[{"data":1,"prerenderedAt":550},["ShallowReactive",2],{"uc-ai-tenant-screening-with-fair-housing-safeguards":3,"uc-regulations":329},{"useCase":4,"evidence":189,"blitsAiDeployments":231,"benchmarks":232,"indicative":233,"related":236,"indexability":327,"includeUnpublished":195},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":37,"valueDrivers":38,"kpis":42,"indicativeValue":46,"macroEstimates":67,"feasibility":73,"implementation":86,"risk":127,"blitsAi":167,"faq":169,"related":182,"datePublished":184,"dateModified":184,"lastVerified":184,"changelog":185,"slug":188},"AI tenant screening with fair housing safeguards","AI tenant screening","AI tenant screening: fair housing safeguards","RealPage says CF Real Estate reported 75% fewer skips and evictions with AI Screening. SafeRent settled a voucher bias suit for $2.275 million.","published","A machine learning model that scores a rental applicant's likelihood of paying rent reliably, from credit, rental payment history, eviction records and income, to help a landlord decide whether to accept, decline or ask for a higher deposit, built and operated so the score and the process around it do not produce a disparate impact on people protected by fair housing law.",[12,13,14,15],"AI rental applicant scoring","automated tenant screening","tenant risk scoring algorithm","AI screening for landlords",[17],"real-estate",[19,20],"risk-management","operations",[22,23],"prediction-and-scoring","classification-and-routing",[25,26],"internal-tools","api","back-office","supervised-agent","mainstream","Landlords and property managers have always screened applicants on credit and criminal history,\nbut traditional screening asks whether an applicant is *able* to pay, not whether they are\n*willing* to prioritize rent over other bills. RealPage, whose AI Screening product is built on a\ndatabase of more than 36 million lease outcomes, argues that past rental payment behaviour predicts\nfuture payment better than a credit score alone, and that conventional screening turns away\nreliable renters who happen to have a thin or damaged credit file.\n\nThe stakes of getting the score wrong fall unevenly. A tenant screening algorithm decides who gets\na home, and the same automation that promises lower vacancy losses and faster decisions can encode\nbias against a group already disadvantaged in credit history, which the Fair Housing Act's ban on\nrace based discrimination does not allow, whoever or whatever makes the decision. In Louis v.\nSafeRent Solutions, a federal court denied the defendants' motion to dismiss in July 2023, holding\nthat the plaintiffs had adequately alleged that a screening score had a disparate impact on Black\nand Hispanic applicants who used housing vouchers; the case then settled for $2.275 million with\ninjunctive relief in November 2024. Voucher, or source of income, status is not itself a protected\nclass under the federal Fair Housing Act; where it is protected, that protection comes from state or\nlocal law.",[32],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"When a housing voucher is used, on average over 73% of the monthly rental payment is paid by public housing authorities directly to housing providers, a fact the SafeRent Score's algorithm did not factor into the applicant's score, according to the plaintiffs' complaint in Louis v. SafeRent Solutions.","Louis, et al. v. SafeRent Solutions, et al.","https://www.cohenmilstein.com/case-study/louis-et-al-v-saferent-solutions-et-al/",2022,"1. **Collect the application data.** Credit history, rental payment history, eviction and criminal\n   records, income and, for voucher holders, the voucher amount, come from the applicant and third\n   party data providers.\n2. **Score the application.** A model trained on historical lease outcomes (paid on time, skipped,\n   evicted) produces a score or a recommendation, weighing payment behaviour, income stability and\n   credit history against the outcomes it has learned to predict.\n3. **Recommend a decision.** The score feeds a threshold the property manager sets (approve,\n   approve with conditions such as a higher deposit or a guarantor, or decline), balancing\n   occupancy targets against payment risk.\n4. **Give the required notice.** Where the score contributes to a decline or a less favourable\n   term, the applicant gets an adverse action notice under the Fair Credit Reporting Act, naming\n   the screening company and stating the right to a free copy of the report and to dispute it.\n5. **Monitor for disparate impact.** The score and its outcomes are tested periodically against\n   protected characteristics, including voucher status where that is a protected class locally, and\n   the model or its inputs are adjusted when a pattern of disparate impact appears.",[39,40,41],"risk-reduction","cost-to-serve","compliance",[43,44,45],"delinquency-rate-reduction","cost-savings","approval-rate-uplift",{"referenceOrg":47,"inputs":48,"formula":62,"currency":63,"period":64,"resultLabel":65,"caveat":66},"A multifamily operator with 40,000 apartment homes",[49,55],{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54},"units","Apartment homes managed",40000,"homes","The reference operator, matching the scale RealPage's own case example uses.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"savingsPerUnit","Net operating income gained per unit per year from reduced non payment and default risk",31,39,"USD per unit per year","RealPage reports AI Screening delivered an average savings of $39 per unit per year in the first full year of results in the field, exceeding an earlier, pre launch forecast of $31 per unit by 25%. Vendor reported, one platform, not independently audited.","units * savingsPerUnit","USD","per year","Net operating income gained from better screening","RealPage's own reported average from the first full year of AI Screening in the field, not an independent study; the blog points to a white paper for methodology that is not cited here. It leaves out the cost of the screening service, of any compliance and testing programme, and of the harm and legal exposure a biased score can create.",[68],{"statement":69,"sourceTitle":70,"sourceUrl":71,"year":72},"One national REIT reported saving $1.1 million across its 52,000 units after adopting RealPage AI Screening, with early lease terminations down 24% year over year and skips and evictions down 38%; across 50,000 same store leases representing 487 unique assets and 130,000 units, RealPage reports AI Screening drove a 10.2% reduction in the average balance owed per move in.","From \"Ability to Pay\" to \"Willingness to Pay\": AI-Based Tenant Screening is Boosting NOI","https://www.realpage.com/blog/ai-tenant-screening-is-boosting-noi/",2021,{"complexity":74,"complexityNote":75,"dataPrerequisites":76,"integrations":81},"high","The scoring model itself is usually bought from a specialist vendor, not built in house, but fitting it into a compliant process is hard: adverse action notices, dispute handling under the Fair Credit Reporting Act, and a genuine, tested process for detecting and correcting disparate impact by protected class and, in many US jurisdictions, by voucher or source of income status.",[77,78,79,80],"Historical lease outcomes (paid on time, late, skipped, evicted) to train or validate the model","Credit, criminal, eviction and income data from applicants and screening bureaus","A record of protected characteristics or proxies for fair housing testing, held separately from the scoring process","Published, consistent approval thresholds and override rules",[82,83,84,85],"Property management system for applications and lease data","Credit bureau and eviction and criminal record data providers","Adverse action notice generation and delivery","A compliance dashboard for periodic disparate impact testing",{"steps":87,"guardrails":103,"humanInTheLoop":108,"kpisToInstrument":109,"failureModes":114},[88,91,94,97,100],{"title":89,"detail":90},"Separate the score from the decision","Treat the model's output as one input to a documented decision policy with human set thresholds, not as the decision itself, so the policy, not only the model, can be tested and changed.",{"title":92,"detail":93},"Test for disparate impact before and after launch, and on a schedule","Run the score against a protected class and source of income breakdown of applicants before launch and on a recurring schedule after, using a method a fair housing lawyer has reviewed; the SafeRent case shows courts will look at outcomes, not intent.",{"title":95,"detail":96},"Do not let the score penalize what it does not understand","A voucher, a co signer or a security deposit changes what an applicant will actually pay each month. Confirm the score or the policy around it accounts for these, rather than scoring on raw income and credit history alone.",{"title":98,"detail":99},"Automate the adverse action notice, do not skip it","Every decline or less favourable term that relies on a consumer report needs a compliant Fair Credit Reporting Act notice naming the screening company and stating the right to a free copy of the report and to dispute it; generate it automatically from the same data the score used.",{"title":101,"detail":102},"Give applicants and staff a way to challenge a score","A documented override and appeal path, reviewed by a person, catches cases the model gets wrong and creates a record that the process is genuinely supervised.",[104,105,106,107],"A human set, documented decision policy sits between the score and the outcome","Periodic disparate impact testing by protected characteristic and source of income, with a named owner","Automated, compliant adverse action notices for every unfavourable decision that uses a consumer report","A logged override and appeal path for applicants and staff","Leasing staff and a compliance or fair housing officer own the approval policy, the thresholds and every override; the model informs, it does not decide alone. A cross functional review, including legal or compliance, signs off on the model or policy before launch and on any material change.",[110,111,112,113],"Approval rate and score distribution by protected characteristic and by source of income status","Delinquency, skip and eviction rate versus the pre AI Screening baseline, on comparable properties","Override rate and outcomes of appealed decisions","Adverse action notices sent on time and disputes received",[115,118,121,124],{"title":116,"detail":117},"Disparate impact hiding in a proxy","A model can discriminate on a protected characteristic through a correlated input, such as credit history, without ever using that characteristic directly. The SafeRent case turned on exactly this. Test outcomes, not just inputs.",{"title":119,"detail":120},"Voucher income treated as if it were not real","Scoring only the tenant's own income when a housing authority pays most of the rent directly undercounts a reliable payer. Model or policy the voucher payment explicitly.",{"title":122,"detail":123},"A frozen threshold in a changing market","An approval threshold tuned for one market or one point in the cycle keeps rejecting good applicants, or keeps approving bad risk, as conditions change. Revisit thresholds on a schedule against real outcomes.",{"title":125,"detail":126},"No one can explain a decline","Staff cannot tell an applicant why they were declined beyond \"the system said so\", which leaves the applicant unable to challenge what drove the decision and weakens the override and appeal path. Keep the score's reasons in a form a person can read and repeat.",{"euAiAct":128,"regulations":130,"guidance":134,"controls":157,"incidents":163},{"tier":74,"basis":129},"A score used to decide whether a natural person is offered housing evaluates creditworthiness in substance, which is the likely reading of Annex III point 5(b) when the outcome is a rental decision rather than a loan, though the annex text itself only names creditworthiness evaluation and credit scoring. The provider of such a high risk system carries risk management, data governance and conformity assessment obligations; the deployer, the landlord or property manager, must use it according to its instructions, ensure human oversight, monitor its operation and keep logs under Article 26, and, where it falls under Annex III point 5(b), carry out a fundamental rights impact assessment under Article 27.",[131,132,133],"eu-ai-act","gdpr","us-fcra",[135,141,146,151],{"title":136,"issuer":137,"region":138,"url":139,"note":140},"42 U.S. Code 3604, discrimination in the sale or rental of housing (Fair Housing Act)","United States Congress","north-america","https://www.law.cornell.edu/uscode/text/42/3604","Prohibits discrimination in the terms, conditions or provision of a rental based on a protected characteristic. In Louis v. SafeRent Solutions, a federal court held in July 2023 that a tenant screening company is subject to this Act even though it is not itself a landlord, and let the claims against the landlord proceed too.",{"title":142,"issuer":143,"region":138,"url":144,"note":145},"Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","Governs the accuracy, use and disclosure of consumer reports, including tenant screening reports, and requires an adverse action notice when a report contributes to a decline or a less favourable term.",{"title":147,"issuer":148,"region":138,"url":149,"note":150},"HUD Issues Fair Housing Act Guidance on Applications of Artificial Intelligence (May 2024, archived)","US Department of Housing and Urban Development","https://archives.hud.gov/news/2024/pr24-098.cfm","In May 2024, HUD issued guidance on how the Fair Housing Act applies to tenant screening when AI and algorithms are used. The page was archived on February 3, 2025; the archive banner does not state that the guidance was withdrawn, and the underlying statute still applies.",{"title":152,"issuer":153,"region":154,"url":155,"note":156},"Annex III, high risk AI systems (point 5, essential private and public services)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Lists creditworthiness evaluation and credit scoring of natural persons as high risk.",[158,159,160,161,162],"Documented, human set decision policy separate from the raw score","Recurring disparate impact testing by protected characteristic and source of income status, reviewed by compliance","Automated Fair Credit Reporting Act adverse action notices naming the screening company, with free report and dispute rights","Logged overrides, appeals and their outcomes","Change control and retesting whenever the model, its inputs or the approval thresholds change",[164],{"title":165,"url":35,"note":166},"Louis v. SafeRent Solutions, tenant screening algorithm settlement","Plaintiffs alleged that SafeRent's tenant screening score gave disproportionately low scores to Black and Hispanic applicants using housing vouchers because its algorithm did not account for the portion of rent a housing authority pays directly, in breach of the Fair Housing Act. A federal court held in July 2023 that a screening company can be subject to the Fair Housing Act even though it is not a landlord, and granted final approval of a $2.275 million settlement with injunctive relief in November 2024, including a requirement that SafeRent stop using its scoring algorithm for voucher holders' applications in Massachusetts.",{"howToBuild":168},"Blits.ai does not build or train the credit and rental risk scoring model itself; that model\nnormally comes from a specialist screening bureau or an in house data science team. What\nBlits.ai is well placed to build is the compliant workflow around an existing score: a\n**custom function** calls the screening provider's API with the applicant's consented data and\nreceives the score and its stated reasons back as structured data. An **agentic workflow** applies\nthe property's documented decision policy to that score, and **human in the loop confirmation**\nholds any decline or unfavourable term for a person to confirm before the adverse action notice is\ndrafted and sent, generated as **structured output** rather than free text so every reason stated\nis one the score actually produced.\n\n**Guardrails** check the workflow's drafted notices and messages before they go out, and a\nscheduled **monitor** runs recurring health checks against the bot and alerts a compliance owner\non failure; the disparate impact testing itself, and reporting approval or override rates by\nsegment, is done outside the platform, on data exported from **run history**. **PII masking**\nprotects applicant data in prompts and logs, and the full **run history** gives an auditable trail\nof every score, policy application and human confirmation. The platform is **model agnostic** and\nsupports **EU and UAE data residency** where applicant data must stay in region.",[170,173,176,179],{"question":171,"answer":172},"Does AI tenant screening actually reduce losses?","RealPage reports that its AI Screening delivered an average savings of $39 per unit per year in the first full year of results in the field, and that CF Real Estate reported a 75% drop in skips and evictions after implementing it. These are RealPage's own reported results, the $39 an average it gives with no disclosed method and the 75% one customer's report, not an independent audit.",{"question":174,"answer":175},"Can a tenant screening algorithm discriminate even without meaning to?","Yes. In Louis v. SafeRent Solutions, a federal court allowed claims to proceed that a screening score's algorithm, by not accounting for the share of rent a housing authority pays directly for voucher holders, produced a disparate impact on Black and Hispanic applicants, and the case settled for $2.275 million with injunctive relief. Fair housing law looks at the outcome, not whether the algorithm's designer intended it.",{"question":177,"answer":178},"Is a landlord responsible if a third party algorithm makes the screening decision?","In Louis v. SafeRent Solutions, a federal court in Massachusetts held in July 2023 that the screening company was subject to the Fair Housing Act even though it is not itself a landlord, and let the Fair Housing Act claims against the landlord proceed too. Keep a human set decision policy and a documented override path so no single score alone determines the outcome.",{"question":180,"answer":181},"What is the difference between this and an AI leasing chatbot?","A leasing agent answers questions, books tours and takes maintenance requests, and should stay out of screening and approval decisions entirely. This use case is the scoring and decision system itself, which carries the fair housing and credit reporting risk that a leasing chatbot is built specifically to avoid.",[183],"apartment-leasing-and-resident-service-agent","2026-09-29",[186],{"date":184,"note":187},"First published","ai-tenant-screening-with-fair-housing-safeguards",[190,215],{"title":191,"useCases":192,"organization":193,"vendors":197,"summary":201,"stage":202,"year":72,"channels":203,"languages":204,"metrics":206,"outcomeDisclosed":207,"sources":208,"verification":210,"grade":212,"id":213,"organizationSlug":214},"CF Real Estate: RealPage AI Screening",[188],{"name":194,"anonymized":195,"country":196,"region":138,"industry":17},"CF Real Estate",false,"US",[198],{"name":199,"role":200},"RealPage","platform","CF Real Estate implemented RealPage AI Screening, which scores rental applicants on their predicted willingness to pay rent using a database of more than 36 million lease outcomes rather than credit history alone. RealPage's own blog reports that CF Real Estate reported a 75% drop in skips and evictions after adopting the tool, as part of a wider set of customer results RealPage says followed the first full year of AI Screening being in the field. Skips and evictions are a broader measure than the standard 30 day delinquency rate, so this figure is not recorded as a delinquency reduction metric below.","production",[25],[205],"en",[],true,[209],{"url":71,"title":70,"publisher":199},{"level":211,"checkedAt":184},"source-verified","C","cf-real-estate-ai-tenant-screening",null,{"title":216,"useCases":217,"organization":218,"vendors":220,"summary":222,"stage":202,"year":223,"channels":224,"languages":225,"metrics":226,"outcomeDisclosed":207,"sources":227,"verification":229,"grade":212,"id":230,"organizationSlug":214},"JVM: RealPage AI Screening through the pandemic",[188],{"name":219,"anonymized":195,"country":196,"region":138,"industry":17},"JVM",[221],{"name":199,"role":200},"RealPage's own blog quotes Kortney Balas, described as JVM's Vice President of Information Management, saying the Covid 19 pandemic was a major test of AI Screening's ability to predict a renter's willingness to prioritize rent over other bills, and that JVM ended April at an improved 1.7% delinquency rate with AI Screening in place. The source names the customer only as \"JVM\"; it is not independently confirmed which company this is.",2020,[25],[205],[],[228],{"url":71,"title":70,"publisher":199},{"level":211,"checkedAt":184},"jvm-realty-ai-tenant-screening",0,[],{"low":234,"high":235},1240000,1560000,[237,259,277,297],{"slug":183,"title":238,"shortTitle":239,"definition":240,"status":9,"industries":241,"functions":242,"patterns":245,"audience":250,"autonomy":28,"adoptionStage":251,"evidenceCount":252,"publicEvidenceCount":252,"organizations":253,"bestGrade":257,"headline":214,"lastVerified":258,"indexable":207},"AI agent for apartment leasing inquiries and resident service","Leasing and resident service agent","An AI agent that answers rental prospects and residents by chat, text, email and phone for a property manager: it answers questions about apartments and policies, books tours, takes maintenance requests, sends renewal and payment reminders, and hands anything that needs judgment to leasing or service staff.",[17],[243,244,20],"customer-service","sales",[246,247,248,249],"conversational-agent","voice-agent","agentic-workflow","rag-knowledge-assistant","customer-facing","early-adopters",3,[254,255,256],"Asset Living","AvalonBay Communities","Equity Residential","B","2026-09-27",{"slug":260,"title":261,"shortTitle":262,"definition":263,"status":9,"industries":264,"functions":268,"patterns":269,"audience":27,"autonomy":271,"adoptionStage":251,"segment":27,"evidenceCount":272,"publicEvidenceCount":272,"organizations":273,"bestGrade":257,"headline":214,"lastVerified":258,"indexable":207},"settlement-fail-prediction-and-exception-management","AI for settlement fail prediction and post trade exception management","Settlement fail prediction","AI that scores each pending securities settlement instruction for its likelihood of failing, names the probable cause (unmatched instruction, wrong settlement details, lack of securities or cash), and helps operations teams work the exceptions and counterparty queries before the intended settlement date, so fewer trades fail and fewer late settlement penalties are paid.",[265,266,267],"capital-markets","banking","wealth-and-asset-management",[20,19],[22,23,248,270],"content-generation","copilot",4,[274,275,276],"BNY","Clearstream","Euroclear",{"slug":278,"title":279,"shortTitle":280,"definition":281,"status":9,"industries":282,"functions":286,"patterns":288,"audience":27,"autonomy":28,"adoptionStage":291,"segment":292,"evidenceCount":252,"publicEvidenceCount":252,"organizations":293,"bestGrade":257,"headline":214,"lastVerified":258,"indexable":207},"continuous-controls-testing","AI for continuous controls testing and control self assessment","Continuous controls testing","AI that moves control testing from periodic samples to continuous, full population assurance: it collects evidence from source systems, maps each artefact to the control it supports, tests every transaction or record against the control's rule, flags exceptions for a human to judge and prepares the risk and control self assessment from incident and loss data for the business to review.",[283,266,284,265,285],"cross-industry","insurance","government",[19,287,20],"regulatory-compliance",[248,289,290,23],"document-processing","anomaly-detection","emerging","second-line",[294,295,296],"Federal Deposit Insurance Corporation","U.S. Department of the Interior","Pension Benefit Guaranty Corporation",{"slug":298,"title":299,"shortTitle":300,"definition":301,"status":9,"industries":302,"functions":306,"patterns":308,"audience":310,"autonomy":271,"adoptionStage":251,"evidenceCount":311,"publicEvidenceCount":312,"organizations":313,"bestGrade":257,"headline":320,"lastVerified":258,"indexable":207},"aiops-incident-triage","AI for IT incident triage and root cause analysis (AIOps)","AIOps incident triage","AI that turns a flood of monitoring alerts into one probable incident, routes it to the right team, proposes likely root causes and remediation from runbooks and past incidents, and drafts the stakeholder updates and the post incident review, while an engineer authorizes every change.",[283,266,303,304,305],"technology","telecommunications","payments",[307,20,19],"it-and-engineering",[290,23,309,249,248],"summarization","employee-facing",7,6,[314,315,316,317,318,319],"Coinbase","Google","Meta","Microsoft","Mizuho Financial Group","TD Bank",{"kpi":321,"label":322,"unit":323,"n":252,"nUpTo":231,"kind":324,"value":325,"qualifier":326,"claimant":214,"organization":214,"vendorReported":195},"accuracy","Accuracy","percent","median",90,"exact",{"indexable":207,"reasons":328},[],[330,335,340,348,355,362,368,375,383,390,397,404,410,416,423,430,436,443,449,455,461,468,473,480,485,490,495,501,503,508,516,522,528,534,539,544],{"id":131,"label":331,"issuer":153,"region":154,"url":332,"description":333,"useCases":334,"indexable":207},"EU AI Act","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.",230,{"id":132,"label":336,"issuer":153,"region":154,"url":337,"description":338,"useCases":339,"indexable":207},"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.",207,{"id":341,"label":342,"issuer":343,"region":344,"url":345,"description":346,"useCases":347,"indexable":207},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":349,"label":350,"issuer":351,"region":138,"url":352,"description":353,"useCases":354,"indexable":207},"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.",92,{"id":356,"label":357,"issuer":358,"region":154,"url":359,"description":360,"useCases":361,"indexable":207},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":363,"label":364,"issuer":153,"region":154,"url":365,"description":366,"useCases":367,"indexable":207},"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":369,"label":370,"issuer":371,"region":154,"url":372,"description":373,"useCases":374,"indexable":207},"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.",50,{"id":376,"label":377,"issuer":378,"region":379,"url":380,"description":381,"useCases":382,"indexable":207},"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.",37,{"id":384,"label":385,"issuer":386,"region":379,"url":387,"description":388,"useCases":389,"indexable":207},"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":391,"label":392,"issuer":393,"region":138,"url":394,"description":395,"useCases":396,"indexable":207},"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.",22,{"id":398,"label":399,"issuer":400,"region":344,"url":401,"description":402,"useCases":403,"indexable":207},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":405,"label":406,"issuer":153,"region":154,"url":407,"description":408,"useCases":409,"indexable":207},"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.",17,{"id":411,"label":412,"issuer":413,"region":154,"url":414,"description":415,"useCases":409,"indexable":207},"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.",{"id":417,"label":418,"issuer":419,"region":138,"url":420,"description":421,"useCases":422,"indexable":207},"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.",16,{"id":424,"label":425,"issuer":426,"region":344,"url":427,"description":428,"useCases":429,"indexable":207},"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":431,"label":432,"issuer":153,"region":154,"url":433,"description":434,"useCases":435,"indexable":207},"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":437,"label":438,"issuer":439,"region":138,"url":440,"description":441,"useCases":442,"indexable":207},"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":444,"label":445,"issuer":446,"region":138,"url":447,"description":448,"useCases":442,"indexable":207},"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":450,"label":451,"issuer":153,"region":154,"url":452,"description":453,"useCases":454,"indexable":207},"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":456,"label":457,"issuer":458,"region":344,"url":459,"description":460,"useCases":454,"indexable":207},"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":462,"label":463,"issuer":464,"region":138,"url":465,"description":466,"useCases":467,"indexable":207},"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.",11,{"id":469,"label":470,"issuer":153,"region":154,"url":471,"description":472,"useCases":467,"indexable":207},"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.",{"id":474,"label":475,"issuer":476,"region":154,"url":477,"description":478,"useCases":479,"indexable":207},"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.",10,{"id":481,"label":482,"issuer":378,"region":379,"url":483,"description":484,"useCases":479,"indexable":207},"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.",{"id":486,"label":487,"issuer":153,"region":154,"url":488,"description":489,"useCases":479,"indexable":207},"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":491,"label":492,"issuer":153,"region":154,"url":493,"description":494,"useCases":479,"indexable":207},"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":496,"label":497,"issuer":153,"region":154,"url":498,"description":499,"useCases":500,"indexable":207},"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.",9,{"id":133,"label":142,"issuer":143,"region":138,"url":144,"description":502,"useCases":311,"indexable":207},"US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":504,"label":505,"issuer":153,"region":154,"url":506,"description":507,"useCases":312,"indexable":207},"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":509,"label":510,"issuer":511,"region":512,"url":513,"description":514,"useCases":515,"indexable":207},"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.",5,{"id":517,"label":518,"issuer":519,"region":154,"url":520,"description":521,"useCases":272,"indexable":207},"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":523,"label":524,"issuer":525,"region":154,"url":526,"description":527,"useCases":272,"indexable":207},"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":529,"label":530,"issuer":531,"region":379,"url":532,"description":533,"useCases":252,"indexable":207},"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":535,"label":536,"issuer":153,"region":154,"url":537,"description":538,"useCases":252,"indexable":207},"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":540,"label":541,"issuer":153,"region":154,"url":542,"description":543,"useCases":252,"indexable":207},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":545,"label":546,"issuer":547,"region":138,"url":548,"description":549,"useCases":252,"indexable":207},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683494465]