[{"data":1,"prerenderedAt":553},["ShallowReactive",2],{"uc-child-welfare-referral-risk-triage":3,"uc-regulations":326},{"useCase":4,"evidence":178,"blitsAiDeployments":235,"benchmarks":236,"indicative":237,"related":240,"indexability":324,"includeUnpublished":184},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":23,"audience":25,"autonomy":26,"adoptionStage":27,"segment":28,"problem":29,"problemStats":30,"howItWorks":36,"valueDrivers":37,"kpis":40,"indicativeValue":45,"macroEstimates":79,"feasibility":80,"implementation":91,"risk":135,"blitsAi":148,"faq":150,"related":163,"datePublished":166,"dateModified":167,"lastVerified":166,"changelog":168,"slug":177},"AI risk scoring in child welfare intake and investigations","Child welfare risk scoring","AI risk scoring in child welfare cases","AI risk scores help child welfare staff decide. Allegheny County scores referrals at intake, Los Angeles County scores open investigations; staff keep the decision.","published","A predictive model that scores a family's risk in the child welfare system, using case history and administrative data, so that staff see a consistent, data informed signal alongside their own judgment, whether that is a call screener and supervisor deciding at referral whether to open an investigation, or a supervisor and social worker deciding how to respond early in one that is already open. The model informs the decision; it does not make it.",[12,13,14,15,16],"child abuse risk scoring","predictive risk modeling in child welfare","child protection referral triage","family screening tool","child welfare intake risk model",[18],"government",[20],"case-management",[22],"prediction-and-scoring",[24],"internal-tools","employee-facing","assist","emerging","children's social care","Child welfare agencies receive a very large number of reports on a hotline, online or in person.\nA call screener has to decide whether to open an investigation, and once an investigation is open,\na supervisor and social worker have only a short window to assess safety and plan a response. Los\nAngeles County's hotline alone received 168,045 calls in 2021, and about 750 emergency response\nsocial workers completed 43,505 investigations involving 86,487 children. Neither a missed risk\nnor an unnecessary investigation is a small thing: one can leave a child unprotected, the other can\nput a family through a distressing and stigmatizing process for nothing.\n\nPredictive risk models built from case history try to give staff a consistent, data informed\nsignal alongside the referral or the open case, whether at the point a screener decides to open an\ninvestigation or in the early days of one that is already open. The same idea has drawn serious\nscrutiny: a model trained on historic investigation and removal decisions can encode whatever bias\nsat inside those decisions, and a family cannot see or challenge a score the way they can challenge\na person's stated reasoning. Allegheny County, whose tool scores referrals at intake, commissioned\nindependent process and impact evaluations as part of the tool's rollout and published its\nmethodology. Los\nAngeles County, whose tool supports investigations that are already open, published its own\nmethodology report, implementation insights and quantitative data for investigations, and built a\nRacial Feedback Equity Loop to check for disproportionate impact on African American families.",[31],{"statement":32,"sourceTitle":33,"sourceUrl":34,"year":35},"In 2021, Los Angeles County's Child Protection Hotline received 168,045 child abuse and neglect calls, and about 750 emergency response social workers completed 43,505 investigations involving 86,487 children in the county.","DCFS Shares Risk Stratification Pilot Insights, Data Online","https://lacounty.gov/2022/08/29/dcfs-shares-risk-stratification-pilot-insights-data-online/",2022,"1. **A referral arrives.** A report of suspected abuse or neglect comes in by phone, online or in\n   person to the agency's intake or hotline service.\n2. **The system pulls linked history.** Using an integrated data warehouse, the model draws in\n   the family's and any named adults' history across child welfare and, where lawfully available,\n   other public systems.\n3. **The model scores the case.** A risk score estimates the likelihood of a defined future harm\n   or need, based on patterns in that linked history.\n4. **Staff see the score alongside the case.** Depending on the deployment, that is a call\n   screener and supervisor deciding whether to open an investigation, or a supervisor and social\n   worker already working an open one. The score is presented alongside, never instead of, the\n   referral or case file and the worker's own read of it.\n5. **A person decides.** The screener and supervisor decide whether to open a formal investigation,\n   offer services or referrals, or take no further action; or, once an investigation is open, the\n   supervisor and social worker decide how to respond and plan services. Either way, the reasoning\n   is recorded.\n6. **The decision and the score are logged and reviewed.** Outcomes are tracked over time,\n   including by protected characteristics, to check the model and the process for disparate impact.",[38,39],"risk-reduction","employee-productivity",[41,42,43,44],"detection-rate-improvement","false-positive-reduction","error-reduction","productivity-gain",{"referenceOrg":46,"inputs":47,"formula":74,"currency":75,"period":76,"resultLabel":77,"caveat":78},"A child welfare agency handling 40,000 referrals a year",[48,54,61,68],{"key":49,"label":50,"low":51,"high":51,"unit":52,"note":53},"referrals","Referrals screened per year",40000,"referrals per year","Editorial assumption for a large county agency, replace with your own referral or call volume. Not anchored to a specific organization on this page, because Allegheny County and Los Angeles County publish figures at different stages (calls, referrals and investigations) that are not directly comparable.",{"key":55,"label":56,"low":57,"high":58,"unit":59,"note":60},"screeningMinutesPerReferral","Screener and supervisor minutes per referral",20,45,"minutes per referral","Editorial assumption for the time to review a referral with its history. Replace with your own time and motion data.",{"key":62,"label":63,"low":64,"high":65,"unit":66,"note":67},"timeSavedShare","Share of screening time saved by having the score and linked history ready",0.05,0.15,"fraction of screening time","Conservative editorial assumption. Allegheny County's published impact evaluation measured effects on screening and case opening decisions, not on screening time, and neither organization on this page has published a measured screening time saving. Replace with your own measurement.",{"key":69,"label":70,"low":71,"high":58,"unit":72,"note":73},"costPerHour","Cost of a screener hour, fully loaded",30,"USD per hour","Editorial assumption for a fully loaded child welfare caseworker cost. Replace with your own.","referrals * (screeningMinutesPerReferral / 60) * timeSavedShare * costPerHour","USD","per year","Screener time cost avoided on referral triage","This values only time saved gathering and reviewing information for the decision. It leaves out the cost of building, validating and continuously monitoring the model for bias. Allegheny County commissioned an independent impact evaluation of its tool (Jeremy Goldhaber-Fiebert, Stanford University, published by Allegheny County DHS, April 2019), which found the AFST increased the accuracy of decisions to advance children to investigation and reduced disparities in case opening rates between Black and white children, but found \"no evidence that the AFST resulted in greater screening consistency\" within individual call screeners. Los Angeles County has not published a comparable before and after evaluation of its own tool's effect on decisions.",[],{"complexity":81,"complexityNote":82,"dataPrerequisites":83,"integrations":87},"high","The model needs years of linked, high quality administrative data across child welfare and, where lawful, related systems, a validation and fairness evaluation before it ever sees a live referral, and an ongoing monitoring program, because an error in this domain can mean a missed protection concern on one side or an unnecessary investigation of a family on the other.",[84,85,86],"Multiple years of linked case history across child welfare, ideally in an integrated data warehouse","A validated method for joining records to the same family or individual reliably","An independent validation and ethical review completed before any live use",[88,89,90],"Case management system used by call screeners, investigators and supervisors","The agency's data warehouse or integration layer linking welfare and related records","Fairness and outcome monitoring reporting for the model's owner",{"steps":92,"guardrails":111,"humanInTheLoop":116,"kpisToInstrument":117,"failureModes":122},[93,96,99,102,105,108],{"title":94,"detail":95},"Commission an independent evaluation before any live score","Have the model, its data and its likely disparate impact validated by an outside evaluator on historic data, and publish the methodology, before a screener ever sees a live score.",{"title":97,"detail":98},"Present the score next to the referral, never instead of it","Show screeners the score and, where possible, the reasons behind it, alongside the full referral and case history, so it is one input among several, not a replacement for reading the case.",{"title":100,"detail":101},"Write a decision rule that keeps a person accountable","Require a screener and a supervisor to make and record the screen in or screen out decision and their reasoning, and make it normal, not exceptional, for staff to depart from the score.",{"title":103,"detail":104},"Build a fairness feedback loop from day one","Track outcomes by race and other protected characteristics, and act on what they show rather than only reporting them. Los Angeles County's Racial Feedback Equity Loop uses data this way to identify screening practices and community reporting patterns that may result in unnecessary investigations disproportionately burdening African American families.",{"title":106,"detail":107},"Publish what you can","Release what you can. Allegheny County commissioned independent process and impact evaluations and published its methodology; Los Angeles County published its methodology report, implementation insights and quantitative data for investigations. Either practice lets families, advocates and researchers hold the program to account.",{"title":109,"detail":110},"Treat a pilot as a pilot before scaling","Gather at least a year of paired decisions and outcomes, in a limited number of offices, before deciding whether to expand, and set out in advance what would justify not expanding.",[112,113,114,115],"A screener and a supervisor make and record every screen in or screen out decision; the score informs it, it does not make it","Independent validation and an ethical review completed before the model sees a live referral","Regular fairness monitoring by race and other protected characteristics, feeding back into the model or the process","Published methodology and, where lawful, anonymized outcome data for external scrutiny","A call screener and a supervisor decide whether to open an investigation, offer services, or take no further action, using the score alongside the full referral, the family's case history and their own professional judgment; where a tool operates inside an already open investigation, a supervisor and social worker use the score the same way to plan the response. Allegheny County describes its tool as designed \"to support, not replace, professional judgment,\" and its own impact evaluation found that for referrals scoring in the AFST's \"mandatory\" screen in range, which is meant to prompt a screen in decision, only 61 percent were in fact screened in between December 2016 and November 2018, evidence that screeners kept and used their discretion even at the top of the score range.",[118,119,120,121],"Screen in and screen out rates against the model's score, including how often and why staff depart from it","Repeat referral or subsequent investigation rates for families who were screened out","Fairness metrics across race and other protected characteristics, tracked over time","Time from referral to decision",[123,126,129,132],{"title":124,"detail":125},"The score becomes the decision in practice","Under caseload pressure, screeners start following the score without engaging with the underlying referral, so human oversight exists on paper only. Audit a sample of decisions against the full case, not just the score used.",{"title":127,"detail":128},"Historic bias in the training data becomes bias in the score","A model trained on past investigation or removal decisions can reproduce whatever bias sat inside those decisions, at scale. Validate and monitor by protected characteristic before launch and continuously after it.",{"title":130,"detail":131},"A population level signal gets read as a finding about one family","The score estimates statistical risk across similar cases; it is not evidence about what is actually happening in this family. Train staff explicitly on that distinction and require them to write their own reasoning, not just cite the score.",{"title":133,"detail":134},"No plan for when to stop","Predictive risk tools in this field draw close public and legal scrutiny. Decide in advance what evidence, such as an adverse fairness finding or an independent evaluation result, would trigger pausing or ending the tool, and who has the authority to do it.",{"euAiAct":136,"regulations":138,"guidance":141,"controls":142,"incidents":147},{"tier":81,"basis":137},"Annex III point 5(a): systems used by or on behalf of a public authority to evaluate a natural person's eligibility for essential public assistance benefits and services, or to grant, reduce, revoke or reclaim them. Recital 58 names \"social services providing protection\" among those essential services, and both tools sit inside exactly that: whether a family receives a child protective investigation and the services that can follow. Article 6(3) lets an Annex III system avoid the high risk tier when it only performs a narrow procedural task, improves a completed human decision, or is a preparatory step, but that exemption does not apply where the system performs profiling of natural persons, which this model does from personal and administrative data. So profiling closes the exemption; it is not a separate route into the high risk tier on its own.",[139,140],"eu-ai-act","nist-ai-rmf",[],[143,144,145,146],"Independent validation and periodic revalidation of the model, published where possible","A documented decision rule requiring a screener and a supervisor, with the score as one input among several","Regular fairness and outcome monitoring across protected characteristics, with a defined escalation and change process","A public, published methodology and, where lawful, anonymized outcome data",[],{"howToBuild":149},"On Blits.ai this sits alongside a case management system rather than inside a conversation: an\n**agentic workflow** with **custom functions** pulls the relevant, authorized case history for a\nreferral, presents it and the agency's own risk score together through a **flow** to the\nscreener, and requires **human in the loop approval**: a person must approve any workflow action\nabove a configured risk threshold, such as flagging a case for closer review, before it is\nrecorded. Every step keeps a **full audit trail**.\n\nBecause a model used this way needs independent validation and ongoing bias monitoring that sit\noutside a conversational AI platform, Blits.ai's role here is the workflow, presentation and\naudit layer around a risk score the agency validates and owns, not the model itself: **analytics**\nand exported run data support the fairness monitoring the agency runs, **PII masking** limits what\nof the underlying case data reaches any model call, and **EU and UAE data residency** keep it in\nregion where required. Where the agency's own scoring service sits outside Blits.ai, a **custom\nfunction** can call it; the platform's **model agnostic** routing covers the language model behind\nthe workflow and the presentation to the screener, not the risk score itself.",[151,154,157,160],{"question":152,"answer":153},"Does the AI decide whether to investigate a family?","No. At Allegheny County, a call screener and a supervisor decide whether to open an investigation, using the score alongside the full referral and case history; Allegheny describes the tool as designed \"to support, not replace, professional judgment.\" At Los Angeles County, the score reaches supervisors and social workers early in an investigation that is already open, to help shape the response, not a decision on whether to open it.",{"question":155,"answer":156},"How is bias in the risk score managed?","Allegheny County commissioned independent process and impact evaluations before and after launch. Los Angeles County built a Racial Feedback Equity Loop to identify screening practices and community reporting patterns that may result in unnecessary investigations disproportionately burdening African American families. Both agencies published their methodology rather than keeping it internal.",{"question":158,"answer":159},"Which agencies have deployed this, and how far?","Allegheny County, Pennsylvania has used the Allegheny Family Screening Tool in its child welfare intake office since August 2016. Los Angeles County piloted a separate risk stratification model in three regional offices starting August 2021; as of its August 2022 update, the county had not decided whether to expand it.",{"question":161,"answer":162},"Is this high risk under the EU AI Act?","Yes, under Annex III point 5(a): a system used by a public authority to evaluate eligibility for essential public assistance benefits and services, or to grant, reduce, revoke or reclaim them, which covers whether a family receives a child protective investigation and response. Because this model also profiles families from personal and administrative data, it cannot claim the Article 6(3) exemption some Annex III systems get for narrow procedural or preparatory tasks. Either way it brings requirements for risk management, data governance, logging, human oversight and conformity assessment, even though the final decision stays with a person.",[164,165],"social-worker-case-note-drafting","benefit-fraud-and-error-detection","2026-09-29","2026-09-30",[169,171,173,175],{"date":167,"note":170},"Published after review by an automated review workflow (independent skeptic review).",{"date":167,"note":172},"Editorial fix pass after adversarial review: corrected the EU AI Act basis to Annex III point 5(a) (essential public services and benefits) with Article 6(3) explained as an exemption the profiling clause closes rather than a separate high risk route, and rewrote FAQ 4 to match; removed the unsupported claim that Allegheny County's evaluations were a response to public scrutiny; removed the Children's Data Network vendor entry on the Los Angeles County evidence record (the source names it as a research partner, not a builder or integrator); aligned the screening consistency quote with its source wording; rephrased FAQ 3; tightened the blitsAi.howToBuild claims on model agnostic routing and PII masking; and added the AFST's \"mandatory\" score band screen in rate to the human in the loop description.",{"date":167,"note":174},"Unpublished by an automated review workflow (independent skeptic review).",{"date":166,"note":176},"First published","child-welfare-referral-risk-triage",[179,204],{"title":180,"useCases":181,"organization":182,"vendors":187,"summary":188,"stage":189,"year":190,"channels":191,"languages":192,"metrics":194,"outcomeDisclosed":184,"sources":195,"verification":199,"grade":201,"id":202,"organizationSlug":203},"Los Angeles County DCFS: risk stratification pilot for child welfare investigations",[177],{"name":183,"anonymized":184,"country":185,"region":186,"industry":18},"Los Angeles County Department of Children and Family Services",false,"US","north-america",[],"Los Angeles County's Department of Children and Family Services (DCFS) launched a risk stratification pilot in August 2021 in three regional offices (Belvedere, Lancaster and Santa Fe Springs). The model was developed following an analysis conducted by DCFS and university based researchers with the Children's Data Network, who established that a relatively small number of investigations show chronic patterns of alleged abuse or neglect that signal significant service needs for families. The tool delivers information to supervisors at the outset of an investigation that is already open, so they and social workers have it early, when they have only a short window to assess safety and plan services; it does not score incoming hotline calls or help decide whether to open an investigation. DCFS built a \"Racial Feedback Equity Loop\" into the model to identify screening practices and community reporting patterns that may result in unnecessary investigations disproportionately burdening African American families, and published a methodology report, implementation insights, quantitative data for investigations and an ethical review of the tool's use case rather than only an internal report. As of the source date, August 2022, DCFS had not decided whether to expand the model beyond the pilot offices.","pilot",2021,[24],[193],"en",[],[196],{"url":34,"title":33,"publisher":197,"date":198},"County of Los Angeles","2022-08-29",{"level":200,"checkedAt":166},"source-verified","B","los-angeles-county-dcfs-risk-stratification",null,{"title":205,"useCases":206,"organization":207,"vendors":209,"summary":213,"stage":214,"year":215,"channels":216,"languages":217,"metrics":218,"outcomeDisclosed":184,"sources":219,"verification":233,"grade":201,"id":234,"organizationSlug":203},"Allegheny County: Family Screening Tool for child welfare intake",[177],{"name":208,"anonymized":184,"country":185,"region":186,"industry":18},"Allegheny County Department of Human Services",[210],{"name":211,"role":212},"Auckland University of Technology, Centre for Social Data Analytics","integrator","Allegheny County's Department of Human Services (DHS) has used the Allegheny Family Screening Tool (AFST) in its child welfare intake office since August 2016 to support call screeners and their supervisors when a report of suspected child abuse or neglect comes in. The tool generates a risk score from data in the county's own data warehouse; staff use the score, alongside their own experience and clinical judgment, to decide whether to open a formal investigation, offer services or take no further action. DHS commissioned independent process, impact and ethical evaluations before and after go live and has since advised other jurisdictions considering similar tools. The 2019 impact evaluation, led by Jeremy Goldhaber-Fiebert of Stanford University, found the AFST increased the accuracy of decisions to advance children to investigation and reduced disparities in case opening rates between Black and white children, but found \"no evidence that the AFST resulted in greater screening consistency\" between call screeners.","scaled",2016,[24],[193],[],[220,223,226,230],{"url":221,"title":222,"publisher":208},"https://www.alleghenycounty.us/Services/Human-Services-DHS/DHS-News-and-Events/Accomplishments-and-Innovations/Allegheny-Family-Screening-Tool","Allegheny Family Screening Tool",{"url":224,"title":225,"publisher":208},"https://www.alleghenycounty.us/files/assets/county/v/1/services/dhs/documents/allegheny-family-screening-tool/impact-evaluation-summary-from-16-acdhs-26_predictiverisk_package_050119_final-5.pdf","Section 5: Impact Evaluation Summary of the Allegheny Family Screening Tool",{"url":227,"title":228,"publisher":229},"https://www.alleghenycounty.us/files/assets/county/v/1/services/dhs/documents/allegheny-family-screening-tool/allegheny-county-predictive-risk-modeling-tool-implementation-process-evaluation.pdf","Allegheny County Predictive Risk Modeling Tool Implementation: Process Evaluation","Hornby Zeller Associates, Inc.",{"url":231,"title":232,"publisher":208},"https://analytics.alleghenycounty.us/wp-content/uploads/2019/05/Ethical-Analysis-16-ACDHS-26_PredictiveRisk_Package_050119_FINAL-2.pdf","Ethical Analysis: Predictive Risk Models at Call Screening for Allegheny County DHS",{"level":200,"checkedAt":166},"allegheny-county-family-screening-tool",0,[],{"low":238,"high":239},20000,202500,[241,268,291,305],{"slug":164,"title":242,"shortTitle":243,"definition":244,"status":9,"industries":245,"functions":246,"patterns":247,"audience":25,"autonomy":251,"adoptionStage":252,"segment":253,"evidenceCount":254,"publicEvidenceCount":254,"organizations":255,"bestGrade":201,"headline":258,"lastVerified":166,"indexable":267},"AI drafting of social work case notes and assessments","Social worker case note drafting","A generative AI tool, often built on speech to text, that turns a social worker's account of a visit or assessment, whether a recorded conversation or their own dictated or typed prompt, into a first draft of the case note or statutory assessment in the format the case record needs, for the social worker to check, correct and sign before it becomes part of the record.",[18],[20],[248,249,250],"speech-analytics","summarization","content-generation","copilot","early-adopters","adult and children's social care",2,[256,257],"Lancashire County Council","Swindon Borough Council",{"kpi":259,"label":260,"unit":261,"n":262,"nUpTo":235,"kind":263,"value":264,"qualifier":265,"claimant":266,"organization":257,"vendorReported":184},"handling-time-reduction","Handling time reduction","percent",1,"reported",63,"exact","organization",true,{"slug":165,"title":269,"shortTitle":270,"definition":271,"status":9,"industries":272,"functions":273,"patterns":276,"audience":278,"autonomy":26,"adoptionStage":252,"evidenceCount":279,"publicEvidenceCount":279,"organizations":280,"bestGrade":201,"headline":286,"lastVerified":290,"indexable":267},"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.",[18],[274,275,20],"fraud-prevention","citizen-services",[22,277],"anomaly-detection","back-office",5,[281,282,283,284,285],"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":41,"label":287,"unit":288,"n":262,"nUpTo":235,"kind":263,"value":289,"qualifier":265,"claimant":266,"organization":282,"vendorReported":184},"Detection improvement","multiplier",2.5,"2026-09-27",{"slug":292,"title":293,"shortTitle":294,"definition":295,"status":9,"industries":296,"functions":298,"patterns":300,"audience":278,"autonomy":301,"adoptionStage":252,"evidenceCount":254,"publicEvidenceCount":254,"organizations":302,"bestGrade":201,"headline":203,"lastVerified":166,"indexable":267},"customs-risk-targeting-and-container-selection","AI for customs risk targeting, container selection and valuation checks","Customs risk targeting and container selection","Machine learning models that score every import or export declaration for the risk that it carries contraband, is misclassified or is undervalued, so a customs administration sends physical inspection, scanning and detailed review to the small share of containers, vehicles and parcels that need it, while the rest clear without an officer touching them, and an officer reviews and acts on every high risk alert.",[18,297],"logistics-and-transportation",[299,274,20],"risk-management",[22,277],"supervised-agent",[303,304],"Indian Customs (Central Board of Indirect Taxes and Customs)","U.S. Customs and Border Protection",{"slug":306,"title":307,"shortTitle":308,"definition":309,"status":9,"industries":310,"functions":311,"patterns":313,"audience":25,"autonomy":26,"adoptionStage":252,"evidenceCount":314,"publicEvidenceCount":314,"organizations":315,"bestGrade":201,"headline":203,"lastVerified":290,"indexable":267},"inspection-prioritization","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.",[18],[299,20,312],"regulatory-compliance",[22,277],8,[316,317,318,319,320,321,322,323],"Care Quality Commission","Driver and Vehicle Standards Agency","U.S. Environmental Protection Agency, Office of Enforcement and Compliance Assurance","U.S. Food and Drug Administration, Office of Information Operations","Food Standards Agency","Nederlandse Arbeidsinspectie","Nederlandse Voedsel- en Warenautoriteit (NVWA)","United States Coast Guard",{"indexable":267,"reasons":325},[],[327,334,340,348,354,361,367,374,382,389,396,402,408,414,421,428,434,441,446,452,459,466,471,476,481,488,493,498,505,510,517,524,530,537,542,547],{"id":139,"label":328,"issuer":329,"region":330,"url":331,"description":332,"useCases":333,"indexable":267},"EU AI Act","European Union","europe","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.",250,{"id":335,"label":336,"issuer":329,"region":330,"url":337,"description":338,"useCases":339,"indexable":267},"gdpr","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.",223,{"id":341,"label":342,"issuer":343,"region":344,"url":345,"description":346,"useCases":347,"indexable":267},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",127,{"id":140,"label":349,"issuer":350,"region":186,"url":351,"description":352,"useCases":353,"indexable":267},"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.",95,{"id":355,"label":356,"issuer":357,"region":330,"url":358,"description":359,"useCases":360,"indexable":267},"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.",73,{"id":362,"label":363,"issuer":329,"region":330,"url":364,"description":365,"useCases":366,"indexable":267},"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.",67,{"id":368,"label":369,"issuer":370,"region":330,"url":371,"description":372,"useCases":373,"indexable":267},"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":375,"label":376,"issuer":377,"region":378,"url":379,"description":380,"useCases":381,"indexable":267},"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":383,"label":384,"issuer":385,"region":378,"url":386,"description":387,"useCases":388,"indexable":267},"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":390,"label":391,"issuer":392,"region":344,"url":393,"description":394,"useCases":395,"indexable":267},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",22,{"id":397,"label":398,"issuer":399,"region":186,"url":400,"description":401,"useCases":395,"indexable":267},"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":403,"label":404,"issuer":329,"region":330,"url":405,"description":406,"useCases":407,"indexable":267},"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":409,"label":410,"issuer":411,"region":330,"url":412,"description":413,"useCases":407,"indexable":267},"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":415,"label":416,"issuer":417,"region":186,"url":418,"description":419,"useCases":420,"indexable":267},"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":422,"label":423,"issuer":424,"region":344,"url":425,"description":426,"useCases":427,"indexable":267},"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":429,"label":430,"issuer":329,"region":330,"url":431,"description":432,"useCases":433,"indexable":267},"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":435,"label":436,"issuer":437,"region":186,"url":438,"description":439,"useCases":440,"indexable":267},"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":442,"label":443,"issuer":329,"region":330,"url":444,"description":445,"useCases":440,"indexable":267},"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.",{"id":447,"label":448,"issuer":449,"region":186,"url":450,"description":451,"useCases":440,"indexable":267},"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":453,"label":454,"issuer":455,"region":344,"url":456,"description":457,"useCases":458,"indexable":267},"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.",12,{"id":460,"label":461,"issuer":462,"region":186,"url":463,"description":464,"useCases":465,"indexable":267},"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":467,"label":468,"issuer":329,"region":330,"url":469,"description":470,"useCases":465,"indexable":267},"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":472,"label":473,"issuer":329,"region":330,"url":474,"description":475,"useCases":465,"indexable":267},"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":477,"label":478,"issuer":329,"region":330,"url":479,"description":480,"useCases":465,"indexable":267},"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":482,"label":483,"issuer":484,"region":330,"url":485,"description":486,"useCases":487,"indexable":267},"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":489,"label":490,"issuer":377,"region":378,"url":491,"description":492,"useCases":487,"indexable":267},"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":494,"label":495,"issuer":329,"region":330,"url":496,"description":497,"useCases":487,"indexable":267},"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":499,"label":500,"issuer":501,"region":186,"url":502,"description":503,"useCases":504,"indexable":267},"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.",7,{"id":506,"label":507,"issuer":329,"region":330,"url":508,"description":509,"useCases":504,"indexable":267},"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":511,"label":512,"issuer":513,"region":514,"url":515,"description":516,"useCases":279,"indexable":267},"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":518,"label":519,"issuer":520,"region":330,"url":521,"description":522,"useCases":523,"indexable":267},"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.",4,{"id":525,"label":526,"issuer":527,"region":330,"url":528,"description":529,"useCases":523,"indexable":267},"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":531,"label":532,"issuer":533,"region":378,"url":534,"description":535,"useCases":536,"indexable":267},"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":538,"label":539,"issuer":329,"region":330,"url":540,"description":541,"useCases":536,"indexable":267},"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":543,"label":544,"issuer":329,"region":330,"url":545,"description":546,"useCases":536,"indexable":267},"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":548,"label":549,"issuer":550,"region":186,"url":551,"description":552,"useCases":536,"indexable":267},"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.",1790783085708]