[{"data":1,"prerenderedAt":632},["ShallowReactive",2],{"uc-mortgage-servicing-borrower-assistant":3,"uc-regulations":401},{"useCase":4,"evidence":192,"blitsAiDeployments":288,"benchmarks":289,"indicative":305,"related":308,"indexability":399,"includeUnpublished":198},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":18,"functions":21,"patterns":25,"channels":29,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":44,"indicativeValue":50,"macroEstimates":91,"feasibility":92,"implementation":104,"risk":142,"blitsAi":170,"faq":172,"related":182,"datePublished":187,"dateModified":187,"lastVerified":187,"changelog":188,"slug":191},"AI assistant for mortgage servicing borrower requests","Mortgage servicing assistant","AI assistant for mortgage servicing borrowers","AI agents answer mortgage borrowers on payments, escrow and tax forms and route hardship to people. Rocket says AI Voice took over 1 million calls in three months.","published","A mortgage servicing borrower assistant is a voice or chat agent that answers a homeowner's questions about an existing mortgage and handles routine requests, for mortgage servicers and banks. It covers topics such as payments, autopay, escrow, tax forms, loan transfers and general forbearance information, can place routine outbound servicing calls, and passes hardship, disputes and other sensitive cases to a servicing specialist with the conversation attached.",[12,13,14,15,16,17],"mortgage servicing chatbot","borrower self service bot","AI voice agent for loan servicing","mortgage servicing virtual assistant","loan servicing chatbot","escrow chatbot",[19,20],"banking","real-estate",[22,23,24],"customer-service","operations","lending-and-credit",[26,27,28],"conversational-agent","voice-agent","rag-knowledge-assistant",[30,31,32],"voice","web-chat","sms","customer-facing","supervised-agent","early-adopters","lending","After closing, a mortgage becomes a long relationship made of small requests: what is my payoff\namount, why did my escrow payment change, how do I enroll in autopay, where is my year end tax\nform, who services my loan now. A servicer receives these questions by phone and live chat,\nmostly in business hours, and every one needs a person who has the loan record open.\n\nVolume is uneven. A regulatory event, a tax season or a servicing transfer can push calls up in\ndays, and the questions repeat. The calls that need judgment (a borrower in hardship, an\ninsurance premium the borrower cannot carry, a dispute) wait in the same queue as the routine\nones.",[],"1. **Identify the borrower.** The assistant verifies the caller or chat user with the same\n   checks an agent would use, and opens the loan record through the servicing platform.\n2. **Understand the request.** A language model classifies the request (payment, autopay,\n   escrow, tax form, transfer, forbearance information) and, where the answer depends on the loan, reads the\n   data from the servicing system.\n3. **Answer or act.** For information it explains the answer in plain language. For routine\n   actions it completes the step inside the limits the servicer has set, for example autopay\n   enrollment or sending a document. It can also place routine outbound calls the servicer\n   schedules, such as occupancy or repair confirmations on a reverse mortgage, and log the\n   outcome.\n4. **Hand over.** Hardship, disputes, complaints and any case the assistant cannot resolve go to\n   a servicing specialist, with the conversation and the borrower's context attached. Borrowers\n   can ask for a person at any point.\n5. **Review.** Servicing leads sample conversations, monitor missed intents and update the\n   knowledge as regulation and investor rules change.",[41,42,43],"cost-to-serve","customer-experience","speed",[45,46,47,48,49],"containment-rate","contact-deflection","interactions-handled","customer-satisfaction","handling-time-reduction",{"referenceOrg":51,"inputs":52,"formula":86,"currency":87,"period":88,"resultLabel":89,"caveat":90},"A mortgage servicer with 500,000 loans in its portfolio",[53,58,65,72,79],{"key":54,"label":55,"low":56,"high":56,"unit":54,"note":57},"loans","Loans serviced",500000,"The reference servicer.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"contactsPerLoan","Assisted contacts per loan per year",1,2,"contacts per loan per year","Editorial assumption, replace with your own call and chat volume.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"routineShare","Share of contacts that are routine servicing",0.5,0.7,"fraction of contacts","Editorial assumption, replace with the share from your contact reason report.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"containment","Share of routine contacts the agent resolves",0.15,0.4,"fraction of routine contacts","Conservative against the evidence on this page. ServisBOT reports 75% containment for Cenlar FSB's chat bot during office hours, a vendor figure for chat only. This range also covers phone calls and a wider request mix.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"costPerContact","Cost of a human handled contact",5,8,"USD per contact","Editorial assumption for a blended phone and chat servicing contact, replace with your fully loaded cost.","loans * contactsPerLoan * routineShare * containment * costPerContact","USD","per year","Human handled servicing contact cost avoided","Gross avoided contact cost only. It leaves out the cost of running the AI, integration and compliance review, the value of 24\u002F7 coverage and any effect on complaints or retention.",[],{"complexity":93,"complexityNote":94,"dataPrerequisites":95,"integrations":99},"medium","The build needs a read and write connection to the servicing platform, caller verification that matches the contact center's current checks, a knowledge base of servicing policies and regulatory notices, and a handover path into the agent desktop. Actions that change a loan record need approval rules from compliance before they go live.",[96,97,98],"Current servicing policies, investor guidelines and borrower facing regulatory notices","Historical call and chat transcripts, labeled by request type and outcome","Loan level data exposed through the servicing platform, with borrower verification",[100,101,102,103],"Mortgage servicing platform (loan, escrow, payment and document data)","Telephony or IVR and live chat platform, with handover to the agent desktop","Identity verification and consent records","Quality monitoring and call recording",{"steps":105,"guardrails":121,"humanInTheLoop":126,"kpisToInstrument":127,"failureModes":132},[106,109,112,115,118],{"title":107,"detail":108},"Start with information only requests","Launch on questions that need no change to the loan record, such as payment status, escrow explanations and tax form requests. Measure containment and accuracy before any action is allowed.",{"title":110,"detail":111},"Add routine actions one at a time","Enable autopay enrollment, document delivery and similar steps individually, each with its own limits and a compliance sign off. Keep an audit record of every action.",{"title":113,"detail":114},"Build the handover before the assistant","Agree with the servicing team which topics always go to a person (hardship, disputes, complaints, bereavement) and make the handover carry the full context.",{"title":116,"detail":117},"Offer an opt out at every step","Tell the borrower they are speaking with an AI agent and let them choose a human at any point, in line with the servicer's disclosure duties.",{"title":119,"detail":120},"Review misses weekly","Read missed intents and escalations each week, add them to the knowledge base or to the always escalate list, and rerun the test set before each change goes live.",[122,123,124,125],"The assistant discloses that it is an AI agent and offers a human at any point","Hardship, loss mitigation, dispute and complaint topics always route to a person","Answers about balances, payments and escrow come from the servicing system, not from model memory","Borrower data is verified before any loan specific detail is shared, and personal data is masked in logs","Servicing specialists take every escalated case and own all hardship and dispute decisions. A quality team samples completed conversations each week and checks them against the loan record, and any change to the allowed actions needs compliance approval.",[128,129,130,131],"Share of conversations resolved without a person, by request type","Share of escalations that the borrower asked for versus the assistant triggered","Accuracy of answers checked against the servicing record on a weekly sample","Borrower satisfaction after the interaction",[133,136,139],{"title":134,"detail":135},"Wrong loan specific answer","An escrow or payoff figure given from stale data or inferred by the model creates a complaint and possible regulatory exposure. Read figures only from the servicing system and sample answers against it.",{"title":137,"detail":138},"Borrower in distress kept in the bot","A borrower in hardship who cannot find the route to a person is the \"doom loop\" the CFPB warns about in its chatbot report. Detect distress words and hardship intents early and route them straight to a specialist.",{"title":140,"detail":141},"Authentication gaps","Weak verification on voice lets a caller reach another borrower's data. Use the same or stronger checks than the human desk and log every verification outcome.",{"euAiAct":143,"regulations":146,"guidance":151,"controls":164,"incidents":169},{"tier":144,"basis":145},"limited","A customer facing assistant that talks to borrowers is subject to the Article 50 transparency duty to tell users they are interacting with an AI system. It does not evaluate creditworthiness. If a deployment later influences loss mitigation or credit decisions for natural persons, reassess it against Annex III point 5(b).",[147,148,149,150],"eu-ai-act","gdpr","us-tcpa","eu-mortgage-credit-directive",[152,158],{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Article 50, transparency obligations for providers and deployers of certain AI systems","European Union","europe","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":159,"issuer":160,"region":161,"url":162,"note":163},"Chatbots in consumer finance","Consumer Financial Protection Bureau","north-america","https:\u002F\u002Fwww.consumerfinance.gov\u002Fdata-research\u002Fresearch-reports\u002Fchatbots-in-consumer-finance\u002F","Warns that chatbots which cannot resolve a problem and block access to a human (\"doom loops\") can harm customers and lead to violations of consumer financial law.",[165,166,167,168],"Disclosure that the borrower is talking to an AI agent, with a human option at every step","Compliance approval for each action the assistant may take on a loan record","Audit trail of identity checks, answers and actions","Recorded and reviewable conversations with personal data masked in analytics",[],{"howToBuild":171},"On Blits.ai this is a voice and chat **agent** grounded in a **knowledge base** with hybrid\nretrieval over the servicer's policies and notices. **Custom functions** read loan, escrow and\npayment data from the servicing platform through REST calls, so figures come from the system\nof record. **Flows** hold the verification step and the always escalate topics, and **human\nhandover** passes the conversation to the agent desktop with context.\n\n**Guardrails** and **PII masking** protect borrower data in messages and logs, **test suites**\nreplay a set of real request types before each release, and **monitors** and **analytics**\ntrack containment and missed intents. The platform is model agnostic and supports EU, Qatar or UAE\ndata residency, a private cloud or on premises. Actions on the loan record, such as autopay\nenrollment, can run as agentic workflows with a human approval step.",[173,176,179],{"question":174,"answer":175},"What share of servicing calls can an AI assistant resolve?","It depends on the request mix, and few servicers publish a resolution rate. ServisBOT reports a 75% containment rate during office hours for Cenlar FSB's borrower bot, a vendor figure for chat. Rocket Companies reports volume rather than a resolution rate: its AI Voice handled more than 1 million inbound servicing calls within three months of launch.",{"question":177,"answer":178},"Which servicing topics should never be automated?","Hardship and loss mitigation, disputes, complaints and anything where a borrower is in distress should go to a person. Longbridge Financial's chief operating officer, for example, says it wants its human agents to talk with borrowers on fixed incomes who are worried about rising homeowners insurance costs.",{"question":180,"answer":181},"Must the borrower be told it is an AI?","Under Article 50 of the EU AI Act users must be told when they interact with an AI system unless it is obvious. In the United States the CFPB has warned that chatbots which leave customers without a route to a person can harm them and risk violations of consumer financial law. Longbridge Financial lets borrowers opt out of the AI interaction and speak with a person.",[183,184,185,186],"collections-and-hardship-agent","home-loan-assistant-and-prequalification","account-and-card-servicing-agent","loan-restructuring-recommendations","2026-11-11",[189],{"date":187,"note":190},"First published","mortgage-servicing-borrower-assistant",[193,223,249],{"title":194,"useCases":195,"organization":196,"vendors":200,"summary":201,"stage":202,"year":203,"channels":204,"languages":205,"metrics":207,"outcomeDisclosed":198,"sources":208,"verification":218,"grade":220,"id":221,"organizationSlug":222},"Longbridge Financial: AI voice agents for reverse mortgage servicing",[191],{"name":197,"anonymized":198,"country":199,"region":161,"industry":19},"Longbridge Financial",false,"US",[],"Longbridge Financial, a US reverse mortgage lender and servicer, put AI voice agents live in January 2026 and announced them on 3 February 2026. They handle routine servicing calls, such as outbound checks that a borrower still lives in the home and that required repairs are done, and transfer callers to a servicing professional when needed. The agents give 24\u002F7 coverage, borrowers can opt out and reach a person, and the company says staffing levels are unchanged. Its chief operating officer says it will take a few months of data to know how much time the agents save, so no outcome is published yet.","production",2026,[30],[206],"en",[],[209,214],{"url":210,"title":211,"publisher":212,"date":213},"https:\u002F\u002Fwww.businesswire.com\u002Fnews\u002Fhome\u002F20260203562556\u002Fen\u002FLongbridge-Financial-Deploys-One-of-the-First-AI-Powered-Voice-Agents-for-Reverse-Mortgage-Servicing","Longbridge Financial Deploys One of the First AI Powered Voice Agents for Reverse Mortgage Servicing","Longbridge Financial (Business Wire)","2026-02-03",{"url":215,"title":216,"publisher":217,"date":213},"https:\u002F\u002Fwww.housingwire.com\u002Farticles\u002Flongbridge-ai-reverse-mortgage\u002F","Longbridge launches AI voice agents for reverse mortgage servicing","HousingWire",{"level":219,"checkedAt":187},"source-verified","B","longbridge-reverse-mortgage-ai-voice-agents",null,{"title":224,"useCases":225,"organization":226,"vendors":228,"summary":229,"stage":230,"year":203,"channels":231,"languages":232,"metrics":233,"outcomeDisclosed":242,"sources":243,"verification":247,"grade":220,"id":248,"organizationSlug":222},"Rocket Companies: AI Voice for inbound mortgage servicing calls",[191],{"name":227,"anonymized":198,"country":199,"region":161,"industry":19},"Rocket Companies",[],"Rocket Companies scaled an AI Voice platform across its mortgage servicing operations in the second quarter of 2026. In its earnings release the company says the system handled more than 1 million inbound calls within three months of launch, that more than half of those calls would otherwise have needed a servicing team member, that task resolution was nearly 25% faster than its old IVR and that client satisfaction reached 4.5 out of 5.","scaled",[30],[206],[234],{"kpi":47,"value":235,"unit":236,"qualifier":237,"period":238,"claimant":239,"quote":240,"sourceUrl":241},1000000,"count","at-least","inbound calls within three months of launch","organization","Within three months of launch, AI Voice handled more than 1 million inbound calls.","https:\u002F\u002Fs205.q4cdn.com\u002F636124780\u002Ffiles\u002Fdoc_financials\u002F2026\u002Fq2\u002FRKT-Earnings-Release-06-30-2026.pdf",true,[244],{"url":241,"title":245,"publisher":227,"date":246},"Rocket Companies Announces Second Quarter 2026 Results","2026-08-06",{"level":219,"checkedAt":187},"rocket-ai-voice-servicing-calls",{"title":250,"useCases":251,"organization":252,"vendors":254,"summary":258,"stage":202,"year":259,"channels":260,"languages":261,"metrics":262,"outcomeDisclosed":242,"sources":277,"verification":285,"grade":286,"id":287,"organizationSlug":222},"Cenlar FSB: borrower bot for mortgage servicing questions",[191],{"name":253,"anonymized":198,"country":199,"region":161,"industry":19},"Cenlar FSB",[255],{"name":256,"role":257},"ServisBOT","platform","Cenlar FSB, a US loan subservicer, deployed a ServisBOT bot in 2020 to answer borrowers' CARES Act forbearance questions across IVR, SMS and web, built in about 30 days, then extended it with a borrower bot in front of live chat for payments, autopay, loan transfers, tax forms and escrow. Cenlar's own executive says 10 to 12% of hardship callers chose the bot over an agent. ServisBOT reports a 75% containment rate during office hours for the chat bot. Cenlar's own site now describes newer generative AI bots for homeowners, including an authenticated escrow bot.",2021,[30,32,31],[206],[263,270],{"kpi":46,"value":264,"unit":265,"qualifier":266,"period":267,"claimant":239,"quote":268,"sourceUrl":269},12,"percent","up-to","borrowers calling about a hardship who opted in to the bot instead of an agent","Once the bot was turned on and offered in our IVR, 10-12% of borrowers who indicated they were calling about a hardship, opted in to an SMS with a link to our website instead of talking to an agent.","https:\u002F\u002Fservisbot.com\u002Fcustomer-stories\u002Fcenlar-fsb\u002F",{"kpi":45,"value":271,"unit":265,"qualifier":272,"period":273,"claimant":274,"quote":275,"sourceUrl":276},75,"exact","during office hours, borrower bot in front of live chat","vendor","During office hours, Cenlar is experiencing a 75% containment rate with only one out of four requests passed to the live agents.","https:\u002F\u002Fservisbot.com\u002Fincrease-containment-rate-case-study\u002F",[278,280,282],{"url":269,"title":279,"publisher":256},"Self Service Chatbot Enables 12% of Borrowers to Resolve Queries",{"url":276,"title":281,"publisher":256},"Achieving 75% Containment Rate using an AI Bot, Case Study",{"url":283,"title":284,"publisher":253},"https:\u002F\u002Fwww.cenlar.com\u002Fclients\u002Ftransforming-the-homeowner-and-customer-service-experience","Transforming the Homeowner and Customer Service Experience",{"level":219,"checkedAt":187},"C","cenlar-servisbot-borrower-bot",0,[290,295,300],{"kpi":45,"label":291,"unit":265,"aggregate":242,"higherIsBetter":242,"n":61,"nUpTo":288,"median":271,"min":271,"max":271,"byClaimant":292,"vendorOnly":242,"points":293},"Containment rate",{"organization":288,"vendor":61,"regulator":288,"independent":288},[294],{"evidenceId":287,"organization":253,"value":271,"qualifier":272,"claimant":274,"grade":286,"pooled":242},{"kpi":47,"label":296,"unit":236,"aggregate":198,"higherIsBetter":242,"n":61,"nUpTo":288,"median":235,"min":235,"max":235,"byClaimant":297,"vendorOnly":198,"points":298},"Interactions handled",{"organization":61,"vendor":288,"regulator":288,"independent":288},[299],{"evidenceId":248,"organization":227,"value":235,"qualifier":237,"claimant":239,"grade":220,"pooled":242},{"kpi":46,"label":301,"unit":265,"aggregate":242,"higherIsBetter":242,"n":288,"nUpTo":61,"median":222,"min":222,"max":222,"byClaimant":302,"vendorOnly":198,"points":303},"Contact deflection",{"organization":288,"vendor":288,"regulator":288,"independent":288},[304],{"evidenceId":287,"organization":253,"value":264,"qualifier":266,"claimant":239,"grade":286,"pooled":198},{"low":306,"high":307},187500,2240000,[309,337,354,385],{"slug":183,"title":310,"shortTitle":311,"definition":312,"status":9,"industries":313,"functions":320,"patterns":322,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"evidenceCount":325,"publicEvidenceCount":82,"organizations":326,"bestGrade":220,"headline":332,"lastVerified":336,"indexable":242},"AI agent for early collections and hardship support","Collections and hardship agent","A voice and messaging agent that contacts customers in early arrears to collect payments. It answers their inbound calls too, takes payments and sets up payment arrangements within preapproved rules, and recognizes signs of hardship or vulnerability so those customers go straight to a trained person.",[314,19,315,316,317,318,319],"cross-industry","payments","telecommunications","energy-and-utilities","automotive","professional-services",[321,22],"collections-and-recovery",[27,26,323,324],"agentic-workflow","classification-and-routing",7,[327,328,329,330,331],"Day Knight & Associates","mBank","OTP Bank Romania","SameDay Auto Finance","Southwest Recovery Services",{"kpi":333,"label":334,"unit":265,"n":62,"nUpTo":288,"kind":335,"value":271,"qualifier":272,"claimant":274,"organization":330,"vendorReported":242},"cost-reduction","Cost reduction","reported","2026-09-27",{"slug":184,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":342,"patterns":344,"audience":33,"autonomy":34,"adoptionStage":35,"segment":345,"evidenceCount":325,"publicEvidenceCount":325,"organizations":346,"bestGrade":220,"headline":222,"lastVerified":336,"indexable":242},"AI home loan assistant with pre qualification","Home loan assistant","An AI home loan assistant with pre qualification answers a customer's home loan questions and runs indicative borrowing estimates, then books the customer with a mortgage specialist. It covers rates, loan to value, fees and the documents needed, runs the affordability estimate from the bank's published rules, and stays grounded in the bank's current, versioned product and policy documents.",[19,20],[24,343,22],"sales",[28,26,27],"front-office",[347,348,349,350,351,352,353],"Better.com","Figure","InstaMortgage","Loft","NatWest Group","Newrez","Safe Rate",{"slug":185,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":359,"patterns":360,"audience":33,"autonomy":34,"adoptionStage":361,"segment":345,"evidenceCount":362,"publicEvidenceCount":363,"organizations":364,"bestGrade":220,"headline":381,"lastVerified":336,"indexable":242},"AI agent for account and card servicing","Account and card servicing","An AI account and card servicing agent is an AI agent that resolves routine banking account and card requests for customers, end to end. It handles things like balances, statements, card blocks and replacements, PIN resets and limit changes, across app, web, messaging and phone, and hands anything sensitive or unusual to a human with the full context.",[19,315],[22,23],[26,27,323,28],"mainstream",19,17,[365,366,367,368,369,370,371,372,373,374,375,376,351,377,378,379,380],"Abu Dhabi Commercial Bank (ADCB)","Abu Dhabi Islamic Bank (ADIB)","Arab Bank","BPCE Group","Commonwealth Bank of Australia","DBS Bank","Emirates Islamic","Emirates NBD","Equity Bank Kenya","Garanti BBVA","KB Kookmin Card","MONETA Money Bank","Simple (Attijariwafa bank)","Trust Bank","Vision Bank","Zenith Bank",{"kpi":45,"label":291,"unit":265,"n":382,"nUpTo":288,"kind":383,"value":384,"qualifier":272,"claimant":222,"organization":222,"vendorReported":198},4,"median",84.8,{"slug":186,"title":386,"shortTitle":387,"definition":388,"status":9,"industries":389,"functions":390,"patterns":392,"audience":395,"autonomy":396,"adoptionStage":397,"segment":36,"evidenceCount":62,"publicEvidenceCount":61,"organizations":398,"bestGrade":220,"headline":222,"lastVerified":336,"indexable":242},"AI recommendations for loan restructuring and hardship arrangements","Restructuring recommendations","AI loan restructuring recommendations are an assistant that proposes the best hardship arrangement for a stressed borrower, for a person to approve. It assembles the borrower's position and tests options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, then recommends the best fit with a written rationale.",[19],[321,24,391],"risk-management",[323,28,393,394],"document-processing","recommendation-and-personalization","employee-facing","copilot","emerging",[369],{"indexable":242,"reasons":400},[],[402,406,411,419,426,433,439,446,454,461,467,473,479,486,493,499,505,511,517,524,529,534,540,545,552,557,562,568,574,579,587,591,597,603,610,615,620,626],{"id":147,"label":403,"issuer":154,"region":155,"url":156,"description":404,"useCases":405,"indexable":242},"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.",298,{"id":148,"label":407,"issuer":154,"region":155,"url":408,"description":409,"useCases":410,"indexable":242},"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.",268,{"id":412,"label":413,"issuer":414,"region":415,"url":416,"description":417,"useCases":418,"indexable":242},"iso-42001","ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",149,{"id":420,"label":421,"issuer":422,"region":161,"url":423,"description":424,"useCases":425,"indexable":242},"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.",115,{"id":427,"label":428,"issuer":429,"region":155,"url":430,"description":431,"useCases":432,"indexable":242},"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.",85,{"id":434,"label":435,"issuer":154,"region":155,"url":436,"description":437,"useCases":438,"indexable":242},"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.",73,{"id":440,"label":441,"issuer":442,"region":155,"url":443,"description":444,"useCases":445,"indexable":242},"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.",53,{"id":447,"label":448,"issuer":449,"region":450,"url":451,"description":452,"useCases":453,"indexable":242},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","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.",37,{"id":455,"label":456,"issuer":457,"region":450,"url":458,"description":459,"useCases":460,"indexable":242},"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":462,"label":463,"issuer":154,"region":155,"url":464,"description":465,"useCases":466,"indexable":242},"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":468,"label":469,"issuer":470,"region":415,"url":471,"description":472,"useCases":466,"indexable":242},"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":474,"label":475,"issuer":476,"region":161,"url":477,"description":478,"useCases":466,"indexable":242},"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":480,"label":481,"issuer":482,"region":161,"url":483,"description":484,"useCases":485,"indexable":242},"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":487,"label":488,"issuer":489,"region":155,"url":490,"description":491,"useCases":492,"indexable":242},"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":149,"label":494,"issuer":495,"region":161,"url":496,"description":497,"useCases":498,"indexable":242},"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.",16,{"id":500,"label":501,"issuer":154,"region":155,"url":502,"description":503,"useCases":504,"indexable":242},"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.",15,{"id":506,"label":507,"issuer":508,"region":415,"url":509,"description":510,"useCases":504,"indexable":242},"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":512,"label":513,"issuer":154,"region":155,"url":514,"description":515,"useCases":516,"indexable":242},"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.",14,{"id":518,"label":519,"issuer":520,"region":161,"url":521,"description":522,"useCases":523,"indexable":242},"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.",13,{"id":525,"label":526,"issuer":154,"region":155,"url":527,"description":528,"useCases":523,"indexable":242},"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":530,"label":531,"issuer":154,"region":155,"url":532,"description":533,"useCases":523,"indexable":242},"solvency-ii","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":535,"label":536,"issuer":537,"region":415,"url":538,"description":539,"useCases":523,"indexable":242},"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":541,"label":542,"issuer":160,"region":161,"url":543,"description":544,"useCases":264,"indexable":242},"us-ecoa-reg-b","ECOA and Regulation B","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.",{"id":546,"label":547,"issuer":548,"region":155,"url":549,"description":550,"useCases":551,"indexable":242},"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.",11,{"id":553,"label":554,"issuer":154,"region":155,"url":555,"description":556,"useCases":551,"indexable":242},"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":558,"label":559,"issuer":154,"region":155,"url":560,"description":561,"useCases":551,"indexable":242},"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.",{"id":563,"label":564,"issuer":449,"region":450,"url":565,"description":566,"useCases":567,"indexable":242},"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.",10,{"id":569,"label":570,"issuer":571,"region":161,"url":572,"description":573,"useCases":83,"indexable":242},"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.",{"id":575,"label":576,"issuer":154,"region":155,"url":577,"description":578,"useCases":83,"indexable":242},"eu-idd","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":580,"label":581,"issuer":582,"region":583,"url":584,"description":585,"useCases":586,"indexable":242},"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.",6,{"id":150,"label":588,"issuer":154,"region":155,"url":589,"description":590,"useCases":82,"indexable":242},"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.",{"id":592,"label":593,"issuer":594,"region":155,"url":595,"description":596,"useCases":82,"indexable":242},"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":598,"label":599,"issuer":600,"region":155,"url":601,"description":602,"useCases":382,"indexable":242},"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":604,"label":605,"issuer":606,"region":450,"url":607,"description":608,"useCases":609,"indexable":242},"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.",3,{"id":611,"label":612,"issuer":154,"region":155,"url":613,"description":614,"useCases":609,"indexable":242},"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.",{"id":616,"label":617,"issuer":449,"region":450,"url":618,"description":619,"useCases":609,"indexable":242},"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":621,"label":622,"issuer":623,"region":161,"url":624,"description":625,"useCases":609,"indexable":242},"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.",{"id":627,"label":628,"issuer":629,"region":583,"url":630,"description":631,"useCases":609,"indexable":242},"sdaia-ai-ethics","SDAIA AI ethics principles","Saudi Data and AI Authority","https:\u002F\u002Fsdaia.gov.sa\u002Fen\u002FSDAIA\u002Fabout\u002FDocuments\u002Fai-principles.pdf","Saudi Arabia's national AI ethics principles, applied across sectors including finance and government.",1794393606322]