[{"data":1,"prerenderedAt":616},["ShallowReactive",2],{"uc-complaints-handling-agent":3,"uc-regulations":414},{"useCase":4,"evidence":204,"blitsAiDeployments":270,"benchmarks":271,"indicative":278,"related":281,"indexability":412,"includeUnpublished":210},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":21,"patterns":25,"channels":31,"audience":37,"autonomy":38,"adoptionStage":39,"segment":40,"problem":41,"problemStats":42,"howItWorks":43,"valueDrivers":44,"kpis":49,"indicativeValue":56,"macroEstimates":84,"feasibility":85,"implementation":99,"risk":142,"blitsAi":180,"faq":182,"related":192,"datePublished":199,"dateModified":199,"lastVerified":199,"changelog":200,"slug":203},"AI agent for complaints recognition, investigation and response","Complaints handling","AI agents for complaints handling and triage","AI agents classify and investigate complaints for a human handler. Lloyds Banking Group cut classification to 1 second; NatWest is testing one with the FCA.","published","An AI agent that recognizes when a customer interaction is a complaint, logs it against the regulatory definition, classifies its root cause and severity, gathers the evidence, drafts the acknowledgement and the response for a human handler to approve, and tracks every statutory deadline until the case is closed.",[12,13,14],"complaint management AI","complaint triage agent","dispute resolution assistant",[16,17,18,19,20],"cross-industry","banking","payments","insurance","telecommunications",[22,23,24],"case-management","customer-service","regulatory-compliance",[26,27,28,29,30],"classification-and-routing","summarization","content-generation","agentic-workflow","rag-knowledge-assistant",[32,33,34,35,36],"agent-desktop","internal-tools","email","web-chat","voice","employee-facing","copilot","early-adopters","middle-office","In regulated industries a complaint is not whatever the customer calls a complaint. Financial\nregulators define it broadly (any expression of dissatisfaction, about the firm's service or\nproducts), and a complaint made in a phone call or a chat counts as much as a letter. Firms must\nacknowledge quickly, resolve within set deadlines, explain the outcome in writing and report\nvolumes and root causes. Missing one is a breach of the rules, not just a service failure.\n\nMuch of the handler's work sits around the decision: reading the history across systems, pulling\nstatements and call notes, working out what went wrong, and writing a response that is accurate,\nfair and clear. Meanwhile complaints hidden in ordinary conversations are\nnever logged, so they are never fixed. Customers now also use generative AI to write complaints,\nwhich the [UK Financial Ombudsman Service](https://www.financial-ombudsman.org.uk/businesses/resolving-complaint/our-insight/embracing-ais-transformational-impact-consumer-complaints)\nsays can produce long, unfocused submissions with fabricated laws, misquoted regulations or\ninvented past decisions.",[],"1. **Recognize.** Every channel (calls, chats, emails, letters, social) is screened for\n   expressions of dissatisfaction against the regulatory definition, so a complaint inside an\n   ordinary call is flagged instead of lost.\n2. **Log and classify.** The agent opens the case, sets the product, root cause and severity,\n   flags vulnerability and possible systemic issues, and starts the deadline clock.\n3. **Investigate.** It gathers the evidence from the relevant systems (transactions, call notes,\n   previous contacts, policies in force at the time) and writes a summary of what happened.\n4. **Draft.** It drafts the acknowledgement and the final response from approved wording, with\n   the reasoning and the redress calculation shown separately for the handler.\n5. **Decide and send, by a human.** A complaint handler reviews the evidence, decides the\n   outcome and approves the letter. The agent tracks deadlines and feeds root causes to\n   the teams that can fix them.",[45,46,47,48],"compliance","employee-productivity","speed","customer-experience",[50,51,52,53,54,55],"processing-time-reduction","handling-time-reduction","time-saved-per-task","accuracy","hours-saved","cost-savings",{"referenceOrg":57,"inputs":58,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A retail bank that handles 50,000 complaints a year",[59,65,72],{"key":60,"label":61,"low":62,"high":62,"unit":63,"note":64},"complaints","Complaints handled per year",50000,"complaints per year","The reference bank.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"minutesSaved","Handler minutes saved per complaint on classification, evidence gathering and drafting",5,20,"minutes per complaint","The low end is the only reported figure on this page, which covers classification alone (Lloyds Banking Group reports complaint classification in 1 second instead of about 5 minutes). The high end adds evidence gathering and drafting, for which no deployment has disclosed a figure; editorial assumption, replace with your own.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"costPerHour","Fully loaded cost of a complaint handler hour",40,70,"USD per hour","Editorial assumption, replace with your own cost.","complaints * minutesSaved / 60 * costPerHour","USD","per year","Complaint handler time released","Handler time only. It leaves out lower redress and ombudsman fees from better first responses, fewer missed deadlines, the value of fixing root causes earlier and the cost of the AI and integrations.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":93},"medium","Complaint classification is live at Lloyds Banking Group and investigation summaries are in testing at NatWest. The difficulty is reaching the evidence across many systems, keeping drafts factually right, recognizing complaints in unstructured conversations and proving to the regulator that nothing is missed.",[89,90,91,92],"The regulatory complaint definition and internal taxonomy of products, causes and severities","Historical complaints with outcomes and final response letters","Approved response templates, clauses and redress rules","Access to call transcripts and chat logs for recognition",[94,95,96,97,98],"Complaint or case management system","Contact centre transcripts and chat platforms","Core banking, card and product systems for evidence","Document generation and correspondence","Management information and regulatory reporting",{"steps":100,"guardrails":116,"humanInTheLoop":122,"kpisToInstrument":123,"failureModes":129},[101,104,107,110,113],{"title":102,"detail":103},"Start with recognition and logging","Screen transcripts and messages for complaints the contact centre did not log, and measure how many are found. This reduces conduct risk before any drafting is automated.",{"title":105,"detail":106},"Summarise the evidence for handlers","Build the investigation summary next, with links to every source, and measure how often handlers agree with it. NatWest's pilot follows this pattern: the agent investigates across data sources and presents a summary to a handler for approval.",{"title":108,"detail":109},"Draft responses from approved wording","Generate drafts from templates and clauses, with the decision and any redress set by the handler, and track edit rates per section.",{"title":111,"detail":112},"Close the loop on root causes","Aggregate causes across cases weekly and route them to product and process owners; spot systemic issues that affect many customers early.",{"title":114,"detail":115},"Test with your regulator in mind","Keep an evaluation set of real cases scored for task accuracy and hallucination, tracked over time. NatWest runs its trial inside the FCA's AI Live Testing with daily tracking of such metrics.",[117,118,119,120,121],"Every outcome and every response letter is approved by a human complaint handler","The agent may escalate a case to a complaint but never downgrade a flagged complaint on its own","Drafts cite the evidence they rely on; unsupported statements are flagged for the handler","Deadlines are computed by rules and alerted, never estimated by the model","Vulnerability and systemic issue flags route to specialist teams","Complaint handlers decide every outcome and approve every letter. Quality assurance samples AI classifications and drafts each week, and a senior owner signs off the recognition rules and thresholds, because a missed complaint is a conduct failure.",[124,125,126,127,128],"Complaints recognized in conversations that were not logged manually","Time from receipt to acknowledgement and to final response","Handler agreement with classifications and edit rate on drafts","Deadline breaches and ombudsman referral and overturn rates","Root causes identified and fixed",[130,133,136,139],{"title":131,"detail":132},"Containment over recognition","A customer facing assistant tuned for containment answers a complaint as a question and never logs it. Screen every conversation against the complaint definition.",{"title":134,"detail":135},"Plausible but wrong responses","A draft misstates facts or policy and the handler, under time pressure, sends it. Show evidence next to every claim and track edit rates.",{"title":137,"detail":138},"Automated unfairness","Triage deprioritises complex or vulnerable cases. Test routing outcomes by customer group and keep humans on vulnerability.",{"title":140,"detail":141},"AI written complaints overwhelm triage","Long AI drafted submissions with invented legal references slow handling. Summarise the customer's actual points and check references before responding.",{"euAiAct":143,"regulations":146,"guidance":155,"controls":173,"incidents":179},{"tier":144,"basis":145},"context-dependent","Complaint handling is not listed in Annex III, so internal classification and drafting for a handler who decides is minimal risk. Where the agent talks to customers to take the complaint, Article 50(1) requires telling them they are dealing with AI. Only a system that also assessed creditworthiness or priced life and health insurance (Annex III point 5(b) or 5(c)) would be high risk for that part.",[147,148,149,150,151,152,153,154],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","eu-psd2","dora","apra-cps-230","telecom-consumer-rules",[156,162,168],{"title":157,"issuer":158,"region":159,"url":160,"note":161},"DISP 1: Treating complainants fairly","Financial Conduct Authority","europe","https://www.handbook.fca.org.uk/handbook/DISP/1/","The UK rules for complaint handling by financial firms, including prompt acknowledgement, the eight week time limit, final response requirements and complaint reporting.",{"title":163,"issuer":164,"region":165,"url":166,"note":167},"RG 271 Internal dispute resolution","Australian Securities and Investments Commission","asia-pacific","https://asic.gov.au/regulatory-resources/find-a-document/regulatory-guides/rg-271-internal-dispute-resolution/","ASIC's standards for internal dispute resolution by Australian financial firms, including what counts as a complaint and maximum response timeframes.",{"title":169,"issuer":170,"region":159,"url":171,"note":172},"Embracing AI's transformational impact on consumer complaints","Financial Ombudsman Service","https://www.financial-ombudsman.org.uk/businesses/resolving-complaint/our-insight/embracing-ais-transformational-impact-consumer-complaints","The UK ombudsman's view of how AI is changing complaints, including consumers' use of generative AI and firms' automated triage.",[174,175,176,177,178],"Recognition rules mapped to the regulatory complaint definition, owned by a senior manager","Human approval of every outcome and final response, recorded in the case","Audit trail of classifications, evidence used and draft changes","Regular accuracy and hallucination testing on real cases","Root cause and systemic issue reporting to governance forums",[],{"howToBuild":181},"On Blits.ai recognition runs on the conversations the platform already handles: an **AI\nagent** with **structured output** labels dissatisfaction in chat and voice, and **human\nhandover** routes the case to the complaints team with a summary of the conversation. Emails\nand letters can reach the same process through the email channel and the **REST API**.\n\nInvestigation and drafting run as an **agentic workflow** that gathers evidence through\n**custom functions** and **SQL knowledge bases**, retrieves approved wording from a\n**knowledge base**, and stops for **human in the loop** approval before anything is sent, with\na full audit trail per run. **PII masking** masks personal data in prompts, **test\nsuites** with LLM based grading check accuracy on real cases, and **monitors** alert when a\nrecognition check fails. The platform is model agnostic, with EU and UAE data residency.",[183,186,189],{"question":184,"answer":185},"Can AI decide complaint outcomes?","It should not. NatWest's agent investigates and presents a summarised view to a complaint handler for approval, and the bank says all AI generated summaries are subject to strict human oversight. The other reported use on this page, at Lloyds Banking Group, is classifying complaints.",{"question":187,"answer":188},"Where does AI save the most time in complaints?","Among the deployments on this page, the only reported gain is in classification: Lloyds Banking Group reports that complaint classification takes 1 second instead of about 5 minutes. NatWest is testing an agent that gathers evidence from several data sources into one summary for the handler, but has not disclosed results.",{"question":190,"answer":191},"What is the biggest risk?","A complaint that is never recognized, for example because a customer facing bot treats it as a question to contain. Rules such as the FCA's DISP 1 require complaints to be recorded and resolved within set time limits, a prompt written acknowledgement and a final response within eight weeks, unless the complaint is resolved by the third business day, so recognition deserves as much testing as drafting.",[193,194,195,196,197,198],"complaints-root-cause-analysis","correspondence-triage-and-routing","outbound-notice-drafting","email-and-ticket-reply-drafting","first-line-contact-centre-agent","card-dispute-and-chargeback-intake","2026-09-27",[201],{"date":199,"note":202},"First published","complaints-handling-agent",[205,235],{"title":206,"useCases":207,"organization":208,"vendors":212,"summary":213,"stage":214,"year":215,"channels":216,"languages":217,"metrics":219,"outcomeDisclosed":210,"sources":220,"verification":229,"grade":232,"id":233,"organizationSlug":234},"NatWest: agentic AI that investigates complaints for a human handler, tested with the FCA",[203],{"name":209,"anonymized":210,"country":211,"region":159,"industry":17},"NatWest Group",false,"GB",[],"NatWest is testing an agentic AI system that investigates customer complaints across several data sources and presents a summarised view to a complaint handler, who approves it, with the aim of speeding up handling and resolution. The trial runs in the FCA's AI Live Testing environment, with performance tracked daily on task accuracy, coherence and hallucination, and every AI generated summary subject to human oversight. The bank plans to use production like environments before any move to live use. It is a pilot; no outcome figures are disclosed.","pilot",2026,[33],[218],"en",[],[221,225],{"url":222,"title":223,"publisher":209,"date":224},"https://www.natwestgroup.com/news-and-insights/latest-stories/ai-and-data/2026/may/collaborating-with-the-financial-conduct-authority-on-testing-ag.html","Collaborating with the Financial Conduct Authority on testing agentic AI","2026-05-05",{"url":226,"title":227,"publisher":228},"https://connect.cefpro.com/article/view/fca-warns-banks-as-agentic-ai-nears-consumer-rollout","FCA Warns Banks as Agentic AI Nears Consumer Rollout","CeFPro Connect",{"level":230,"checkedAt":231},"source-verified","2026-09-26","B","natwest-agentic-ai-complaints-handling","natwest-group",{"title":236,"useCases":237,"organization":238,"vendors":240,"summary":241,"stage":242,"year":243,"channels":244,"languages":245,"metrics":246,"outcomeDisclosed":255,"sources":256,"verification":267,"grade":232,"id":268,"organizationSlug":269},"Lloyds Banking Group: AI that classifies customer complaints",[203],{"name":239,"anonymized":210,"country":211,"region":159,"industry":17},"Lloyds Banking Group",[],"Lloyds Banking Group lists complaints handling and automation among the roughly 50 generative AI use cases it had live in 2025. In its 2025 results presentation the bank reports that complaint classification now takes 1 second instead of about 5 minutes. The bank attributes about GBP 50 million of P&L benefit in 2025 to its generative AI use cases as a whole and does not break out the share of the complaints use case.","production",2025,[33],[218],[247],{"kpi":52,"value":68,"unit":248,"qualifier":249,"period":250,"baseline":251,"claimant":252,"quote":253,"sourceUrl":254},"minutes","approximately","in 2025","About 5 minutes to classify a complaint before the AI use case; 1 second after","organization","Outcome: Classification times reduced to 1 second (from c.5 mins)","https://web.archive.org/web/20260905205440id_/https://www.lloydsbankinggroup.com/assets/pdfs/investors/financial-performance/lloyds-banking-group-plc/2025/q4/2025-lbg-fy-presentation.pdf",true,[257,260,263],{"url":254,"title":258,"publisher":239,"date":259},"2025 Results presentation (Internet Archive snapshot)","2026-01-29",{"url":261,"title":262,"publisher":239,"date":259},"https://www.lloydsbankinggroup.com/assets/pdfs/investors/financial-performance/lloyds-banking-group-plc/2025/q4/2025-lbg-fy-presentation.pdf","2025 Results presentation",{"url":264,"title":265,"publisher":239,"date":259,"archivedUrl":266},"https://www.lloydsbankinggroup.com/media/press-releases/2026/lloyds-banking-group/ai-driven-benefits-2026.html","Lloyds Banking Group expects over £100 million in value from next‑generation AI in 2026","https://web.archive.org/web/20260129171626/https://www.lloydsbankinggroup.com/media/press-releases/2026/lloyds-banking-group/ai-driven-benefits-2026.html",{"level":230,"checkedAt":199},"lloyds-banking-group-ai-complaints-processing",null,0,[272],{"kpi":52,"label":273,"unit":248,"aggregate":255,"higherIsBetter":255,"n":274,"nUpTo":270,"median":68,"min":68,"max":68,"byClaimant":275,"vendorOnly":210,"points":276},"Time saved per task",1,{"organization":274,"vendor":270,"regulator":270,"independent":270},[277],{"evidenceId":268,"organization":239,"value":68,"qualifier":249,"claimant":252,"grade":232,"pooled":255},{"low":279,"high":280},166666.6666666667,1166666.6666666667,[282,299,325,346,364,401],{"slug":193,"title":283,"shortTitle":284,"definition":285,"status":9,"industries":286,"functions":288,"patterns":290,"audience":291,"autonomy":38,"adoptionStage":292,"segment":293,"evidenceCount":294,"publicEvidenceCount":294,"organizations":295,"bestGrade":232,"headline":269,"lastVerified":199,"indexable":255},"AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[16,17,19,18,20,287],"government",[24,23,289],"analytics-and-reporting",[26,27,29,30],"back-office","emerging","second-line",3,[296,297,298],"Centers for Medicare and Medicaid Services","Board of Governors of the Federal Reserve System","Federal Trade Commission",{"slug":194,"title":300,"shortTitle":301,"definition":302,"status":9,"industries":303,"functions":304,"patterns":306,"audience":291,"autonomy":308,"adoptionStage":309,"segment":291,"evidenceCount":310,"publicEvidenceCount":310,"organizations":311,"bestGrade":232,"headline":318,"lastVerified":199,"indexable":255},"AI for inbound correspondence triage and routing","Correspondence triage and routing","AI that sorts inbound correspondence before anyone answers it: it takes every inbound letter, email, upload and secure message into one intake, identifies what it is, extracts the key fields, links it to the right customer and account, sets priority and routes it to the right team or workflow, replacing the manual sorting desk.",[16,17,19,287],[305,23,22],"operations",[26,307,27],"document-processing","supervised-agent","mainstream",6,[312,313,314,315,316,317],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":53,"label":319,"unit":320,"n":274,"nUpTo":270,"kind":321,"value":322,"qualifier":323,"claimant":324,"organization":316,"vendorReported":255},"Accuracy","percent","reported",91,"exact","vendor",{"slug":195,"title":326,"shortTitle":327,"definition":328,"status":9,"industries":329,"functions":332,"patterns":335,"audience":37,"autonomy":38,"adoptionStage":39,"segment":291,"evidenceCount":68,"publicEvidenceCount":68,"organizations":337,"bestGrade":232,"headline":342,"lastVerified":231,"indexable":255},"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.",[16,17,19,287,330,331],"healthcare","wealth-and-asset-management",[305,23,333,24,334],"collections-and-recovery","claims",[28,30,336],"translation",[338,339,340,341],"Acentra Health","Hiscox","Health Resources and Services Administration","SS&C Technologies",{"kpi":50,"label":343,"unit":320,"n":274,"nUpTo":270,"kind":321,"value":344,"qualifier":323,"claimant":324,"organization":345,"vendorReported":255},"Cycle time reduction",25,"SS&C GIDS and RS",{"slug":196,"title":347,"shortTitle":348,"definition":349,"status":9,"industries":350,"functions":352,"patterns":353,"audience":37,"autonomy":38,"adoptionStage":309,"evidenceCount":310,"publicEvidenceCount":310,"organizations":354,"bestGrade":232,"headline":361,"lastVerified":199,"indexable":255},"AI reply drafting for customer email and support tickets","Email and ticket reply drafting","A copilot for asynchronous service work that drafts the reply to an incoming customer email, message or ticket once it has reached an agent: it summarizes the request, pulls the relevant customer data and approved knowledge, and drafts a reply in the organization's tone and the customer's language for the agent to check, edit and send. Live calls and chats, and the sorting of the inbox itself, are separate use cases.",[16,287,17,20,351],"technology",[23,305],[28,27,30,26],[355,356,357,358,359,360],"Centers for Disease Control and Prevention","First National Bank","HYPE","Nomad eSIM","Transportation Security Administration","Turing",{"kpi":50,"label":343,"unit":320,"n":362,"nUpTo":270,"kind":321,"value":363,"qualifier":249,"claimant":324,"organization":357,"vendorReported":255},2,50,{"slug":197,"title":365,"shortTitle":366,"definition":367,"status":9,"industries":368,"functions":371,"patterns":372,"audience":375,"autonomy":308,"adoptionStage":309,"segment":376,"evidenceCount":344,"publicEvidenceCount":377,"organizations":378,"bestGrade":232,"headline":395,"lastVerified":199,"indexable":255},"AI agent for first line contact centre service","First line contact centre","An AI agent that answers the first line of inbound customer contact on phone, chat and messaging, resolves general and routine questions end to end in the customer's own language, and routes everything complex, sensitive or regulated to the right human team with the context attached.",[16,17,18,20,369,370,331],"travel-and-hospitality","retail-and-ecommerce",[23],[373,374,30,26],"conversational-agent","voice-agent","customer-facing","front-office",18,[379,380,381,382,383,384,385,386,387,388,389,209,390,391,392,393,394],"Air India","Airbnb","Bank of America","Bank of the Philippine Islands","BT Group","Commonwealth Bank of Australia","Ingka Group","JetBlue","Klarna","Lufthansa Group","Mobily","Pegasus Airlines","Telkomsel","Together Credit Union","Vodafone Germany","Vodafone",{"kpi":396,"label":397,"unit":320,"n":398,"nUpTo":270,"kind":399,"value":400,"qualifier":323,"claimant":269,"organization":269,"vendorReported":210},"containment-rate","Containment rate",7,"median",47,{"slug":198,"title":402,"shortTitle":403,"definition":404,"status":9,"industries":405,"functions":406,"patterns":408,"audience":375,"autonomy":308,"adoptionStage":39,"segment":376,"evidenceCount":409,"publicEvidenceCount":294,"organizations":410,"bestGrade":232,"headline":269,"lastVerified":199,"indexable":255},"AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[17,18],[23,407,305],"fraud-prevention",[373,374,26,307,29],4,[384,387,411],"Visa",{"indexable":255,"reasons":413},[],[415,421,426,434,442,447,453,457,464,469,475,481,488,495,501,506,513,519,525,530,536,542,548,553,557,564,571,576,581,588,594,600,606,611],{"id":147,"label":416,"issuer":417,"region":159,"url":418,"description":419,"useCases":420,"indexable":255},"EU AI Act","European Union","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":148,"label":422,"issuer":417,"region":159,"url":423,"description":424,"useCases":425,"indexable":255},"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":427,"label":428,"issuer":429,"region":430,"url":431,"description":432,"useCases":433,"indexable":255},"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":435,"label":436,"issuer":437,"region":438,"url":439,"description":440,"useCases":441,"indexable":255},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":152,"label":443,"issuer":417,"region":159,"url":444,"description":445,"useCases":446,"indexable":255},"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":149,"label":448,"issuer":449,"region":159,"url":450,"description":451,"useCases":452,"indexable":255},"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":150,"label":454,"issuer":158,"region":159,"url":455,"description":456,"useCases":400,"indexable":255},"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.",{"id":458,"label":459,"issuer":460,"region":165,"url":461,"description":462,"useCases":463,"indexable":255},"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":153,"label":465,"issuer":466,"region":165,"url":467,"description":468,"useCases":344,"indexable":255},"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":470,"label":471,"issuer":472,"region":430,"url":473,"description":474,"useCases":69,"indexable":255},"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":476,"label":477,"issuer":478,"region":438,"url":479,"description":480,"useCases":69,"indexable":255},"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":482,"label":483,"issuer":484,"region":159,"url":485,"description":486,"useCases":487,"indexable":255},"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":489,"label":490,"issuer":491,"region":430,"url":492,"description":493,"useCases":494,"indexable":255},"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":496,"label":497,"issuer":417,"region":159,"url":498,"description":499,"useCases":500,"indexable":255},"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":502,"label":503,"issuer":417,"region":159,"url":504,"description":505,"useCases":500,"indexable":255},"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":507,"label":508,"issuer":509,"region":438,"url":510,"description":511,"useCases":512,"indexable":255},"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":514,"label":515,"issuer":417,"region":159,"url":516,"description":517,"useCases":518,"indexable":255},"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":520,"label":521,"issuer":522,"region":438,"url":523,"description":524,"useCases":518,"indexable":255},"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":154,"label":526,"issuer":527,"region":430,"url":528,"description":529,"useCases":518,"indexable":255},"Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":531,"label":532,"issuer":417,"region":159,"url":533,"description":534,"useCases":535,"indexable":255},"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":537,"label":538,"issuer":539,"region":438,"url":540,"description":541,"useCases":535,"indexable":255},"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":543,"label":544,"issuer":460,"region":165,"url":545,"description":546,"useCases":547,"indexable":255},"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":417,"region":159,"url":551,"description":552,"useCases":547,"indexable":255},"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":151,"label":554,"issuer":417,"region":159,"url":555,"description":556,"useCases":547,"indexable":255},"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":558,"label":559,"issuer":560,"region":159,"url":561,"description":562,"useCases":563,"indexable":255},"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":565,"label":566,"issuer":567,"region":438,"url":568,"description":569,"useCases":570,"indexable":255},"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":572,"label":573,"issuer":417,"region":159,"url":574,"description":575,"useCases":570,"indexable":255},"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":577,"label":578,"issuer":417,"region":159,"url":579,"description":580,"useCases":310,"indexable":255},"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":582,"label":583,"issuer":584,"region":585,"url":586,"description":587,"useCases":68,"indexable":255},"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":589,"label":590,"issuer":591,"region":159,"url":592,"description":593,"useCases":409,"indexable":255},"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":595,"label":596,"issuer":597,"region":159,"url":598,"description":599,"useCases":409,"indexable":255},"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":601,"label":602,"issuer":603,"region":165,"url":604,"description":605,"useCases":294,"indexable":255},"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":607,"label":608,"issuer":417,"region":159,"url":609,"description":610,"useCases":294,"indexable":255},"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":612,"label":613,"issuer":298,"region":438,"url":614,"description":615,"useCases":294,"indexable":255},"us-fcra","Fair Credit Reporting Act","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.",1790598294449]