[{"data":1,"prerenderedAt":762},["ShallowReactive",2],{"uc-benefits-eligibility-and-application-assistant":3,"uc-regulations":558},{"useCase":4,"evidence":230,"blitsAiDeployments":428,"benchmarks":429,"indicative":456,"related":459,"indexability":556,"includeUnpublished":236},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":22,"channels":27,"audience":32,"autonomy":33,"adoptionStage":34,"problem":35,"problemStats":36,"howItWorks":42,"valueDrivers":43,"kpis":48,"indicativeValue":55,"macroEstimates":97,"feasibility":98,"implementation":112,"risk":158,"blitsAi":206,"faq":208,"related":218,"datePublished":224,"dateModified":224,"lastVerified":225,"changelog":226,"slug":229},"AI assistant for benefits eligibility questions and applications","Benefits eligibility and application assistant","AI assistant for benefits eligibility and claims","AI assistants explain benefit rules, guide applications and answer status questions; caseworkers decide. DWP's voice platform handles about 1 million calls a month.","published","An AI assistant that helps people understand which public benefits and grants may apply to them, explains the rules and documents in plain language, guides them through the application and checks it for completeness, while the eligibility decision stays with the agency's rules and caseworkers.",[12,13,14,15],"benefits chatbot","social security virtual assistant","welfare application assistant","benefits navigator",[17],"government",[19,20,21],"citizen-services","case-management","customer-service",[23,24,25,26],"conversational-agent","rag-knowledge-assistant","voice-agent","document-processing",[28,29,30,31],"web-chat","voice","mobile-app","whatsapp","customer-facing","copilot","early-adopters","Benefit rules are complex and change often, and the people who need them are often under\npressure: a lost job, a disaster, a new disability, a family change. Many never\nclaim support they are entitled to: in Great Britain alone, DWP estimates that up to 910,000\nfamilies entitled to Pension Credit did not claim it. Others apply for the wrong scheme, and\nincomplete applications bounce back and forth between applicant and caseworker. Phone lines fill\nwith questions about claim progress, payment dates and missing documents.\n\nIt is also an area where automation has already caused serious public harm. The Dutch childcare\nbenefits scandal, where a risk profiling algorithm used nationality as a risk factor, shows what\nhappens when an algorithm's output drives how applicants are treated. An assistant here must widen\naccess and cut rework without quietly deciding who gets support.",[37],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"DWP estimates that up to 910,000 families in Great Britain who were entitled to Pension Credit did not claim it in the financial year ending 2024, leaving up to £2.5 billion unclaimed.","Income-related benefits: estimates of take-up: financial year ending 2024","https://www.gov.uk/government/statistics/income-related-benefits-estimates-of-take-up-financial-year-ending-2024/income-related-benefits-estimates-of-take-up-financial-year-ending-2024",2025,"1. **Explain the schemes.** The assistant answers questions about benefits, grants and support in\n   plain language from the agency's approved rules and guidance, with sources.\n2. **Screen, do not decide.** Where the agency allows it, it runs the official eligibility\n   questions (often a rules engine owned by the agency) and says which schemes look worth applying\n   for, stating clearly that the outcome is not a decision.\n3. **Guide the application.** It explains what each question means, which documents are needed and\n   why, in the applicant's language, and checks the form for gaps before submission.\n4. **Answer status questions.** For authenticated users it reads case status, next payment date\n   and missing evidence from the case system.\n5. **Hand over.** Anyone in crisis, disputing a decision or showing signs of vulnerability goes to\n   a caseworker with the conversation attached.",[44,45,46,47],"inclusion-and-access","customer-experience","cost-to-serve","speed",[49,50,51,52,53,54],"interactions-handled","users-served","accuracy","response-time-reduction","containment-rate","processing-time-reduction",{"referenceOrg":56,"inputs":57,"formula":92,"currency":93,"period":94,"resultLabel":95,"caveat":96},"A regional benefits agency that receives 500,000 applications a year",[58,64,71,78,85],{"key":59,"label":60,"low":61,"high":61,"unit":62,"note":63},"applications","Applications received per year",500000,"applications per year","The reference agency.",{"key":65,"label":66,"low":67,"high":68,"unit":69,"note":70},"incompleteShare","Share of applications returned for missing information",0.15,0.3,"fraction of applications","Editorial assumption. Replace with your own rework rate.",{"key":72,"label":73,"low":74,"high":75,"unit":76,"note":77},"reductionShare","Share of those incomplete applications the assistant prevents",0.2,0.4,"fraction of incomplete applications","Editorial assumption; no public benchmark yet measures this directly.",{"key":79,"label":80,"low":81,"high":82,"unit":83,"note":84},"reworkHours","Caseworker hours per incomplete application",0.5,1,"hours per application","Editorial assumption covering contact, chasing documents and re entry.",{"key":86,"label":87,"low":88,"high":89,"unit":90,"note":91},"hourlyCost","Fully loaded caseworker cost per hour",35,55,"USD per hour","Editorial assumption. Replace with your own cost.","applications * incompleteShare * reductionShare * reworkHours * hourlyCost","USD","per year","Caseworker rework cost avoided","Covers rework on incomplete applications only. It leaves out contact centre savings on status questions, faster payment to applicants, higher take up (which raises benefit spending) and the cost of building and governing the assistant.",[],{"complexity":99,"complexityNote":100,"dataPrerequisites":101,"integrations":106},"high","Answering general questions is straightforward; screening and application support touch the most regulated decisions in government. The work is in keeping the assistant separate from the eligibility decision, integrating with case systems and identity, and proving it treats groups fairly.",[102,103,104,105],"Approved, current benefit rules and guidance with owners and effective dates","The agency's official eligibility logic, preferably as an executable rules service","Application forms and document requirements per scheme","Case status data reachable through APIs for authenticated users",[107,108,109,110,111],"Identity and authentication (national login or agency account)","Case management and payment systems for status and next payment","Rules engine for eligibility screening, owned by the policy team","Document upload and verification services","Contact centre and caseworker queues for handover",{"steps":113,"guardrails":132,"humanInTheLoop":138,"kpisToInstrument":139,"failureModes":145},[114,117,120,123,126,129],{"title":115,"detail":116},"Separate information from decision","Write down which outputs are information, which are screening and which are decisions, and keep the last with the agency's rules engine and caseworkers. Put this in the service design and the register entry.",{"title":118,"detail":119},"Start with explanation and status","Launch with plain language explanations and authenticated status questions, which carry low decision risk and high contact volume (DWP's voice platform answers next payment questions in the IVR).",{"title":121,"detail":122},"Use the official rules for screening","If you add eligibility screening, call the agency's own rules service rather than letting a language model interpret the law, and label the result as indicative.",{"title":124,"detail":125},"Test for fairness and vulnerability","Build test sets across languages, disabilities and circumstances, including people in crisis, and check that handover triggers fire.",{"title":127,"detail":128},"Pilot with caseworkers watching","Pilot with a limited group and have caseworkers review transcripts weekly. Leeds tested answers against a question set with reference answers verified by domain experts before launch, and plans to review transcripts and feedback during its pilot.",{"title":130,"detail":131},"Measure take up and rework, not only contacts","Track incomplete applications, time to decision and take up among eligible groups, not just conversations.",[133,134,135,136,137],"The assistant never tells a person they are or are not entitled; screening results are labelled indicative and come from the official rules","Answers only from approved rules and guidance, with sources and effective dates","Automatic handover on crisis, vulnerability, disputes and appeals","Personal data masking in logs and prompts, and data minimisation in what the assistant asks","Equal treatment tests across language and demographic groups before and after every change","Caseworkers decide every application and every change to an award. They review samples of conversations each week, with priority for handovers and complaints, and policy owners approve each new scheme or rule before the assistant explains it.",[140,141,142,143,144],"Share of applications submitted complete, with and without the assistant","Time from first contact to decision","Accuracy of answers on a weekly expert reviewed sample","Handover rate and reasons, including vulnerability triggers","Take up and outcome differences across language and demographic groups",[146,149,152,155],{"title":147,"detail":148},"Screening becomes the decision","Applicants told they probably do not qualify stop applying. Nissewaard's register entry names this exact risk. Label screening as indicative and always allow an application.",{"title":150,"detail":151},"Automated suspicion","In the Dutch childcare benefits scandal, Amnesty International found that a risk profiling algorithm using nationality led to discrimination and racial profiling of applicants. Keep fraud models out of the assistant and govern them separately.",{"title":153,"detail":154},"Outdated rules","Benefit rates and thresholds change every year. Tie content to effective dates and retest on every change.",{"title":156,"detail":157},"No route to a person","A chatbot that cannot hand over leaves people in crisis with a phone number at best. Leeds' pilot, which does not escalate to a human, at least points distressed users to emergency helplines and gives the council's number. Add a direct human route before scaling.",{"euAiAct":159,"regulations":162,"guidance":169,"controls":191,"incidents":197},{"tier":160,"basis":161},"context-dependent","Annex III point 5(a) makes AI high risk when it is used by or on behalf of public authorities to evaluate the eligibility of natural persons for essential public assistance benefits and services, or to grant, reduce, revoke or reclaim them. An assistant that only explains rules and guides applications carries the Article 50 transparency duties (limited risk); one that screens or scores eligibility falls under point 5(a), and a public body deploying it must carry out a fundamental rights impact assessment first (Article 27).",[163,164,165,166,167,168],"eu-ai-act","gdpr","nist-ai-rmf","iso-42001","uk-gdpr","uk-atrs",[170,176,180,186],{"title":171,"issuer":172,"region":173,"url":174,"note":175},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(a) covers AI used to evaluate eligibility for essential public assistance benefits and services.",{"title":177,"issuer":172,"region":173,"url":178,"note":179},"Article 27, fundamental rights impact assessment for high risk AI systems","https://artificialintelligenceact.eu/article/27/","Public bodies deploying high risk AI must assess the impact on fundamental rights before use.",{"title":181,"issuer":182,"region":183,"url":184,"note":185},"Directive on Automated Decision-Making","Treasury Board of Canada Secretariat","north-america","https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592","Requires an algorithmic impact assessment, notice before decisions, explanation after decisions and human involvement for automated decision systems of Canadian federal institutions.",{"title":187,"issuer":188,"region":173,"url":189,"note":190},"AI Playbook for the UK Government","UK Government","https://www.gov.uk/government/publications/ai-playbook-for-the-uk-government","Guidance for UK public bodies on building and governing AI services.",[192,193,194,195,196],"Documented boundary between information, screening and decision in the service design and register entry","Fundamental rights or algorithmic impact assessment before launch where screening is involved","Equality monitoring of outcomes and handovers across groups","Right to a human caseworker and to apply regardless of any screening result","Audit trail of every answer, source and screening result shown to an applicant",[198,202],{"title":199,"url":200,"note":201},"Xenophobic machines: discrimination through unregulated use of algorithms in the Dutch childcare benefits scandal","https://www.amnesty.org/en/documents/eur35/4686/2021/en/","Amnesty International's analysis of how the Dutch tax authorities' risk profiling of childcare benefit applicants used nationality as a risk factor, resulting in discrimination and racial profiling.",{"title":203,"url":204,"note":205},"Automated Neglect: how the World Bank's push to allocate cash assistance using algorithms threatens rights","https://www.hrw.org/report/2023/06/13/automated-neglect/how-world-banks-push-allocate-cash-assistance-using-algorithms","Human Rights Watch on how Takaful, a World Bank funded cash transfer programme in Jordan, ranks families by algorithm and deprives many people of their right to social security.",{"howToBuild":207},"On Blits.ai the conversation runs as an **AI agent** grounded in a **knowledge base** of approved\nbenefit rules and guidance, retrieved with **hybrid search**. Eligibility screening is not left\nto the model: a **custom function** calls the agency's own rules service, and the screening\njourney runs as a **flow** with deterministic steps, an authentication block and sensitive data\nflags on the questions it asks. Status and payment questions use custom functions against the\ncase system; document uploads use the receive attachment block.\n\n**Guardrails** stop the agent from stating entitlement, **PII masking** removes personal data\nbefore text reaches a model, and **human handover** routes crisis, vulnerability and disputes to\ncaseworkers with the transcript. The same agent works on **web chat, WhatsApp and voice**, with\nlanguage detection and translation for multilingual service. **Test suites** check fairness and\nhandover scenarios on every change, and the platform runs in **EU and UAE data residency** regions.",[209,212,215],{"question":210,"answer":211},"Can an AI assistant decide benefit eligibility?","It should not. Under the EU AI Act, AI that evaluates eligibility for public assistance is high risk, and public examples keep the decision elsewhere: DWP's voice platform makes no decisions and routes callers to advisers, and Nissewaard's eligibility check is a rules based decision tree whose outcome caseworkers can overrule.",{"question":213,"answer":214},"What do benefits assistants handle today?","Mostly explanation, status and routing. Federal Student Aid's Aidan reached over 2.6 million unique customers in just over two years, and about 1 million calls a month pass through DWP's voice platform, which answers some questions in the IVR, signposts callers to GOV.UK or routes them to an adviser.",{"question":216,"answer":217},"How accurate are they?","Public figures are sparse and set their own bars. Leeds reports that 83% of answers scored at least 3 out of 5 in quality in testing before launch, with input from domain experts, and DWP reports 97% speech recognition success. Measure accuracy on your own schemes with experts before launch.",[219,220,221,222,223],"citizen-information-assistant","benefit-fraud-and-error-detection","public-service-translation","permit-and-licence-application-processing","immigration-and-visa-application-assistant","2026-09-27","2026-09-26",[227],{"date":224,"note":228},"First published","benefits-eligibility-and-application-assistant",[231,261,298,317,342,372,404],{"title":232,"useCases":233,"organization":234,"vendors":238,"summary":242,"stage":243,"year":244,"channels":245,"languages":247,"metrics":249,"outcomeDisclosed":236,"sources":250,"verification":256,"grade":258,"id":259,"organizationSlug":260},"Gemeente Nissewaard: rules based eligibility check in online benefit applications",[229],{"name":235,"anonymized":236,"country":237,"region":173,"industry":17},"Gemeente Nissewaard",false,"NL",[239],{"name":240,"role":241},"Centric Netherlands BV","platform","Nissewaard runs an online application service for social assistance (bijstand), special assistance, support for the self employed and minimum income schemes. During the application a decision tree checks the data read in and the applicant's answers against the legal criteria and shows the outcome to the applicant; caseworkers can overrule it. The tool, supplied by Centric, has been in use since March 2017, and the register entry names the risk that applicants give up because it sets wrong expectations about the outcome. Entries for other municipalities say the same service is used by about 50 Dutch municipalities.","production",2026,[246],"internal-tools",[248],"nl",[],[251],{"url":252,"title":253,"publisher":254,"date":255},"https://algoritmes.overheid.nl/nl/algoritme/gm1930/33327482/sociaal-domein-ediensten-voor-aanvragen","Sociaal Domein: eDiensten voor aanvragen, Gemeente Nissewaard","Algoritmeregister van de Nederlandse overheid","2026-09-22",{"level":257,"checkedAt":225},"source-verified","B","gemeente-nissewaard-benefit-application-eligibility-check",null,{"title":262,"useCases":263,"organization":264,"vendors":267,"summary":270,"stage":271,"year":41,"channels":272,"languages":273,"metrics":275,"outcomeDisclosed":290,"sources":291,"verification":296,"grade":258,"id":297,"organizationSlug":260},"DWP: Conversational Platform on the benefits telephone lines",[229],{"name":265,"anonymized":236,"country":266,"region":173,"industry":17},"Department for Work and Pensions","GB",[268],{"name":269,"role":241},"Omilia Natural Language Solutions","Callers to the benefit telephone lines of the Department for Work and Pensions (DWP) are asked what they are calling about. Speech recognition and a natural language model then signpost them to GOV.UK, help them log in to their online account, answer some questions in the IVR (such as the next payment amount and date) or route them to the right adviser. The platform also runs identity and verification questions against the DWP trust hub by API. It makes no decisions; callers can always ask for a human.","scaled",[29],[274],"en",[276,284],{"kpi":49,"value":277,"unit":278,"qualifier":279,"period":280,"claimant":281,"quote":282,"sourceUrl":283},1000000,"count","approximately","calls per month","organization","At present approximately 1 million calls per month pass through the Conversational Platform.","https://www.gov.uk/algorithmic-transparency-records/dwp-conversational-platform",{"kpi":51,"value":285,"unit":286,"qualifier":287,"period":288,"claimant":281,"quote":289,"sourceUrl":283},97,"percent","exact","speech recognition success","97% speech recognition success",true,[292],{"url":283,"title":293,"publisher":294,"date":295},"DWP: Conversational Platform","GOV.UK (Algorithmic Transparency Recording Standard)","2025-12-16",{"level":257,"checkedAt":225},"dwp-conversational-platform",{"title":299,"useCases":300,"organization":301,"vendors":304,"summary":305,"stage":306,"year":41,"channels":307,"languages":308,"metrics":309,"outcomeDisclosed":236,"sources":310,"verification":315,"grade":258,"id":316,"organizationSlug":260},"FEMA: machine translation of disaster survivors' documents for Individual Assistance",[221,229],{"name":302,"anonymized":236,"country":303,"region":183,"industry":17},"Federal Emergency Management Agency","US",[],"FEMA plans to translate the full text of non English documents that disaster survivors submit with their Individual Assistance applications, instead of relying on a contractor's summary of each document. The agency expects faster case processing and a drop in cost from about USD 40 per document to pennies. Original and translation will both be stored in the survivor's file, as substantiating documents that support assistance determinations.","announced",[246],[274],[],[311],{"url":312,"title":313,"publisher":314},"https://raw.githubusercontent.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory/main/Data/2025_individually_reported_AI_use_cases.csv","2025 individually reported AI use cases (consolidated federal inventory data)","Office of Management and Budget (GitHub)",{"level":257,"checkedAt":225},"fema-individual-assistance-document-translation",{"title":318,"useCases":319,"organization":320,"vendors":322,"summary":323,"stage":271,"year":41,"channels":324,"languages":325,"metrics":326,"outcomeDisclosed":290,"sources":335,"verification":340,"grade":258,"id":341,"organizationSlug":260},"Federal Student Aid: Aidan virtual assistant on StudentAid.gov",[229],{"name":321,"anonymized":236,"country":303,"region":183,"industry":17},"Federal Student Aid (U.S. Department of Education)",[],"Federal Student Aid, the office of the U.S. Department of Education that runs federal student financial aid, operates Aidan, a virtual assistant on StudentAid.gov that uses natural language processing to answer common financial aid questions and help customers find information about their own federal aid. The agency reports it in its AI use case inventory with usage figures for its first two years.",[28],[274],[327,332],{"kpi":50,"value":328,"unit":278,"qualifier":329,"period":330,"claimant":281,"quote":331,"sourceUrl":312},2600000,"at-least","unique customers in just over two years","In just over two years, Aidan has interacted with over 2.6 million unique customers, resulting in more than 11 million user messages.",{"kpi":49,"value":333,"unit":278,"qualifier":329,"period":334,"claimant":281,"quote":331,"sourceUrl":312},11000000,"user messages in just over two years",[336,337],{"url":312,"title":313,"publisher":314},{"url":338,"title":339,"publisher":314},"https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory","2025 Federal Agency AI Use Case Inventory",{"level":257,"checkedAt":225},"federal-student-aid-aidan-virtual-assistant",{"title":343,"useCases":344,"organization":345,"vendors":347,"summary":356,"stage":357,"year":41,"channels":358,"languages":359,"metrics":360,"outcomeDisclosed":290,"sources":366,"verification":370,"grade":258,"id":371,"organizationSlug":260},"Leeds City Council: Money Information Centre chatbot",[229],{"name":346,"anonymized":236,"country":266,"region":173,"industry":17},"Leeds City Council",[348,351,353],{"name":349,"role":350},"Kainos Software Ltd","integrator",{"name":352,"role":241},"Amazon Web Services (Amazon Bedrock)",{"name":354,"role":355},"Anthropic (Claude 3 Sonnet)","model-provider","Leeds City Council piloted a retrieval augmented chatbot on its Money Information Centre website, a site with information on money and support services. It answers only from website content, with Amazon Bedrock guardrails that keep it on topic, does not give financial advice and warns users that no human is on the other side. It does not hand over to a human agent; instead it gives the council's phone number and refers users who show distress to emergency helplines. An evaluation plan decides at the end of a six week pilot whether it delivers its benefits.","pilot",[28],[274],[361],{"kpi":51,"value":362,"unit":286,"qualifier":287,"period":363,"claimant":281,"quote":364,"sourceUrl":365},83,"chatbot responses scored at least 3 out of 5 in quality, in testing before launch with input from domain experts","On average, 83% of chatbot responses were scored at least a 3 out of 5 in quality.","https://www.gov.uk/algorithmic-transparency-records/leeds-city-council-money-information-centre-chatbot",[367],{"url":365,"title":368,"publisher":294,"date":369},"Leeds City Council: Money Information Centre Chatbot","2025-06-26",{"level":257,"checkedAt":225},"leeds-city-council-money-information-centre-chatbot",{"title":373,"useCases":374,"organization":375,"vendors":378,"summary":385,"stage":243,"year":41,"channels":386,"languages":388,"metrics":390,"outcomeDisclosed":290,"sources":397,"verification":401,"grade":402,"id":403,"organizationSlug":260},"Région Sud: automated document checks for jobseeker training grants",[229],{"name":376,"anonymized":236,"country":377,"region":173,"industry":17},"Région Provence-Alpes-Côte d'Azur (Région Sud)","FR",[379,381,383],{"name":380,"role":241},"Microsoft (Azure OpenAI Service)",{"name":382,"role":355},"Mistral AI (models on Azure)",{"name":384,"role":350},"Exakis Nelite","As part of its regional AI plan, Région Sud in southern France automated the verification of the supporting documents that jobseekers submit for skills training grants, and deployed a chatbot on Azure OpenAI Service and Mistral models that helps agents at its Allo Région call centre answer citizens; the administration receives 75,000 requests a year. The chatbot queries the region's own databases in a secure environment.",[246,387],"agent-desktop",[389],"fr",[391],{"kpi":49,"value":392,"unit":278,"qualifier":329,"period":393,"claimant":394,"quote":395,"sourceUrl":396},30000,"grant documents per year","vendor","At the same time, they have automated the verification of documents required for jobseekers skill training grants: no fewer than 30,000 documents per year are now processed by the machine.","https://www.microsoft.com/en/customers/story/23393-region-sud-microsoft-365-copilot",[398],{"url":396,"title":399,"publisher":400},"Région Sud and Microsoft co-build a smart 4.0 territory with Azure OpenAI Service and Microsoft 365 Copilot","Microsoft Customer Stories",{"level":257,"checkedAt":225},"C","region-sud-training-grant-document-verification",{"title":405,"useCases":406,"organization":407,"vendors":410,"summary":413,"stage":243,"year":41,"channels":414,"languages":415,"metrics":416,"outcomeDisclosed":290,"sources":423,"verification":426,"grade":402,"id":427,"organizationSlug":260},"YoungWilliams: Priya agent for SNAP, child support and Summer EBT enquiries",[229],{"name":408,"anonymized":236,"country":303,"region":183,"industry":409},"YoungWilliams","professional-services",[411],{"name":412,"role":241},"Microsoft (Azure AI Foundry Agent Service)","YoungWilliams, a US company, provides call centre and other services to government health and human services organizations. Its AI agent Priya answers public enquiries on child support services, the Supplemental Nutrition Assistance Program (SNAP) and Summer EBT, covering benefit applications, eligibility changes, reapplication and wait times, and supports human representatives by retrieving information, summarizing cases and clarifying policy. It retrieves live government data through Azure AI Search and Bing Search, asks clarifying questions instead of assuming, and enforces role based access to sensitive data.",[28,387],[274],[417],{"kpi":52,"value":418,"unit":286,"qualifier":287,"period":419,"baseline":420,"claimant":394,"quote":421,"sourceUrl":422},99,"initial response time, from almost four minutes to about three seconds","human customer service representatives","Priya was 99% faster in initial response time and delivered 45% more empathetic interactions","https://www.microsoft.com/en/customers/story/23958-young-williams-azure-ai-agent-service",[424],{"url":422,"title":425,"publisher":400},"YoungWilliams cuts call center response time 99% with Azure AI Foundry Agent Service",{"level":257,"checkedAt":225},"youngwilliams-priya-benefits-agent",0,[430,439,446,451],{"kpi":49,"label":431,"unit":278,"aggregate":236,"higherIsBetter":290,"n":432,"nUpTo":428,"median":277,"min":392,"max":333,"byClaimant":433,"vendorOnly":236,"points":435},"Interactions handled",3,{"organization":434,"vendor":82,"regulator":428,"independent":428},2,[436,437,438],{"evidenceId":341,"organization":321,"value":333,"qualifier":329,"claimant":281,"grade":258,"pooled":290},{"evidenceId":297,"organization":265,"value":277,"qualifier":279,"claimant":281,"grade":258,"pooled":290},{"evidenceId":403,"organization":376,"value":392,"qualifier":329,"claimant":394,"grade":402,"pooled":290},{"kpi":51,"label":440,"unit":286,"aggregate":290,"higherIsBetter":290,"n":434,"nUpTo":428,"median":441,"min":362,"max":285,"byClaimant":442,"vendorOnly":236,"points":443},"Accuracy",90,{"organization":434,"vendor":428,"regulator":428,"independent":428},[444,445],{"evidenceId":297,"organization":265,"value":285,"qualifier":287,"claimant":281,"grade":258,"pooled":290},{"evidenceId":371,"organization":346,"value":362,"qualifier":287,"claimant":281,"grade":258,"pooled":290},{"kpi":52,"label":447,"unit":286,"aggregate":290,"higherIsBetter":290,"n":82,"nUpTo":428,"median":418,"min":418,"max":418,"byClaimant":448,"vendorOnly":290,"points":449},"Response time reduction",{"organization":428,"vendor":82,"regulator":428,"independent":428},[450],{"evidenceId":427,"organization":408,"value":418,"qualifier":287,"claimant":394,"grade":402,"pooled":290},{"kpi":50,"label":452,"unit":278,"aggregate":236,"higherIsBetter":290,"n":82,"nUpTo":428,"median":328,"min":328,"max":328,"byClaimant":453,"vendorOnly":236,"points":454},"Users served",{"organization":82,"vendor":428,"regulator":428,"independent":428},[455],{"evidenceId":341,"organization":321,"value":328,"qualifier":329,"claimant":281,"grade":258,"pooled":290},{"low":457,"high":458},262500,3300000,[460,486,509,526,544],{"slug":219,"title":461,"shortTitle":462,"definition":463,"status":9,"industries":464,"functions":465,"patterns":467,"audience":32,"autonomy":469,"adoptionStage":470,"evidenceCount":471,"publicEvidenceCount":472,"organizations":473,"bestGrade":258,"headline":483,"lastVerified":224,"indexable":290},"AI assistant for citizen information and government services","Citizen information assistant","An AI assistant that answers residents' and businesses' questions about government services in plain language, grounded only in official guidance with links to the source, points them to the right online service or office, and hands anything personal, urgent or outside its content to a human with the context attached.",[17],[19,21,466],"knowledge-management",[24,23,25,468],"classification-and-routing","supervised-agent","mainstream",11,9,[474,475,476,477,478,479,480,481,482],"Abu Dhabi Government (TAMM)","Driver and Vehicle Licensing Agency","Estonian Information System Authority (RIA)","Foreign, Commonwealth and Development Office","Gemeente Tilburg","Government Digital Service","Government of the City of Buenos Aires","Madrid Destino","Montgomery County Government",{"kpi":51,"label":440,"unit":286,"n":82,"nUpTo":428,"kind":484,"value":485,"qualifier":329,"claimant":281,"organization":477,"vendorReported":236},"reported",76,{"slug":220,"title":487,"shortTitle":488,"definition":489,"status":9,"industries":490,"functions":491,"patterns":493,"audience":496,"autonomy":497,"adoptionStage":34,"evidenceCount":498,"publicEvidenceCount":498,"organizations":499,"bestGrade":258,"headline":504,"lastVerified":224,"indexable":290},"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],[492,19,20],"fraud-prevention",[494,495],"prediction-and-scoring","anomaly-detection","back-office","assist",5,[500,265,501,502,503],"Centers for Medicare and Medicaid Services","Gemeente Rotterdam","U.S. Department of the Treasury, Bureau of the Fiscal Service","Uitvoeringsinstituut Werknemersverzekeringen (UWV)",{"kpi":505,"label":506,"unit":507,"n":82,"nUpTo":428,"kind":484,"value":508,"qualifier":287,"claimant":281,"organization":265,"vendorReported":236},"detection-rate-improvement","Detection improvement","multiplier",2.5,{"slug":221,"title":510,"shortTitle":511,"definition":512,"status":9,"industries":513,"functions":514,"patterns":516,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":519,"publicEvidenceCount":519,"organizations":520,"bestGrade":258,"headline":260,"lastVerified":225,"indexable":290},"AI translation and interpretation for multilingual public services","Public service translation","AI that translates government content, documents and conversations between officials and the public, in writing and in real time speech, so people can use public services in their own language, with human translators and interpreters reviewing what carries legal or safety weight.",[17],[19,21,515],"operations",[517,23,518,26],"translation","speech-analytics",8,[521,522,523,302,524,481,482,525],"Baltimore City 911 (Emergency Communications)","Delaware County","European Commission","Internal Revenue Service","U.S. Department of State (Bureau of Consular Affairs)",{"slug":222,"title":527,"shortTitle":528,"definition":529,"status":9,"industries":530,"functions":531,"patterns":533,"audience":535,"autonomy":33,"adoptionStage":536,"evidenceCount":498,"publicEvidenceCount":498,"organizations":537,"bestGrade":258,"headline":542,"lastVerified":225,"indexable":290},"AI for permit and licence application processing","Permit and licence application processing","AI that helps applicants submit complete permit and licence applications and helps officers process them, by answering questions about requirements, checking applications for missing or inconsistent information, pulling the relevant policies, history and constraints, and drafting reports, while the grant or refusal stays with a named officer or a published rule.",[17],[19,20,532],"regulatory-compliance",[26,534,24,23],"agentic-workflow","employee-facing","emerging",[538,346,539,540,541],"Intellectual Property Office","U.S. Fish and Wildlife Service","U.S. Department of Agriculture","West Berkshire Council",{"kpi":51,"label":440,"unit":286,"n":82,"nUpTo":428,"kind":484,"value":543,"qualifier":329,"claimant":281,"organization":346,"vendorReported":236},85,{"slug":223,"title":545,"shortTitle":546,"definition":547,"status":9,"industries":548,"functions":549,"patterns":550,"audience":32,"autonomy":33,"adoptionStage":34,"evidenceCount":551,"publicEvidenceCount":551,"organizations":552,"bestGrade":258,"headline":260,"lastVerified":224,"indexable":290},"AI for immigration and visa applications, from applicant questions to case preparation","Immigration and visa application assistant","AI that helps applicants understand immigration and visa requirements and submit complete applications, and helps immigration staff prepare cases by extracting form data, classifying evidence, routing applications and supporting interviews, while every grant or refusal is decided by an officer against the immigration rules.",[17],[19,20,515],[23,26,468,517],4,[553,525,554,555],"Home Office (Visa, Status and Information Services)","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services",{"indexable":290,"reasons":557},[],[559,564,569,576,581,587,593,600,608,615,622,628,633,640,646,651,658,664,670,676,681,687,693,698,703,709,715,720,726,733,739,745,751,756],{"id":163,"label":560,"issuer":172,"region":173,"url":561,"description":562,"useCases":563,"indexable":290},"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.",197,{"id":164,"label":565,"issuer":172,"region":173,"url":566,"description":567,"useCases":568,"indexable":290},"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":166,"label":570,"issuer":571,"region":572,"url":573,"description":574,"useCases":575,"indexable":290},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":165,"label":577,"issuer":578,"region":183,"url":579,"description":580,"useCases":362,"indexable":290},"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.",{"id":582,"label":583,"issuer":172,"region":173,"url":584,"description":585,"useCases":586,"indexable":290},"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":167,"label":588,"issuer":589,"region":173,"url":590,"description":591,"useCases":592,"indexable":290},"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":594,"label":595,"issuer":596,"region":173,"url":597,"description":598,"useCases":599,"indexable":290},"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":601,"label":602,"issuer":603,"region":604,"url":605,"description":606,"useCases":607,"indexable":290},"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":609,"label":610,"issuer":611,"region":604,"url":612,"description":613,"useCases":614,"indexable":290},"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":616,"label":617,"issuer":618,"region":572,"url":619,"description":620,"useCases":621,"indexable":290},"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":623,"label":624,"issuer":625,"region":183,"url":626,"description":627,"useCases":621,"indexable":290},"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":168,"label":629,"issuer":188,"region":173,"url":630,"description":631,"useCases":632,"indexable":290},"UK Algorithmic Transparency Recording Standard","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":634,"label":635,"issuer":636,"region":572,"url":637,"description":638,"useCases":639,"indexable":290},"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":641,"label":642,"issuer":172,"region":173,"url":643,"description":644,"useCases":645,"indexable":290},"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":647,"label":648,"issuer":172,"region":173,"url":649,"description":650,"useCases":645,"indexable":290},"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":652,"label":653,"issuer":654,"region":183,"url":655,"description":656,"useCases":657,"indexable":290},"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":659,"label":660,"issuer":172,"region":173,"url":661,"description":662,"useCases":663,"indexable":290},"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":665,"label":666,"issuer":667,"region":183,"url":668,"description":669,"useCases":663,"indexable":290},"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":671,"label":672,"issuer":673,"region":572,"url":674,"description":675,"useCases":663,"indexable":290},"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":677,"label":678,"issuer":172,"region":173,"url":679,"description":680,"useCases":471,"indexable":290},"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":682,"label":683,"issuer":684,"region":183,"url":685,"description":686,"useCases":471,"indexable":290},"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":688,"label":689,"issuer":603,"region":604,"url":690,"description":691,"useCases":692,"indexable":290},"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":694,"label":695,"issuer":172,"region":173,"url":696,"description":697,"useCases":692,"indexable":290},"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":699,"label":700,"issuer":172,"region":173,"url":701,"description":702,"useCases":692,"indexable":290},"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":704,"label":705,"issuer":706,"region":173,"url":707,"description":708,"useCases":472,"indexable":290},"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.",{"id":710,"label":711,"issuer":712,"region":183,"url":713,"description":714,"useCases":519,"indexable":290},"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.",{"id":716,"label":717,"issuer":172,"region":173,"url":718,"description":719,"useCases":519,"indexable":290},"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":721,"label":722,"issuer":172,"region":173,"url":723,"description":724,"useCases":725,"indexable":290},"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.",6,{"id":727,"label":728,"issuer":729,"region":730,"url":731,"description":732,"useCases":498,"indexable":290},"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":734,"label":735,"issuer":736,"region":173,"url":737,"description":738,"useCases":551,"indexable":290},"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":740,"label":741,"issuer":742,"region":173,"url":743,"description":744,"useCases":551,"indexable":290},"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":746,"label":747,"issuer":748,"region":604,"url":749,"description":750,"useCases":432,"indexable":290},"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":752,"label":753,"issuer":172,"region":173,"url":754,"description":755,"useCases":432,"indexable":290},"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":757,"label":758,"issuer":759,"region":183,"url":760,"description":761,"useCases":432,"indexable":290},"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.",1790598296527]