":119},"Build compliance in","Encode identity checks, disclosures, AI disclosure, contact limits and quiet hours in the flow, not in the prompt, and log each one.",{"title":121,"detail":122},"Pilot against a control group","Run the agent on part of the book, compare roll rates, promises kept, complaints and satisfaction with a human handled control, then widen.",[124,125,126,127,128,129],"Identity verification before any mention of the debt","Clear disclosure that the customer is speaking with an AI agent","Arrangements only within preapproved rules; anything else goes to a person","Immediate handover and a pause in collection on any hardship or vulnerability signal","Contact frequency, time of day and channel consent enforced by the system","No threats, pressure tactics or misleading statements, checked by output guardrails","Trained specialists handle every hardship, vulnerability, dispute and complaint case and every arrangement outside the rules. Quality teams review a sample of AI conversations each week against the conduct standard, and compliance approves every change to scripts or arrangement rules.",[132,133,134,135,136],"Roll rate from the first to the second arrears bucket versus control","Promise to pay kept rate","Share of conversations routed for hardship, and specialist agreement with the routing","Complaints and conduct breaches per thousand conversations","Cost per account resolved",[138,141,144,147],{"title":139,"detail":140},"Missed vulnerability","The agent keeps pressing for payment when a customer mentions illness or job loss. Route on broad signals and review missed cases every week.",{"title":142,"detail":143},"Arrangements that fail","Easy plans accepted to end the call and then broken. Check affordability within the rules and track kept rates per arrangement type.",{"title":145,"detail":146},"Contact that becomes harassment","Automation makes it cheap to call too often. Enforce frequency limits in the system, per customer across channels.",{"title":148,"detail":149},"Payment data in transcripts","Card numbers read aloud end up in logs. Use payment links or secure capture and mask card data before it reaches the model.",{"euAiAct":151,"regulations":154,"guidance":161,"controls":190,"incidents":196},{"tier":152,"basis":153},"limited","A customer facing collections agent must disclose that it is AI (Article 50). It is not listed in Annex III as long as it applies preapproved arrangement rules and does not itself evaluate creditworthiness; an affordability model that decides who gets which arrangement for individuals should be assessed separately against Annex III point 5(b).",[155,156,157,158,159,160],"eu-ai-act","gdpr","uk-consumer-duty","pci-dss","telecom-consumer-rules","us-tcpa",[162,168,174,180,185],{"title":163,"issuer":164,"region":165,"url":166,"note":167},"FG21/1: guidance for firms on the fair treatment of vulnerable customers","Financial Conduct Authority","europe","https://www.fca.org.uk/publication/finalised-guidance/fg21-1.pdf","Expectations for identifying and supporting vulnerable customers, including in automated channels.",{"title":169,"issuer":170,"region":171,"url":172,"note":173},"Debt Collection Practices (Regulation F)","Consumer Financial Protection Bureau","north-america","https://www.consumerfinance.gov/rules-policy/final-rules/debt-collection-practices-regulation-f/","US rules under the Fair Debt Collection Practices Act on communications with consumers, harassment, misleading statements and unfair practices. They govern debt collectors as the FDCPA defines that term, which generally covers third party collectors rather than creditors collecting their own debts.",{"title":175,"issuer":176,"region":177,"url":178,"note":179},"RG 96 Debt collection guideline: for collectors and creditors","Australian Securities and Investments Commission and Australian Competition and Consumer Commission","asia-pacific","https://asic.gov.au/regulatory-resources/find-a-document/regulatory-guides/rg-96-debt-collection-guideline-for-collectors-and-creditors/","Joint ACCC and ASIC guideline on how Australian consumer protection law applies to debt collection, for creditors collecting their own debts and for external collection agencies.",{"title":181,"issuer":182,"region":165,"url":183,"note":184},"Regulation (EU) 2024/1689 (AI Act), Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":186,"issuer":187,"region":171,"url":188,"note":189},"Declaratory ruling on AI generated voices under the TCPA (FCC 24-17)","Federal Communications Commission","https://docs.fcc.gov/public/attachments/FCC-24-17A1.pdf","AI generated voices count as \"artificial or prerecorded voice\" under the TCPA, so US outbound AI calls need the consent the TCPA requires.",[191,192,193,194,195],"Approved scripts and arrangement rules under change control","Logged identity check, disclosures and consent for every contact","Weekly quality sampling with a specific check for missed vulnerability","Complaint monitoring linked to AI conversations","Card data masked or captured outside the conversation",[],{"howToBuild":198},"On Blits.ai this is an **AI agent** on **voice** (telephony with real time streaming speech\nrecognition and synthesis, and call transfer), **SMS**, **WhatsApp** and **email**, with the regulated steps\n(identity check, disclosures, arrangement offer) built as deterministic **flows** and **DTMF**\ninput where customers prefer the keypad. **Custom functions** read arrears data and write\narrangements to the servicing system, and payments go through **payment links** from the payments\nservice, so card numbers never enter the conversation; on voice, **DTMF** capture with the sensitive\ndata flag keeps digits out of the transcript, and in text channels **credit card number detection\nand tokenization** and **PII masking** at the gateway tokenize or redact card numbers a customer types.\n\n**Sentiment analysis** and **guardrails** help detect distress, and **human handover** sends\nhardship and vulnerability cases to a specialist with a conversation summary, including **live\ntakeover** of a voice call. **Agentic tasks** schedule follow ups such as a check after a promised\npayment date. **Test suites** replay hardship scenarios on every change, and **analytics** show\ninteractions, satisfaction and sentiment. The platform is model agnostic and supports\nseveral languages, including Arabic.",[200,203,206],{"question":201,"answer":202},"What results do lenders report from AI collections agents?","The figures on this page come from vendor case studies about US auto lenders and a collection agency. Skit.ai reports 43% higher collections and 75% lower call costs in early delinquency at SameDay Auto Finance, and 63% lower collection costs at Day Knight & Associates, whose own executive says collections doubled. Test against a control group before relying on such numbers.",{"question":204,"answer":205},"Should an AI agent handle customers in financial hardship?","It should recognise them and pass them to a trained person quickly, not negotiate hardship on its own. Hardship and vulnerability need judgment, flexibility and often referral to support that an agent should not decide.",{"question":207,"answer":208},"Is an AI collections agent allowed to call customers?","Generally yes, within the same rules as human collectors: identity checks, disclosures, contact frequency limits, quiet hours and channel consent, plus disclosure that it is AI. Some markets add rules on automated calls: in the US, the FCC treats AI generated voices as artificial voices under the Telephone Consumer Protection Act, which sets consent rules for such calls. Check local telemarketing and collection law.",[210,211,212,213,214,215],"loan-restructuring-recommendations","outbound-notice-drafting","financial-wellbeing-coach","credit-early-warning-monitoring","proactive-outbound-engagement-agent","utility-billing-and-move-agent","2026-09-27",[218],{"date":216,"note":219},"First published","collections-and-hardship-agent",[222,265],{"title":223,"useCases":224,"organization":225,"vendors":229,"summary":233,"stage":234,"year":235,"channels":236,"languages":237,"metrics":239,"outcomeDisclosed":254,"sources":255,"verification":259,"grade":262,"id":263,"organizationSlug":264},"Day Knight & Associates: voice and SMS agents for consumer and healthcare debt",[220],{"name":226,"anonymized":227,"country":228,"region":171,"industry":23},"Day Knight & Associates",false,"US",[230],{"name":231,"role":232},"Skit.ai","platform","Day Knight & Associates, a Missouri agency founded in 2001 that collects healthcare and consumer debt, moved from outbound and inbound voice AI to a combined voice and SMS setup. The vendor reports that adding SMS reached consumers that voice alone missed and that, within a month of going multichannel, the cost of collecting a dollar fell from 22 cents to 8 cents; the agency's vice president of business development says collections doubled. It shows how channel choice, not only automation, drives results in collections.","production",2026,[33,34],[238],"en",[240,248],{"kpi":52,"value":241,"unit":242,"qualifier":243,"period":244,"claimant":245,"quote":246,"sourceUrl":247},63,"percent","exact","cost of collections, after moving to voice plus SMS","vendor","See how Day Knight & Associates used AI for Debt Collections to double recoveries, reduce collection costs by 63%, and scale multichannel outreach with Voice AI and SMS.","https://skit.ai/resource/case-studies/22-cents-to-8-cents-how-skit-ai-cut-day-knights-cost-of-collections-by-63-and-doubled-recovery/",{"kpi":51,"value":249,"unit":242,"qualifier":250,"period":251,"claimant":252,"quote":253,"sourceUrl":247},100,"approximately","collections after moving to voice plus SMS","organization","After adopting Skit.ai’s multichannel platform, we were able to double our collections and connectivity rate.",true,[256],{"url":247,"title":257,"publisher":231,"date":258},"22¢ to 8¢, 2X recoveries: Day Knight & Associates Scales Smarter with Skit AI for Debt Collections","2026-04-07",{"level":260,"checkedAt":261},"source-verified","2026-09-26","C","day-knight-associates-multichannel-collections",null,{"title":266,"useCases":267,"organization":268,"vendors":270,"summary":272,"stage":234,"year":235,"channels":273,"languages":274,"metrics":275,"outcomeDisclosed":254,"sources":284,"verification":287,"grade":262,"id":288,"organizationSlug":264},"SameDay Auto Finance: voice AI for early stage collections",[220],{"name":269,"anonymized":227,"country":228,"region":171,"industry":18},"SameDay Auto Finance",[271],{"name":231,"role":232},"SameDay Auto Finance, a Dallas auto lender whose portfolio sits mostly in early delinquency, moved its early stage outreach to AI voice agents calling around the clock, with SMS for customers who do not answer calls, and redeployed its human agents to inbound returns, skip tracing and complex accounts. The rollout ran in four phases over a year. The vendor reports 43% higher collections and 75% lower collection call costs in the early delinquency buckets.",[33,34],[238],[276,281],{"kpi":51,"value":277,"unit":242,"qualifier":243,"period":278,"claimant":245,"quote":279,"sourceUrl":280},43,"early stage delinquency","SameDay Auto Finance Achieves 43% Higher Collections and 75% Lower Call Costs in Early-DPD using AI for Collections.","https://skit.ai/resource/case-studies/from-legacy-tech-to-2x-ptp-inone-year/",{"kpi":52,"value":282,"unit":242,"qualifier":243,"period":283,"claimant":245,"quote":279,"sourceUrl":280},75,"collection call cost, early stage delinquency",[285],{"url":280,"title":286,"publisher":231,"date":258},"SameDay Auto Finance Achieves 43% Higher Collections and 75% Lower Call Costs",{"level":260,"checkedAt":261},"sameday-auto-finance-voice-collections",0,[291,299],{"kpi":52,"label":292,"unit":242,"aggregate":254,"higherIsBetter":254,"n":293,"nUpTo":289,"median":294,"min":241,"max":282,"byClaimant":295,"vendorOnly":254,"points":296},"Cost reduction",2,69,{"organization":289,"vendor":293,"regulator":289,"independent":289},[297,298],{"evidenceId":288,"organization":269,"value":282,"qualifier":243,"claimant":245,"grade":262,"pooled":254},{"evidenceId":263,"organization":226,"value":241,"qualifier":243,"claimant":245,"grade":262,"pooled":254},{"kpi":51,"label":300,"unit":242,"aggregate":254,"higherIsBetter":254,"n":293,"nUpTo":289,"median":301,"min":277,"max":249,"byClaimant":302,"vendorOnly":227,"points":304},"Recovery uplift",71.5,{"organization":303,"vendor":303,"regulator":289,"independent":289},1,[305,306],{"evidenceId":263,"organization":226,"value":249,"qualifier":250,"claimant":252,"grade":262,"pooled":254},{"evidenceId":288,"organization":269,"value":277,"qualifier":243,"claimant":245,"grade":262,"pooled":254},{"low":308,"high":309},240000,1680000,[311,329,358,374,388,398],{"slug":210,"title":312,"shortTitle":313,"definition":314,"status":9,"industries":315,"functions":316,"patterns":319,"audience":323,"autonomy":324,"adoptionStage":325,"segment":41,"evidenceCount":293,"publicEvidenceCount":303,"organizations":326,"bestGrade":328,"headline":264,"lastVerified":216,"indexable":254},"AI recommendations for loan restructuring and hardship arrangements","Restructuring recommendations","An assistant that assembles a stressed borrower's position, tests restructuring options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, and recommends the best fit with a written rationale for a person to approve.",[18],[25,317,318],"lending-and-credit","risk-management",[30,320,321,322],"rag-knowledge-assistant","document-processing","recommendation-and-personalization","employee-facing","copilot","emerging",[327],"Commonwealth Bank of Australia","B",{"slug":211,"title":330,"shortTitle":331,"definition":332,"status":9,"industries":333,"functions":338,"patterns":342,"audience":323,"autonomy":324,"adoptionStage":40,"segment":345,"evidenceCount":346,"publicEvidenceCount":346,"organizations":347,"bestGrade":328,"headline":352,"lastVerified":261,"indexable":254},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[17,18,334,335,336,337],"insurance","government","healthcare","wealth-and-asset-management",[339,26,25,340,341],"operations","regulatory-compliance","claims",[343,320,344],"content-generation","translation","back-office",5,[348,349,350,351],"Acentra Health","Hiscox","Health Resources and Services Administration","SS&C Technologies",{"kpi":353,"label":354,"unit":242,"n":303,"nUpTo":289,"kind":355,"value":356,"qualifier":243,"claimant":245,"organization":357,"vendorReported":254},"processing-time-reduction","Cycle time reduction","reported",25,"SS&C GIDS and RS",{"slug":212,"title":359,"shortTitle":360,"definition":361,"status":9,"industries":362,"functions":363,"patterns":365,"audience":38,"autonomy":39,"adoptionStage":40,"segment":367,"evidenceCount":69,"publicEvidenceCount":83,"organizations":368,"bestGrade":328,"headline":264,"lastVerified":216,"indexable":254},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","An in app AI assistant that the customer opens to understand their own money: it uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[18],[26,364],"marketing",[29,322,366,30],"prediction-and-scoring","front-office",[369,327,370,371,372,373],"Bank of America","Hyundai Card","Royal Bank of Canada","Starling Bank","Westpac",{"slug":213,"title":375,"shortTitle":376,"definition":377,"status":9,"industries":378,"functions":379,"patterns":380,"audience":323,"autonomy":383,"adoptionStage":40,"segment":41,"evidenceCount":82,"publicEvidenceCount":82,"organizations":384,"bestGrade":262,"headline":264,"lastVerified":216,"indexable":254},"AI early warning and covenant monitoring for loan portfolios","Credit early warning and covenants","A monitoring system that tracks covenant tests and borrower reporting across a loan book, reads financials, filings and news, and combines them with payment and sector signals to flag borrowers whose credit is deteriorating, with the evidence and a suggested next step for the relationship manager.",[18],[318,317],[381,321,30,382],"anomaly-detection","summarization","assist",[385,386,387],"OakNorth Bank","PNC Financial Services","Sumitomo Mitsui Banking Corporation",{"slug":214,"title":389,"shortTitle":390,"definition":391,"status":9,"industries":392,"functions":393,"patterns":395,"audience":38,"autonomy":39,"adoptionStage":325,"segment":367,"evidenceCount":82,"publicEvidenceCount":82,"organizations":396,"bestGrade":328,"headline":264,"lastVerified":216,"indexable":254},"AI agent for proactive customer outreach, activation and retention","Proactive outreach and activation","An AI agent that holds the conversation when a bank reaches out first to change something about the customer's account or products, triggered by an event or a campaign: low balance and fee avoidance alerts, payment and renewal reminders, card activation, dormant account reactivation and offers the customer already qualifies for, over messaging or voice, while the bank's own systems decide who is contacted and why. Reminders about appointments and deliveries the customer booked, and the in app coach the customer opens, are separate use cases.",[18,19],[364,394,26],"sales",[29,28,30,322],[369,397,327],"Capital One",{"slug":215,"title":399,"shortTitle":400,"definition":401,"status":9,"industries":402,"functions":403,"patterns":404,"audience":38,"autonomy":39,"adoptionStage":40,"evidenceCount":83,"publicEvidenceCount":346,"organizations":405,"bestGrade":328,"headline":411,"lastVerified":261,"indexable":254},"AI agent for utility billing, payments, meter readings and move in or move out","Utility billing and home moves","An AI agent for energy and water customers that explains bills and tariffs, takes meter readings, sets up or changes payments, and handles move in and move out (final reads, closing one account and opening the next), across phone, messaging, email and the app, while anyone in payment difficulty, in a vulnerable situation or with a complaint is handed to a person.",[21],[26,339],[29,28,30,320],[406,407,408,409,410],"Aydem Energy","Dubai Electricity and Water Authority","EDF","Octopus Energy","Pacific Gas and Electric Company",{"kpi":53,"label":412,"unit":242,"n":293,"nUpTo":289,"kind":355,"value":282,"qualifier":243,"claimant":252,"organization":406,"vendorReported":227},"Containment rate",{"indexable":254,"reasons":414},[],[416,420,425,433,440,446,453,458,465,471,477,483,490,497,503,508,515,521,527,532,538,542,548,553,558,565,570,575,580,587,593,599,605,610],{"id":155,"label":417,"issuer":182,"region":165,"url":183,"description":418,"useCases":419,"indexable":254},"EU AI Act","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":156,"label":421,"issuer":182,"region":165,"url":422,"description":423,"useCases":424,"indexable":254},"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":426,"label":427,"issuer":428,"region":429,"url":430,"description":431,"useCases":432,"indexable":254},"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":434,"label":435,"issuer":436,"region":171,"url":437,"description":438,"useCases":439,"indexable":254},"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":441,"label":442,"issuer":182,"region":165,"url":443,"description":444,"useCases":445,"indexable":254},"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":447,"label":448,"issuer":449,"region":165,"url":450,"description":451,"useCases":452,"indexable":254},"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":157,"label":454,"issuer":164,"region":165,"url":455,"description":456,"useCases":457,"indexable":254},"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":459,"label":460,"issuer":461,"region":177,"url":462,"description":463,"useCases":464,"indexable":254},"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":466,"label":467,"issuer":468,"region":177,"url":469,"description":470,"useCases":356,"indexable":254},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",{"id":158,"label":472,"issuer":473,"region":429,"url":474,"description":475,"useCases":476,"indexable":254},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":478,"label":479,"issuer":480,"region":171,"url":481,"description":482,"useCases":476,"indexable":254},"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":165,"url":487,"description":488,"useCases":489,"indexable":254},"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":429,"url":494,"description":495,"useCases":496,"indexable":254},"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":182,"region":165,"url":500,"description":501,"useCases":502,"indexable":254},"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":182,"region":165,"url":506,"description":507,"useCases":502,"indexable":254},"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":171,"url":512,"description":513,"useCases":514,"indexable":254},"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":182,"region":165,"url":518,"description":519,"useCases":520,"indexable":254},"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":171,"url":525,"description":526,"useCases":520,"indexable":254},"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":159,"label":528,"issuer":529,"region":429,"url":530,"description":531,"useCases":520,"indexable":254},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":533,"label":534,"issuer":182,"region":165,"url":535,"description":536,"useCases":537,"indexable":254},"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":160,"label":539,"issuer":187,"region":171,"url":540,"description":541,"useCases":537,"indexable":254},"Telephone Consumer Protection Act","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":543,"label":544,"issuer":461,"region":177,"url":545,"description":546,"useCases":547,"indexable":254},"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":549,"label":550,"issuer":182,"region":165,"url":551,"description":552,"useCases":547,"indexable":254},"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":554,"label":555,"issuer":182,"region":165,"url":556,"description":557,"useCases":547,"indexable":254},"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":559,"label":560,"issuer":561,"region":165,"url":562,"description":563,"useCases":564,"indexable":254},"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":566,"label":567,"issuer":170,"region":171,"url":568,"description":569,"useCases":69,"indexable":254},"us-ecoa-reg-b","ECOA and Regulation B","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":571,"label":572,"issuer":182,"region":165,"url":573,"description":574,"useCases":69,"indexable":254},"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":576,"label":577,"issuer":182,"region":165,"url":578,"description":579,"useCases":83,"indexable":254},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":581,"label":582,"issuer":583,"region":584,"url":585,"description":586,"useCases":346,"indexable":254},"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":588,"label":589,"issuer":590,"region":165,"url":591,"description":592,"useCases":68,"indexable":254},"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":594,"label":595,"issuer":596,"region":165,"url":597,"description":598,"useCases":68,"indexable":254},"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":600,"label":601,"issuer":602,"region":177,"url":603,"description":604,"useCases":82,"indexable":254},"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.",{"id":606,"label":607,"issuer":182,"region":165,"url":608,"description":609,"useCases":82,"indexable":254},"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":611,"label":612,"issuer":613,"region":171,"url":614,"description":615,"useCases":82,"indexable":254},"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.",1790598294729]