[{"data":1,"prerenderedAt":599},["ShallowReactive",2],{"uc-tax-compliance-risk-scoring":3,"uc-regulations":392},{"useCase":4,"evidence":200,"blitsAiDeployments":291,"benchmarks":292,"indicative":304,"related":307,"indexability":390,"includeUnpublished":206},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":25,"audience":28,"autonomy":29,"adoptionStage":30,"problem":31,"problemStats":32,"howItWorks":38,"valueDrivers":39,"kpis":44,"indicativeValue":50,"macroEstimates":79,"feasibility":80,"implementation":92,"risk":135,"blitsAi":178,"faq":180,"related":190,"datePublished":195,"dateModified":195,"lastVerified":195,"changelog":196,"slug":199},"AI for tax compliance risk scoring and audit selection","Tax compliance risk scoring","AI for tax audit selection and risk scoring","The IRS, HMRC and the Belastingdienst use models and rules to choose which tax returns to check. How risk scoring works, how to value it and how to keep it fair.","published","Models that score tax returns, taxpayers and transactions for the risk of error, underreporting or fraud, so that a tax administration spends its audit and compliance capacity where the risk is highest, with an officer deciding every compliance action and the selection itself monitored for fairness.",[12,13,14,15],"AI audit selection","tax risk scoring","tax return risk assessment","case selection for tax audits",[17],"government",[19,20,21],"risk-management","case-management","fraud-prevention",[23,24],"prediction-and-scoring","anomaly-detection",[26,27],"internal-tools","api","back-office","assist","early-adopters","Tax administrations can examine only part of the returns they receive, so the question\nof which returns to open decides both how much revenue is protected and who carries the burden of\nan audit. Traditional selection relies on fixed rules, random samples and the judgment of\nexperienced staff. That misses new schemes, sends officers to returns that turn out to be correct\n(so called no change audits) and struggles with complex taxpayers, such as large partnerships,\nwhere the risk is spread over many entities and schedules.\n\nMore data now supports model based selection: third party reporting, electronic invoicing, bank and\ncross border information exchange. It has also shown the risk. A selection model that is accurate\non average can still concentrate audits on particular groups, and the Dutch childcare benefits\nscandal, in which the tax administration's risk classification used nationality, made fairness,\ntransparency and human review preconditions rather than extras.",[33],{"statement":34,"sourceTitle":35,"sourceUrl":36,"year":37},"The IRS projects an annual gross tax gap of USD 696 billion for tax year 2022, of which USD 539 billion comes from tax understated on timely filed returns.","IRS: The tax gap","https://www.irs.gov/statistics/irs-the-tax-gap",2024,"1. **Assemble the risk picture.** Returns are joined with third party data the administration\n   already holds: employer and bank reporting, invoices, customs data, prior audit results and\n   information exchanged with other countries.\n2. **Score.** Anomaly detection flags returns that break a taxpayer's own pattern or deviate from\n   peers; supervised models trained on past audit outcomes estimate the likelihood and size of a\n   correction; business rules encode known risks. The approaches can be combined; the\n   Belastingdienst VAT signal model on this page, for example, is purely rules based.\n3. **Explain the signal.** Each score comes with the features and rules that drove it, so an\n   officer can see why a return was flagged and challenge it.\n4. **Select with people.** Risk teams turn scores into case lists, mixed with a random sample that\n   keeps measuring the unflagged population, and an officer decides whether to open a check.\n5. **Learn and monitor.** Audit results feed back into the model; selection rates and outcomes are\n   compared across groups to detect disparate impact before it becomes a scandal.",[40,41,42,43],"risk-reduction","employee-productivity","compliance","cost-to-serve",[45,46,47,48,49],"detection-rate-improvement","false-positive-reduction","users-served","interactions-handled","accuracy",{"referenceOrg":51,"inputs":52,"formula":74,"currency":75,"period":76,"resultLabel":77,"caveat":78},"A national tax administration that completes 10,000 desk and field audits a year",[53,60,67],{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"audits","Audits completed per year",8000,12000,"audits per year","Editorial assumption. Replace with your own audit volume.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"yieldPerAudit","Average additional tax assessed per audit",5000,15000,"EUR per audit","Editorial assumption. Replace with your own average yield, including audits that end with no change.",{"key":68,"label":69,"low":70,"high":71,"unit":72,"note":73},"uplift","Relative increase in yield from better selection",0.05,0.15,"fraction of yield","Editorial assumption, deliberately modest. The public evidence on this page reports usage and selection practice, but no audited tax yield uplift.","audits * yieldPerAudit * uplift","EUR","per year","Additional tax assessed from the same audit capacity","Assessed tax, not collected tax. It leaves out collection losses, appeals, the cost of building and assuring the models, the deterrence effect and the benefit to compliant taxpayers of fewer no change audits.",[],{"complexity":81,"complexityNote":82,"dataPrerequisites":83,"integrations":87},"high","The data usually exists; the difficulty is governance. Selection models touch taxpayer rights, need a legal basis for each data source, must be explainable to officers and courts, and need fairness monitoring that often lacks the protected characteristic data to do it well.",[84,85,86],"Historical audit outcomes, including no change results, linked to returns","Third party information returns and invoices, with a documented legal basis for each","A random audit or sample programme to measure the unflagged population",[88,89,90,91],"Return processing and taxpayer account systems","Case management for compliance checks and audits","Data warehouse with third party and exchange of information data","Analytics workspace for risk teams and model monitoring",{"steps":93,"guardrails":109,"humanInTheLoop":115,"kpisToInstrument":116,"failureModes":122},[94,97,100,103,106],{"title":95,"detail":96},"Start where the data is richest and the harm is lowest","Begin with business taxes such as VAT, where invoices and returns give a strong signal and the population is mostly companies, before models touch individuals and families.",{"title":98,"detail":99},"Keep a random sample running","Reserve part of the audit capacity for random selection. It is the only way to measure what the model misses and to prove it beats the old approach.",{"title":101,"detail":102},"Put explanation in front of the officer","Show the reasons for a flag next to the return. HMRC's VAT tool, for example, shows expected against observed values for each return period, so the officer judges the anomaly.",{"title":104,"detail":105},"Test for disparate impact before and after go live","Compare selection rates and hit rates across groups you can measure, including proxies such as income band and region, and document how you will act on a gap.",{"title":107,"detail":108},"Publish what you run","Register every selection model with its purpose, data and human oversight. The Dutch Belastingdienst publishes its selection and signal models in the national algorithm register.",[110,111,112,113,114],"No protected characteristic, or obvious proxy such as nationality, as a model feature","A score never triggers an assessment or penalty on its own; an officer decides every action","Random sample alongside model selection to measure performance and fairness","Documented legal basis for every data source used in scoring","Model changes reviewed by an independent validation function before use","Risk analysts own the models and the case lists; officers decide whether to open a check and carry out every compliance action. Taxpayers keep the normal review and appeal rights, and a fairness review of selection outcomes is reported to senior management at least yearly.",[117,118,119,120,121],"Hit rate (share of selected cases with a correction) against random selection","No change rate of audits, before and after","Additional tax assessed per audit hour","Selection and hit rates across measurable groups","Share of flags overridden by officers, with reasons",[123,126,129,132],{"title":124,"detail":125},"Discriminatory selection","A model trained on past audits learns past bias, or uses a proxy such as nationality. The Dutch childcare benefits scandal and the IRS earned income tax credit disparities show the harm. Remove proxies and monitor outcomes by group.",{"title":127,"detail":128},"Feedback loops","The model only learns from returns it selected, so it keeps finding the same risks. Random audits break the loop.",{"title":130,"detail":131},"Unexplainable flags","Officers who cannot see why a return was flagged either ignore the model or trust it blindly. Show the drivers of each score.",{"title":133,"detail":134},"Blacklists that outlive their purpose","Risk signals stored about individuals and never removed can follow people for years. Set retention and review rules for every signal list.",{"euAiAct":136,"regulations":139,"guidance":146,"controls":163,"incidents":169},{"tier":137,"basis":138},"context-dependent","Risk selection for administrative tax audits is not listed in Annex III, and Recital 59 says systems used by tax and customs authorities in administrative proceedings should not be treated as high risk law enforcement systems. Use in criminal tax investigations (Annex III point 6, law enforcement), or evaluating the eligibility of natural persons for public assistance benefits run through the tax system (Annex III point 5(a)), can make it high risk. When individuals are scored in administrative tax work, the GDPR applies, including its profiling rules (Member States may restrict some rights for taxation matters under Article 23). Article 22 applies when a decision with legal or similarly significant effect is taken solely by the model. Criminal investigations fall outside the GDPR and under the Law Enforcement Directive (EU) 2016/680 instead.",[140,141,142,143,144,145],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[147,153,158],{"title":148,"issuer":149,"region":150,"url":151,"note":152},"Regulation (EU) 2024/1689 (AI Act), Recital 59","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Systems intended for administrative proceedings by tax and customs authorities should not be classified as high risk law enforcement systems.",{"title":154,"issuer":155,"region":150,"url":156,"note":157},"Algoritmeregister van de Nederlandse overheid","Government of the Netherlands","https://algoritmes.overheid.nl/nl","The Dutch national algorithm register, where the Belastingdienst and other agencies publish their selection and risk models with purpose, method and human oversight.",{"title":159,"issuer":160,"region":150,"url":161,"note":162},"Algorithmic Transparency Recording Standard hub","UK government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","UK public bodies, including HMRC, publish transparency records for algorithmic tools used in compliance work.",[164,165,166,167,168],"Register entry per model with purpose, data, owner and human oversight","Fairness monitoring of selection and hit rates, reported at least yearly","Explanation of each flag available to the officer and, on request, in disputes","Independent model validation before first use and after material change","Retention limits on risk signals held about individuals",[170,174],{"title":171,"url":172,"note":173},"Amnesty International: Xenophobic machines: Discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal","https://www.amnesty.org/en/wp-content/uploads/2021/10/EUR3546862021ENGLISH.pdf","The Dutch tax authorities used a risk classification model in which nationality counted as a risk factor when checking childcare benefit applications, contributing to wrongful fraud accusations against parents.",{"title":175,"url":176,"note":177},"Stanford SIEPR: Measuring and mitigating racial disparities in tax audits","https://siepr.stanford.edu/publications/working-paper/measuring-and-mitigating-racial-disparities-tax-audits","Researchers estimate that, despite race blind selection, Black taxpayers were audited at 2.9 to 4.7 times the rate of non Black taxpayers, driven mainly by audits of earned income tax credit claims.",{"howToBuild":179},"Blits.ai does not replace a tax administration's scoring models; it builds the work around them.\nAn **agentic workflow** can pick up a flagged return, pull the return and the third party data\nthrough **custom functions** (REST calls and SQL queries), and prepare a case summary with the\ndrivers of the flag for the officer, stopping for **human in the loop approval** before anything\nis sent to the taxpayer. A **knowledge base** with hybrid retrieval over the audit manual and\nguidance lets officers ask how a risk should be handled.\n\nEvery run keeps a full audit trail, **PII masking** runs at the gateway before text reaches a\nmodel, and **test suites** check the case summaries against known cases on every change. The\nplatform is **model agnostic** and offers EU and UAE data residency, which matters for taxpayer\ndata that must stay in the country or region.",[181,184,187],{"question":182,"answer":183},"Do tax administrations really use AI to choose audits?","Yes, in varying forms. The IRS announced in 2023 that machine learning helped select large partnership returns for examination, HMRC gives around 5,500 VAT officers an anomaly detection tool, and the Dutch Belastingdienst publishes its selection models, such as a rules based VAT signal model for large businesses, in the national algorithm register.",{"question":185,"answer":186},"Is audit selection by AI high risk under the EU AI Act?","Usually not for administrative tax audits, which Recital 59 keeps out of the law enforcement category. It can become high risk when used in criminal investigations or to decide on public benefits. When individuals are scored, GDPR profiling rules apply, with Article 22 covering any decision the model takes on its own.",{"question":188,"answer":189},"How do you keep selection fair?","Exclude protected characteristics and proxies, keep a random sample, and compare selection and hit rates across groups every year. The childcare benefits scandal and research on US earned income tax credit audits show what happens without these checks.",[191,192,193,194],"benefit-fraud-and-error-detection","inspection-prioritization","tax-questions-and-filing-assistant","ai-model-inventory","2026-09-27",[197],{"date":195,"note":198},"First published","tax-compliance-risk-scoring",[201,242,267],{"title":202,"useCases":203,"organization":204,"vendors":208,"summary":212,"stage":213,"year":214,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":231,"sources":232,"verification":237,"grade":239,"id":240,"organizationSlug":241},"HM Revenue and Customs: VAT Return Analysis Tool with anomaly detection for compliance checks",[199],{"name":205,"anonymized":206,"country":207,"region":150,"industry":17},"HM Revenue and Customs",false,"GB",[209],{"name":210,"role":211},"In house (HMRC Data Science Analytics)","in-house","HMRC's VAT Return Analysis Tool brings a VAT trader's entity, ledger and return data for the most recent seven years into one interactive view for VAT officers, and uses a classical statistical model (seasonal trend decomposition with an interquartile range rule) to flag anomalous values in the return history. Officers use it to prepare and carry out compliance checks; the tool makes no decisions, and any assessment is made by an officer and can be appealed through the normal route. HMRC's transparency record says around 5,500 officers are licensed and the tool is used about 1,500 times a day.","scaled",2025,[26],[217],"en",[219,227],{"kpi":47,"value":220,"unit":221,"qualifier":222,"period":223,"claimant":224,"quote":225,"sourceUrl":226},5500,"count","approximately","licensed VAT officers","organization","Around 5,500 officers have a license to use the tool as part of their VAT compliance work.","https://www.gov.uk/algorithmic-transparency-records/hmrc-vat-return-analysis-tool",{"kpi":48,"value":228,"unit":221,"qualifier":222,"period":229,"claimant":224,"quote":230,"sourceUrl":226},1500,"per day","There are ~1,500 daily uses of the tool.",true,[233],{"url":226,"title":234,"publisher":235,"date":236},"HMRC: VAT Return Analysis Tool (algorithmic transparency record)","GOV.UK","2025-12-16",{"level":238,"checkedAt":195},"source-verified","B","hmrc-vat-return-analysis-tool",null,{"title":243,"useCases":244,"organization":245,"vendors":248,"summary":251,"stage":252,"year":37,"channels":253,"languages":254,"metrics":256,"outcomeDisclosed":206,"sources":257,"verification":265,"grade":239,"id":266,"organizationSlug":241},"Belastingdienst: rules based VAT signal model for large businesses, published in the Dutch algorithm register",[199],{"name":246,"anonymized":206,"country":247,"region":150,"industry":17},"Belastingdienst","NL",[249],{"name":250,"role":211},"In house (Belastingdienst)","Since 1 October 2024 the Dutch Tax and Customs Administration has supported staff of its Large Businesses directorate with a signal model that risk assesses VAT returns. The model applies business rules drawn from legislation, expertise and statistics, is explicitly not self learning, and sorts signals into priority categories that help staff decide when and by whom a signal is handled. The register says the model also takes decisions itself; where it cannot (more complex situations or a deviation in the return), a staff member intervenes. Samples are also drawn from returns the model does not select, and the rules are evaluated every year against those results. It is one of several VAT signal models the Belastingdienst has published in the national algorithm register.","production",[26],[255],"nl",[],[258,261],{"url":259,"title":260,"publisher":154},"https://algoritmes.overheid.nl/nl/algoritme/189378/62272663/signaalmodel-omzetbelasting-grote-ondernemingen-sob-go","Signaalmodel Omzetbelasting Grote Ondernemingen (SOB GO), Algoritmeregister",{"url":262,"title":263,"publisher":246,"date":264},"https://over-ons.belastingdienst.nl/onderwerpen/omgaan-met-gegevens/algoritmeregister/signaalmodel-omzetbelasting-grote-ondernemingen-sob-go/","Signaalmodel Omzetbelasting Grote Ondernemingen (SOB GO)","2024-11-26",{"level":238,"checkedAt":195},"belastingdienst-vat-signal-model-large-businesses",{"title":268,"useCases":269,"organization":270,"vendors":274,"summary":277,"stage":278,"year":279,"channels":280,"languages":281,"metrics":282,"outcomeDisclosed":206,"sources":283,"verification":288,"grade":239,"id":289,"organizationSlug":290},"Internal Revenue Service: machine learning to select large partnership returns for examination",[199],{"name":271,"anonymized":206,"country":272,"region":273,"industry":17},"Internal Revenue Service","US","north-america",[275],{"name":276,"role":211},"In house (IRS data science and tax enforcement teams)","In September 2023 the IRS announced that it was expanding its Large Partnership Compliance programme, which it described as a pilot leveraging AI, to additional large partnerships. It said the returns had been selected with the help of AI by data scientists and tax enforcement experts who applied machine learning to identify compliance risk in partnership tax, general income tax and accounting, and international tax. The IRS said it would open examinations of 75 of the largest partnerships, each with more than USD 10 billion in assets on average, a segment that had seen little examination coverage. The IRS also said AI would help improve case selection so that fewer taxpayers face audits that end with no change. No outcome of the AI selected examinations is published in the release.","pilot",2023,[26],[217],[],[284],{"url":285,"title":286,"publisher":271,"date":287},"https://www.irs.gov/newsroom/irs-announces-sweeping-effort-to-restore-fairness-to-tax-system-with-inflation-reduction-act-funding-new-compliance-efforts","IRS announces sweeping effort to restore fairness to tax system with Inflation Reduction Act funding (IR-2023-166)","2023-09-08",{"level":238,"checkedAt":195},"irs-large-partnership-audit-selection","internal-revenue-service",0,[293,299],{"kpi":48,"label":294,"unit":221,"aggregate":206,"higherIsBetter":231,"n":295,"nUpTo":291,"median":228,"min":228,"max":228,"byClaimant":296,"vendorOnly":206,"points":297},"Interactions handled",1,{"organization":295,"vendor":291,"regulator":291,"independent":291},[298],{"evidenceId":240,"organization":205,"value":228,"qualifier":222,"claimant":224,"grade":239,"pooled":231},{"kpi":47,"label":300,"unit":221,"aggregate":206,"higherIsBetter":231,"n":295,"nUpTo":291,"median":220,"min":220,"max":220,"byClaimant":301,"vendorOnly":206,"points":302},"Users served",{"organization":295,"vendor":291,"regulator":291,"independent":291},[303],{"evidenceId":240,"organization":205,"value":220,"qualifier":222,"claimant":224,"grade":239,"pooled":231},{"low":305,"high":306},2000000,27000000,[308,329,346,370],{"slug":191,"title":309,"shortTitle":310,"definition":311,"status":9,"industries":312,"functions":313,"patterns":315,"audience":28,"autonomy":29,"adoptionStage":30,"evidenceCount":316,"publicEvidenceCount":316,"organizations":317,"bestGrade":239,"headline":323,"lastVerified":195,"indexable":231},"AI for benefit fraud and error detection in social security","Benefit fraud and error detection","Risk models that help a social security or benefits agency decide which claims, payments and recipients to check for fraud or error, so that caseworkers verify the riskiest cases first, while every decision on entitlement stays with a person and the model is tested for fairness before and during use.",[17],[21,314,20],"citizen-services",[23,24],5,[318,319,320,321,322],"Centers for Medicare and Medicaid Services","Department for Work and Pensions","Gemeente Rotterdam","U.S. Department of the Treasury, Bureau of the Fiscal Service","Uitvoeringsinstituut Werknemersverzekeringen (UWV)",{"kpi":45,"label":324,"unit":325,"n":295,"nUpTo":291,"kind":326,"value":327,"qualifier":328,"claimant":224,"organization":319,"vendorReported":206},"Detection improvement","multiplier","reported",2.5,"exact",{"slug":192,"title":330,"shortTitle":331,"definition":332,"status":9,"industries":333,"functions":334,"patterns":336,"audience":337,"autonomy":29,"adoptionStage":30,"evidenceCount":338,"publicEvidenceCount":338,"organizations":339,"bestGrade":239,"headline":241,"lastVerified":195,"indexable":231},"AI for risk based inspection prioritization in food safety, workplace and environmental regulation","Inspection prioritization","Models that predict which premises, operators or activities are most likely to be non compliant, so that inspectors in food safety, workplace safety, environmental and other regulation spend their visits where the risk is highest, ideally with inspectors choosing the visits and random inspections testing the model.",[17],[19,20,335],"regulatory-compliance",[23,24],"employee-facing",6,[340,341,342,343,344,345],"Care Quality Commission","Driver and Vehicle Standards Agency","U.S. Environmental Protection Agency, Office of Enforcement and Compliance Assurance","Food Standards Agency","Nederlandse Arbeidsinspectie","Nederlandse Voedsel- en Warenautoriteit (NVWA)",{"slug":193,"title":347,"shortTitle":348,"definition":349,"status":9,"industries":350,"functions":351,"patterns":354,"audience":359,"autonomy":360,"adoptionStage":361,"evidenceCount":362,"publicEvidenceCount":362,"organizations":363,"bestGrade":239,"headline":365,"lastVerified":369,"indexable":231},"AI assistant for tax questions and filing support","Tax questions and filing assistant","An AI assistant that answers taxpayers' questions about taxes, deadlines, refunds and payments, lets authenticated taxpayers check their status or set up a payment plan within set rules, and guides them through filing, while assessments, penalties and disputes stay with the tax authority's staff and systems.",[17],[314,352,353],"customer-service","finance-and-accounting",[355,356,357,358],"conversational-agent","voice-agent","rag-knowledge-assistant","classification-and-routing","customer-facing","supervised-agent","mainstream",4,[364,205,271],"ClearTax",{"kpi":49,"label":366,"unit":367,"n":295,"nUpTo":291,"kind":326,"value":368,"qualifier":328,"claimant":224,"organization":205,"vendorReported":206},"Accuracy","percent",83.03,"2026-09-26",{"slug":194,"title":371,"shortTitle":372,"definition":373,"status":9,"industries":374,"functions":379,"patterns":381,"audience":337,"autonomy":384,"adoptionStage":30,"evidenceCount":362,"publicEvidenceCount":362,"organizations":385,"bestGrade":239,"headline":241,"lastVerified":195,"indexable":231},"AI system and model inventory with shadow AI discovery","AI model inventory","A governed register of every AI system and model an organization builds, buys or uses, with its owner, purpose, data, risk tier and approval status, kept current by AI that discovers unregistered use, reads the documentation and assembles the evidence a board, auditor or supervisor asks for.",[375,376,377,17,378],"cross-industry","banking","insurance","manufacturing",[19,335,380],"it-and-engineering",[382,383,357,358],"agentic-workflow","document-processing","copilot",[386,387,388,389],"Board of Governors of the Federal Reserve System","Office of Management and Budget","Unilever","U.S. Department of Justice",{"indexable":231,"reasons":391},[],[393,397,402,409,415,421,427,434,442,449,456,462,467,474,480,485,492,498,504,510,516,522,528,533,538,545,552,557,562,569,575,581,588,593],{"id":140,"label":394,"issuer":149,"region":150,"url":151,"description":395,"useCases":396,"indexable":231},"EU AI Act","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":141,"label":398,"issuer":149,"region":150,"url":399,"description":400,"useCases":401,"indexable":231},"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":143,"label":403,"issuer":404,"region":405,"url":406,"description":407,"useCases":408,"indexable":231},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":142,"label":410,"issuer":411,"region":273,"url":412,"description":413,"useCases":414,"indexable":231},"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":416,"label":417,"issuer":149,"region":150,"url":418,"description":419,"useCases":420,"indexable":231},"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":144,"label":422,"issuer":423,"region":150,"url":424,"description":425,"useCases":426,"indexable":231},"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":428,"label":429,"issuer":430,"region":150,"url":431,"description":432,"useCases":433,"indexable":231},"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":435,"label":436,"issuer":437,"region":438,"url":439,"description":440,"useCases":441,"indexable":231},"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":443,"label":444,"issuer":445,"region":438,"url":446,"description":447,"useCases":448,"indexable":231},"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":450,"label":451,"issuer":452,"region":405,"url":453,"description":454,"useCases":455,"indexable":231},"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":457,"label":458,"issuer":459,"region":273,"url":460,"description":461,"useCases":455,"indexable":231},"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":145,"label":463,"issuer":464,"region":150,"url":161,"description":465,"useCases":466,"indexable":231},"UK Algorithmic Transparency Recording Standard","UK Government","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":468,"label":469,"issuer":470,"region":405,"url":471,"description":472,"useCases":473,"indexable":231},"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":475,"label":476,"issuer":149,"region":150,"url":477,"description":478,"useCases":479,"indexable":231},"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":481,"label":482,"issuer":149,"region":150,"url":483,"description":484,"useCases":479,"indexable":231},"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":486,"label":487,"issuer":488,"region":273,"url":489,"description":490,"useCases":491,"indexable":231},"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":493,"label":494,"issuer":149,"region":150,"url":495,"description":496,"useCases":497,"indexable":231},"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":499,"label":500,"issuer":501,"region":273,"url":502,"description":503,"useCases":497,"indexable":231},"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":505,"label":506,"issuer":507,"region":405,"url":508,"description":509,"useCases":497,"indexable":231},"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":511,"label":512,"issuer":149,"region":150,"url":513,"description":514,"useCases":515,"indexable":231},"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":517,"label":518,"issuer":519,"region":273,"url":520,"description":521,"useCases":515,"indexable":231},"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":523,"label":524,"issuer":437,"region":438,"url":525,"description":526,"useCases":527,"indexable":231},"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":529,"label":530,"issuer":149,"region":150,"url":531,"description":532,"useCases":527,"indexable":231},"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":534,"label":535,"issuer":149,"region":150,"url":536,"description":537,"useCases":527,"indexable":231},"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":539,"label":540,"issuer":541,"region":150,"url":542,"description":543,"useCases":544,"indexable":231},"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":546,"label":547,"issuer":548,"region":273,"url":549,"description":550,"useCases":551,"indexable":231},"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":553,"label":554,"issuer":149,"region":150,"url":555,"description":556,"useCases":551,"indexable":231},"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":558,"label":559,"issuer":149,"region":150,"url":560,"description":561,"useCases":338,"indexable":231},"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":563,"label":564,"issuer":565,"region":566,"url":567,"description":568,"useCases":316,"indexable":231},"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":570,"label":571,"issuer":572,"region":150,"url":573,"description":574,"useCases":362,"indexable":231},"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":576,"label":577,"issuer":578,"region":150,"url":579,"description":580,"useCases":362,"indexable":231},"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":582,"label":583,"issuer":584,"region":438,"url":585,"description":586,"useCases":587,"indexable":231},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",3,{"id":589,"label":590,"issuer":149,"region":150,"url":591,"description":592,"useCases":587,"indexable":231},"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":594,"label":595,"issuer":596,"region":273,"url":597,"description":598,"useCases":587,"indexable":231},"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.",1790598301910]