[{"data":1,"prerenderedAt":558},["ShallowReactive",2],{"uc-property-valuation-support":3,"uc-regulations":348},{"useCase":4,"evidence":170,"blitsAiDeployments":245,"benchmarks":246,"indicative":253,"related":256,"indexability":346,"includeUnpublished":176},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":24,"channels":27,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":47,"macroEstimates":76,"feasibility":77,"implementation":88,"risk":121,"blitsAi":149,"faq":151,"related":164,"datePublished":165,"dateModified":165,"lastVerified":165,"changelog":166,"slug":169},"AI support for property valuation and appraisal","Property valuation support","AI property valuation and appraisal support","Fannie Mae estimates appraisal alternatives saved US mortgage borrowers over $2.5 billion since 2020; C3 AI reports a 40% accuracy gain at Riverside County.","published","AI, most often an automated valuation model, that estimates a property's market value from comparable sales, property characteristics and location data, and either offers to replace a full appraisal within set limits or gives a professional valuer a first pass estimate, the closest comparable sales and a reliability score, so the valuer's time goes to the properties that need a person's judgment.",[12,13,14,15],"automated valuation model","AVM","AI property appraisal","mass appraisal AI",[17,18,19],"real-estate","banking","government",[21,22,23],"lending-and-credit","case-management","operations",[25,26],"prediction-and-scoring","classification-and-routing",[28,29],"internal-tools","api","employee-facing","supervised-agent","mainstream","lending","Every mortgage, remortgage and property tax reassessment needs a value, and a full, on site\nappraisal takes a professional's time to inspect the property, research comparable sales and write\na report for each individual property. The Royal Institution of Chartered Surveyors, a global\nprofessional body for the sector, notes that residential valuation data is often publicly\navailable while commercial property data is often less widely available, which makes automated\nmodels less reliable outside standard, homogeneous housing.\n\nAutomated valuation models are not new, and not all of them use machine learning; RICS points out\nthat some still apply fixed, rule based formulas. What has changed is how many of them now learn\nfrom large, continuously updated datasets of sales, tax records and property characteristics, and\nhow far organizations are willing to let a model's output stand in for a person's inspection.",[],"1. **Collect the comparables.** The model pulls comparable sales, tax assessment and land registry\n   records, property characteristics and location data covering the area.\n2. **Predict and compare.** A regression or machine learning model estimates the property's value\n   and identifies the closest comparable properties that have sold.\n3. **Score the reliability.** The system scores how confident the estimate is, typically from how\n   closely it tracks the comparable sales it used.\n4. **Route by confidence.** High confidence, low risk cases are auto accepted, for example as an\n   appraisal waiver or a direct enrollment at the sale price; low confidence or high value cases\n   are queued for a person.\n5. **A professional reviews the queue.** Valuers or appraisers spend their time on the batches the\n   model flagged as uncertain or close to a decision boundary, not on every case.",[38,39,40,41],"cost-to-serve","speed","employee-productivity","customer-experience",[43,44,45,46],"cost-reduction","customer-savings","accuracy","automation-rate",{"referenceOrg":48,"inputs":49,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A residential mortgage lender originating 50,000 loans a year",[50,57,64],{"key":51,"label":52,"low":53,"high":54,"unit":55,"note":56},"loans","Loans originated per year",20000,100000,"loans per year","Editorial assumption, replace with your own origination volume.",{"key":58,"label":59,"low":60,"high":61,"unit":62,"note":63},"avmEligibleShare","Share of loans eligible for an automated valuation instead of a full appraisal",0.2,0.5,"fraction of loans","Editorial assumption, replace with your own eligibility policy and loan mix.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"appraisalFeeAvoided","Appraisal fee avoided per automated valuation",400,700,"USD per loan","Editorial assumption for a typical US conventional appraisal fee, replace with your own.","loans * avmEligibleShare * appraisalFeeAvoided","USD","per year","Appraisal fee cost avoided","Only the appraisal fee the borrower avoids paying, for the lender's loans that qualify for an automated valuation instead of a full appraisal. It leaves out the cost of building and validating the model, the appraisals still needed for higher risk or higher value loans, and any difference in collateral risk between an automated and a fully manual valuation, which none of the deployments on this page report as a single figure.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"high","Building or buying a model a regulator, lender or model risk team will accept needs deep, clean sales and property data and a documented method for testing accuracy and fairness across property types and areas; the Valuation Office Agency aligned its in house testing to International Association of Assessing Officers AVM standards and had the International Association of Assessing Officers review its model's development process before relying on it.",[81,82,83],"Comparable sales, tax assessment and property characteristic data covering the geography","A documented method to test accuracy and fairness across property types, values and areas","A defined confidence threshold that decides which properties get an automated value and which go to a person",[85,86,87],"Automated underwriting system or tax assessment system that consumes the estimate","Geospatial, land registry or multiple listing service data feeds","A case queue for the properties the model routes to manual valuation",{"steps":89,"guardrails":105,"humanInTheLoop":109,"kpisToInstrument":110,"failureModes":114},[90,93,96,99,102],{"title":91,"detail":92},"Decide where an automated value can stand in for a person","Set the loan to value, price band or property type limits within which an automated value is acceptable, matching what a regulator or an internal model risk function expects.",{"title":94,"detail":95},"Build the confidence score, not just the estimate","Score every estimate's reliability from its distance to comparable sales, so low confidence cases route to a person automatically rather than being accepted at face value.",{"title":97,"detail":98},"Test for accuracy and fairness before launch","Run a ratio study across property types, price bands and geographies, not just an overall error rate, against a recognized mass appraisal standard.",{"title":100,"detail":101},"Keep a professional in the loop for the exceptions","Route low confidence and high value properties to a valuer or appraiser, and feed that person's corrections back into ongoing monitoring of the model.",{"title":103,"detail":104},"Publish the method","A public register entry or a documented internal policy explaining what the model does and why builds the trust an automated valuation program needs from regulators and customers.",[106,107,108],"A confidence score below a set threshold always routes to a human valuer, never an automated value alone","Independent testing against a recognized mass appraisal standard before launch and after every material model change","Loan to value, price band or property type limits on when an automated value replaces a full appraisal","A qualified valuer or appraiser reviews every property the model flags as low confidence or above a value threshold, and a central analytics or model risk team monitors overall accuracy and fairness against completed sales and manual valuations.",[111,112,113],"Automated valuations issued versus properties routed to a person, split by price band and area","Accuracy and dispersion of automated valuations against confirmed sale prices or completed manual valuations","Appeals or challenges to automated valuations as a share of all automated valuations issued",[115,118],{"title":116,"detail":117},"Confident but wrong in a fast moving market","A model trained on past sales lags a market that is moving quickly and prices systematically low or high; monitor the gap between automated valuations and completed sales every month, not only at launch.",{"title":119,"detail":120},"Uneven accuracy across areas and property types","Data rich urban areas get a more accurate value than rural or unusual properties, so the model quietly serves some customers worse than others; a ratio study by property type and area, not an overall figure alone, is what catches this.",{"euAiAct":122,"regulations":125,"guidance":132,"controls":145,"incidents":148},{"tier":123,"basis":124},"context-dependent","An automated valuation model values the collateral, not the person, so it is not itself listed in Annex III; the EU Mortgage Credit Directive treats property valuation (Article 19) and the creditworthiness assessment of the borrower (Article 18) as separate steps, and Article 18(3) says the creditworthiness assessment must not be based predominantly on the value of the property exceeding the amount of credit, or on an assumption that the property's value will increase. The valuation becomes relevant to Annex III point 5(b), creditworthiness assessment of natural persons, only where its output is built into a separate system that evaluates the borrower's creditworthiness, and whether that happens depends on how the lender designs the credit decision, not on the valuation model itself.",[126,127,128,129,130,131],"eu-ai-act","gdpr","eu-mortgage-credit-directive","us-ecoa-reg-b","uk-gdpr","uk-atrs",[133,139],{"title":134,"issuer":135,"region":136,"url":137,"note":138},"Responsible use of AI case study: valuation","Royal Institution of Chartered Surveyors","europe","https://www.rics.org/profession-standards/rics-standards-and-guidance/conduct-competence/responsible-use-of-ai/ruai-case-studies-02","Explains that automated valuation models should support, not replace, a qualified valuer's judgment for high risk valuations such as those informing lending, legal disputes or investment decisions.",{"title":140,"issuer":141,"region":142,"url":143,"note":144},"Quality Control Standards for Automated Valuation Models","CFPB, OCC, Federal Reserve, FDIC, NCUA and FHFA","north-america","https://www.consumerfinance.gov/rules-policy/final-rules/quality-control-standards-for-automated-valuation-models/","The 2024 US interagency final rule requiring mortgage originators and secondary market issuers to adopt policies and controls so that automated valuation models used to value a consumer's principal dwelling maintain a high level of confidence in the estimates, protect data integrity, avoid conflicts of interest, are tested by random sample review and comply with nondiscrimination law.",[146,147],"A qualified valuer signs off on every high value, high risk or low confidence automated valuation before it is relied on","An independently reviewed accuracy and fairness study by property type and area, refreshed on a set cycle",[],{"howToBuild":150},"The valuation model itself, the regression, comparable matching and confidence scoring, runs on\nthe organization's own data science stack or a specialist valuation vendor; Blits.ai is not where\nyou build a mass appraisal model. What fits on Blits.ai is the layer that puts the estimate to\nwork: an SQL knowledge base over the valuation, sales and property data lets an agent answer a\nvaluer's question about why a property scored the way it did and which comparable sales it used,\nin plain language rather than a raw output table.\n\nAgentic workflows with human in the loop confirmation route every low confidence or above\nthreshold estimate to a valuer with approve and reject controls and the comparable evidence\nattached, instead of a queue nobody checks, and every override a valuer makes is logged for the\naccuracy study. Analytics track the\nsplit between automated and manually reviewed valuations over time, and the platform's model\nagnostic routing and EU and UAE data residency options fit a regulated valuation function without\nmoving data outside a required region.",[152,155,158,161],{"question":153,"answer":154},"Can AI fully replace a professional valuer?","It depends on the deployment. Riverside County routes properties outside its configured AVM variance thresholds to a person, and the Valuation Office Agency routes the batches its model scores as least reliable or closest to a Council Tax band boundary to a valuer; RICS is explicit that automated valuation models should support, not replace, the valuation process for lending, legal or investment decisions. Fannie Mae's Value Acceptance goes further and replaces the appraisal entirely for eligible loans, within set loan to value and loan type limits, rather than routing anything to a human valuer.",{"question":156,"answer":157},"How much do lenders and governments save with automated valuation?","Fannie Mae estimates that appraisal alternatives such as Value Acceptance saved US mortgage borrowers more than $2.5 billion since early 2020, though that figure also covers Value Acceptance + Property Data, which uses a third party data collector rather than a fully automated valuation. The UK Valuation Office Agency's own early estimate for its Wales Council Tax revaluation model is a reduction in the cost of a revaluation by one third compared with a fully manual valuation, an estimate for a revaluation planned for 2028 that has not happened yet.",{"question":159,"answer":160},"How accurate does an automated valuation need to be?","There is no single number. C3 AI reports a 40% improvement in model accuracy at Riverside County, over the county's previous linear regression models, demonstrating the ability to directly enroll up to 97% of all property sales. Independent testing against a recognized mass appraisal standard, not a vendor's own figure alone, is what a buyer should ask for.",{"question":162,"answer":163},"Is an automated valuation high risk under the EU AI Act?","An automated valuation model is not itself listed in Annex III; it values the collateral, not the borrower. It becomes relevant to Annex III point 5(b), creditworthiness assessment of natural persons, only where its output is built into a separate system that assesses the borrower, which depends on how the lender designs that system, not on the valuation model alone.",[],"2026-09-28",[167],{"date":165,"note":168},"First published","property-valuation-support",[171,197,224],{"title":172,"useCases":173,"organization":174,"vendors":178,"summary":179,"stage":180,"year":181,"channels":182,"languages":183,"metrics":185,"outcomeDisclosed":176,"sources":186,"verification":192,"grade":194,"id":195,"organizationSlug":196},"UK Valuation Office Agency: automated valuation model for the 2028 Wales Council Tax revaluation",[169],{"name":175,"anonymized":176,"country":177,"region":136,"industry":19},"Valuation Office Agency",false,"GB",[],"The UK's Valuation Office Agency built its own Automated Valuation Model, without an external supplier, to give a first pass value, the closest comparable sales, a reliability score and a Council Tax band for the vast majority of Wales's 1.5 million domestic properties ahead of the 2028 Council Tax revaluation. Valuers focus their time on the batches of properties the model flags as least reliable or closest to a band boundary, and the International Association of Assessing Officers reviewed the model's development process and reported confidence in its quality.","production",2025,[28],[184],"en",[],[187],{"url":188,"title":189,"publisher":190,"date":191},"https://www.gov.uk/algorithmic-transparency-records/valuation-office-agency-automated-valuation-model","Valuation Office Agency: Automated Valuation Model","GOV.UK","2025-08-06",{"level":193,"checkedAt":165},"source-verified","B","uk-voa-wales-automated-valuation-model",null,{"title":198,"useCases":199,"organization":200,"vendors":203,"summary":204,"stage":205,"year":206,"channels":207,"languages":208,"metrics":209,"outcomeDisclosed":218,"sources":219,"verification":222,"grade":194,"id":223,"organizationSlug":196},"Fannie Mae: Value Acceptance automated valuation for mortgage loans",[169],{"name":201,"anonymized":176,"country":202,"region":142,"industry":18},"Fannie Mae","US",[],"Fannie Mae's Desktop Underwriter can issue Value Acceptance, an offer to skip a traditional appraisal, using what Fannie Mae calls a robust data and modeling framework to confirm the validity of a property's value and sale price. Fannie Mae announced that, beginning in the first quarter of 2025, the eligible loan to value ratio for Value Acceptance on purchase loans for primary residences and second homes would increase from 80% to 90%, and it estimates that appraisal alternatives such as Value Acceptance and Value Acceptance + Property Data have saved mortgage borrowers more than $2.5 billion since early 2020.","scaled",2024,[29],[184],[210],{"kpi":44,"value":211,"unit":212,"currency":72,"qualifier":213,"period":214,"claimant":215,"quote":216,"sourceUrl":217},2500000000,"currency","at-least","since early 2020","organization","Since early 2020, Fannie Mae estimates the use of appraisal alternatives such as Value Acceptance and Value Acceptance + Property Data on loans Fannie Mae has acquired saved mortgage borrowers more than $2.5 billion.","https://www.fanniemae.com/newsroom/fannie-mae-news/fannie-mae-announces-changes-appraisal-alternatives-requirements",true,[220],{"url":217,"title":221,"publisher":201},"Fannie Mae Announces Changes to Appraisal Alternatives Requirements",{"level":193,"checkedAt":165},"fannie-mae-value-acceptance-appraisal-alternatives",{"title":225,"useCases":226,"organization":227,"vendors":229,"summary":233,"stage":180,"year":206,"channels":234,"languages":235,"metrics":236,"outcomeDisclosed":176,"sources":237,"verification":242,"grade":243,"id":244,"organizationSlug":196},"Riverside County: C3 AI residential property appraisal",[169],{"name":228,"anonymized":176,"country":202,"region":142,"industry":19},"Riverside County Assessor-County Clerk-Recorder",[230],{"name":231,"role":232},"C3 AI","platform","Riverside County's Assessor-County Clerk-Recorder deployed the C3 AI Residential Property Appraisal application for around 460,000 single family homes and condominiums, replacing manual linear regression models used to automatically enroll eligible change of ownership transfers at their sale price. C3 AI describes this as Riverside's initial production deployment, delivered in under six months, meant to demonstrate the application's ability to improve staff efficiency and reduce the complexity of its modeling approach.",[28],[184],[],[238],{"url":239,"title":240,"publisher":231,"archivedUrl":241},"https://c3.ai/customers/riverside-county-drives-40-increase-in-model-accuracy-for-property-appraisal/","Riverside County Drives 40% Increase in Model Accuracy for Property Appraisal","https://web.archive.org/web/20241203111911/https://c3.ai/customers/riverside-county-drives-40-increase-in-model-accuracy-for-property-appraisal/",{"level":193,"checkedAt":165},"C","riverside-county-c3-ai-property-appraisal",0,[247],{"kpi":44,"label":248,"unit":212,"currency":72,"aggregate":176,"higherIsBetter":218,"n":249,"nUpTo":245,"median":211,"min":211,"max":211,"byClaimant":250,"vendorOnly":176,"points":251},"Customer savings",1,{"organization":249,"vendor":245,"regulator":245,"independent":245},[252],{"evidenceId":223,"organization":201,"value":211,"qualifier":213,"claimant":215,"grade":194,"pooled":218},{"low":254,"high":255},1600000,35000000,[257,287,309,329],{"slug":258,"title":259,"shortTitle":260,"definition":261,"status":9,"industries":262,"functions":265,"patterns":267,"audience":270,"autonomy":31,"adoptionStage":32,"segment":270,"evidenceCount":271,"publicEvidenceCount":271,"organizations":272,"bestGrade":194,"headline":279,"lastVerified":286,"indexable":218},"correspondence-triage-and-routing","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.",[263,18,264,19],"cross-industry","insurance",[23,266,22],"customer-service",[26,268,269],"document-processing","summarization","back-office",6,[273,274,275,276,277,278],"Ecclesia Group","Encova Insurance","Loadsure","The Master Trust Bank of Japan","Travelers","U.S. Department of Veterans Affairs",{"kpi":45,"label":280,"unit":281,"n":249,"nUpTo":245,"kind":282,"value":283,"qualifier":284,"claimant":285,"organization":277,"vendorReported":218},"Accuracy","percent","reported",91,"exact","vendor","2026-09-27",{"slug":288,"title":289,"shortTitle":290,"definition":291,"status":9,"industries":292,"functions":295,"patterns":297,"audience":270,"autonomy":31,"adoptionStage":32,"evidenceCount":299,"publicEvidenceCount":300,"organizations":301,"bestGrade":194,"headline":307,"lastVerified":286,"indexable":218},"intelligent-document-processing","AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[263,19,293,294],"automotive","manufacturing",[23,22,296],"finance-and-accounting",[268,298,26],"computer-vision",7,5,[302,303,304,305,306],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":45,"label":280,"unit":281,"n":249,"nUpTo":245,"kind":282,"value":308,"qualifier":213,"claimant":285,"organization":302,"vendorReported":218},90,{"slug":310,"title":311,"shortTitle":312,"definition":313,"status":9,"industries":314,"functions":316,"patterns":317,"audience":270,"autonomy":31,"adoptionStage":319,"segment":270,"evidenceCount":320,"publicEvidenceCount":321,"organizations":322,"bestGrade":243,"headline":325,"lastVerified":286,"indexable":218},"account-servicing-execution","AI for back office account servicing execution","Account servicing execution","AI that executes the servicing requests that land in operations queues, such as address and mandate changes, standing instructions, beneficiary updates, reissues, payoff and reference letters and loan maintenance, by reading the request, checking it against policy and entitlements, and preparing or making the change in core systems under dual control.",[18,264,315],"wealth-and-asset-management",[23,21],[318,268,26],"agentic-workflow","early-adopters",3,2,[323,324],"Banco Supervielle","SS&C Technologies",{"kpi":326,"label":327,"unit":281,"n":321,"nUpTo":245,"kind":282,"value":328,"qualifier":284,"claimant":285,"organization":324,"vendorReported":218},"processing-time-reduction","Cycle time reduction",95,{"slug":330,"title":331,"shortTitle":332,"definition":333,"status":9,"industries":334,"functions":335,"patterns":337,"audience":340,"autonomy":341,"adoptionStage":319,"evidenceCount":342,"publicEvidenceCount":342,"organizations":343,"bestGrade":194,"headline":196,"lastVerified":286,"indexable":218},"immigration-and-visa-application-assistant","AI for immigration and visa applications, from applicant questions to case preparation","Immigration and visa application assistant","AI that helps applicants understand immigration and visa requirements and submit complete applications, and helps immigration staff prepare cases by extracting form data, classifying evidence, routing applications and supporting interviews, while every grant or refusal is decided by an officer against the immigration rules.",[19],[336,22,23],"citizen-services",[338,268,26,339],"conversational-agent","translation","customer-facing","copilot",4,[344,345,304,305],"Home Office (Visa, Status and Information Services)","U.S. Department of State (Bureau of Consular Affairs)",{"indexable":218,"reasons":347},[],[349,355,360,368,375,381,387,394,402,409,416,422,428,435,441,446,453,459,465,471,477,483,489,494,499,506,512,517,522,529,535,541,547,552],{"id":126,"label":350,"issuer":351,"region":136,"url":352,"description":353,"useCases":354,"indexable":218},"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":127,"label":356,"issuer":351,"region":136,"url":357,"description":358,"useCases":359,"indexable":218},"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":361,"label":362,"issuer":363,"region":364,"url":365,"description":366,"useCases":367,"indexable":218},"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":369,"label":370,"issuer":371,"region":142,"url":372,"description":373,"useCases":374,"indexable":218},"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":376,"label":377,"issuer":351,"region":136,"url":378,"description":379,"useCases":380,"indexable":218},"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":130,"label":382,"issuer":383,"region":136,"url":384,"description":385,"useCases":386,"indexable":218},"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":388,"label":389,"issuer":390,"region":136,"url":391,"description":392,"useCases":393,"indexable":218},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","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":395,"label":396,"issuer":397,"region":398,"url":399,"description":400,"useCases":401,"indexable":218},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":403,"label":404,"issuer":405,"region":398,"url":406,"description":407,"useCases":408,"indexable":218},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":410,"label":411,"issuer":412,"region":364,"url":413,"description":414,"useCases":415,"indexable":218},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":417,"label":418,"issuer":419,"region":142,"url":420,"description":421,"useCases":415,"indexable":218},"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":131,"label":423,"issuer":424,"region":136,"url":425,"description":426,"useCases":427,"indexable":218},"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":429,"label":430,"issuer":431,"region":364,"url":432,"description":433,"useCases":434,"indexable":218},"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":436,"label":437,"issuer":351,"region":136,"url":438,"description":439,"useCases":440,"indexable":218},"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":442,"label":443,"issuer":351,"region":136,"url":444,"description":445,"useCases":440,"indexable":218},"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":447,"label":448,"issuer":449,"region":142,"url":450,"description":451,"useCases":452,"indexable":218},"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":454,"label":455,"issuer":351,"region":136,"url":456,"description":457,"useCases":458,"indexable":218},"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":460,"label":461,"issuer":462,"region":142,"url":463,"description":464,"useCases":458,"indexable":218},"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":466,"label":467,"issuer":468,"region":364,"url":469,"description":470,"useCases":458,"indexable":218},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":472,"label":473,"issuer":351,"region":136,"url":474,"description":475,"useCases":476,"indexable":218},"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":478,"label":479,"issuer":480,"region":142,"url":481,"description":482,"useCases":476,"indexable":218},"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":484,"label":485,"issuer":397,"region":398,"url":486,"description":487,"useCases":488,"indexable":218},"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":490,"label":491,"issuer":351,"region":136,"url":492,"description":493,"useCases":488,"indexable":218},"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":495,"label":496,"issuer":351,"region":136,"url":497,"description":498,"useCases":488,"indexable":218},"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":500,"label":501,"issuer":502,"region":136,"url":503,"description":504,"useCases":505,"indexable":218},"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":129,"label":507,"issuer":508,"region":142,"url":509,"description":510,"useCases":511,"indexable":218},"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":513,"label":514,"issuer":351,"region":136,"url":515,"description":516,"useCases":511,"indexable":218},"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":518,"label":519,"issuer":351,"region":136,"url":520,"description":521,"useCases":271,"indexable":218},"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":523,"label":524,"issuer":525,"region":526,"url":527,"description":528,"useCases":300,"indexable":218},"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":530,"label":531,"issuer":532,"region":136,"url":533,"description":534,"useCases":342,"indexable":218},"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":536,"label":537,"issuer":538,"region":136,"url":539,"description":540,"useCases":342,"indexable":218},"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":542,"label":543,"issuer":544,"region":398,"url":545,"description":546,"useCases":320,"indexable":218},"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":548,"label":549,"issuer":351,"region":136,"url":550,"description":551,"useCases":320,"indexable":218},"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":553,"label":554,"issuer":555,"region":142,"url":556,"description":557,"useCases":320,"indexable":218},"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.",1790598306577]