[{"data":1,"prerenderedAt":580},["ShallowReactive",2],{"uc-student-enrollment-and-services-assistant":3,"uc-regulations":369},{"useCase":4,"evidence":200,"blitsAiDeployments":283,"benchmarks":284,"indicative":292,"related":295,"indexability":367,"includeUnpublished":206},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":30,"autonomy":31,"adoptionStage":32,"problem":33,"problemStats":34,"howItWorks":40,"valueDrivers":41,"kpis":46,"indicativeValue":53,"macroEstimates":89,"feasibility":90,"implementation":102,"risk":145,"blitsAi":179,"faq":181,"related":194,"datePublished":195,"dateModified":195,"lastVerified":195,"changelog":196,"slug":199},"AI assistant for student enrollment and student services","Student enrollment assistant","AI chatbot for student enrollment and services","AI assistants answer students by text and chat and nudge them to enroll. Georgia State cut summer melt by 22% with a new student portal and its Pounce chatbot.","published","An AI assistant that answers admitted and current students' questions about admissions, financial aid, registration, housing and deadlines by text message and web chat, sends timely reminders for the tasks each student still has to complete, and hands personal or complex cases to staff.",[12,13,14,15],"university admissions chatbot","summer melt chatbot","student services chatbot","enrollment nudging assistant",[17],"education",[19,20],"customer-service","operations",[22,23,24],"conversational-agent","rag-knowledge-assistant","classification-and-routing",[26,27,28,29],"sms","web-chat","whatsapp","mobile-app","customer-facing","supervised-agent","early-adopters","Between acceptance and the first day of class, students face a string of administrative hurdles:\nfinancial aid forms, verification documents, immunization records, placement tests, housing and\nregistration. Students without someone to guide them can stall at any one of these steps, and\nsome simply never show up, a pattern known as summer melt. Georgia State University describes it\nas a problem for at risk students, especially those from urban school districts.\n\nAdmissions and student service offices cannot answer thousands of repetitive questions at the\nmoment students ask them, often in the evening and at weekends, and mass emails go unread. The\nsame pattern continues after enrollment: students do not know which office to contact, deadlines\npass, and staff spend their time on questions a published policy already answers instead of on\nthe students who need a person.",[35],{"statement":36,"sourceTitle":37,"sourceUrl":38,"year":39},"Georgia State University, listing Lindsay Page and Ben Castleman's book Summer Melt (2014) among its sources, reports that as many as 20 percent of students from urban school districts who are admitted to college and confirm their intent to enroll never attend any post secondary institution.","Reduction of Summer Melt (Internet Archive snapshot)","https://web.archive.org/web/20260514132005/https://success.gsu.edu/reduction-of-summer-melt/",2014,"1. **Know each student's open tasks.** The assistant reads from the student information system\n   which steps each admitted or current student has still to complete: aid documents, deposits,\n   immunizations, orientation, registration.\n2. **Nudge at the right time.** It sends short, personal reminders by text message before each\n   deadline, and short surveys (for example intent to enroll) whose answers flow back to staff.\n3. **Answer questions around the clock.** Students reply or ask in their own words; answers come\n   from the institution's approved policies, deadlines and office information.\n4. **Route what needs a person.** Questions about a student's own aid package, a crisis,\n   wellbeing or anything the knowledge base does not cover go to the right office with the\n   conversation attached.\n5. **Learn from the questions.** Staff review the most common questions and failed answers to fix\n   confusing web pages and processes, not only the bot.",[42,43,44,45],"inclusion-and-access","customer-experience","employee-productivity","cost-to-serve",[47,48,49,50,51,52],"interactions-handled","hours-saved","users-served","contact-deflection","customer-satisfaction","response-time-reduction",{"referenceOrg":54,"inputs":55,"formula":84,"currency":85,"period":86,"resultLabel":87,"caveat":88},"A public university with 30,000 students and 6,000 new students a year",[56,63,70,77],{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"questions","Student questions and replies per year across admissions and student services",100000,200000,"messages per year","Editorial assumption. For scale, Georgia State reports more than 200,000 answers to incoming students in one summer, and Mainstay reports 81,167 messages handled in a year by Adelphi University's assistant.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"handledShare","Share of messages the assistant handles without staff",0.5,0.8,"fraction of messages","Editorial assumption, replace with your own data after a first term.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"minutesPerMessage","Staff minutes per message",1,2,"minutes per message","Mainstay's case study for Adelphi University assumes approximately one minute per message; the high value allows for emails and calls that take longer. Editorial assumption, replace with your own.",{"key":78,"label":79,"low":80,"high":81,"unit":82,"note":83},"staffCostPerHour","Fully loaded cost of a staff hour",30,45,"USD per hour","Editorial assumption, replace with your own cost.","questions * handledShare * minutesPerMessage / 60 * staffCostPerHour","USD","per year","Staff time released for student support","Counts staff time only. It leaves out the larger effect that institutions such as Georgia State report, more admitted students actually enrolling, and the cost of the platform, integration and content upkeep.",[],{"complexity":91,"complexityNote":92,"dataPrerequisites":93,"integrations":97},"medium","Answering general questions is straightforward. The value comes from personal nudges, which need a clean feed of each student's open tasks from the student information system, consent for text messaging and a working handover to several offices.",[94,95,96],"Current, owned content per office (admissions, aid, registrar, housing, bursar)","Each student's open enrollment tasks and deadlines from the student information system","Mobile numbers with consent to receive text messages",[98,99,100,101],"Student information system and admissions CRM","Messaging (SMS, WhatsApp) and web chat on the institution's site","Ticketing or case routing to each student service office","Single sign on for questions about a student's own record",{"steps":103,"guardrails":119,"humanInTheLoop":125,"kpisToInstrument":126,"failureModes":132},[104,107,110,113,116],{"title":105,"detail":106},"Map the drop off points","List the steps between acceptance and the first day (or between terms) where students stall, and the questions they ask at each. Use last year's data on who did not show up.",{"title":108,"detail":109},"Clean up the content first","Give each office ownership of its answers with a review date. The assistant is only as good as the policies and deadlines behind it.",{"title":111,"detail":112},"Design nudges with the offices","Agree the reminder calendar and wording with admissions, aid and the registrar, keep messages short and personal, and respect quiet hours and opt outs.",{"title":114,"detail":115},"Define the handover","Decide which topics go to which office and within what time, and pass the conversation along so students do not repeat themselves.",{"title":117,"detail":118},"Measure against a comparison group","Where possible, compare enrollment and task completion with students who did not receive the assistant, as Georgia State did in a randomized trial, rather than counting messages alone.",[120,121,122,123,124],"Answers only from approved institutional content, with a refusal and a route to staff otherwise","No decisions on admission, aid or placement; the assistant informs and reminds","Opt in and opt out for text messaging, with quiet hours","Crisis and wellbeing keywords routed to trained staff immediately","Personal data minimized in messages and masked in logs","Staff in each office own their content and handle every case that concerns a student's own record, money or wellbeing. A cross office group decides campaign strategy, timing and wording, as the task force Adelphi University set up does. Someone should also review unanswered questions and handovers every week.",[127,128,129,130,131],"Enrollment or task completion rate versus a comparison group","Messages handled without staff and handover rate by topic","Response rate to reminders and surveys","Student satisfaction with answers","Opt out rate from text messaging",[133,136,139,142],{"title":134,"detail":135},"Nudges that become noise","Too many or generic messages lead to opt outs, and billing reminders crowd out help. Coordinate campaigns centrally and keep them relevant to each student's open tasks.",{"title":137,"detail":138},"Stale answers","Deadlines and policies change each term. Give every answer an owner and a review date.",{"title":140,"detail":141},"Bot as a wall","Students with urgent or personal problems cannot reach a person. Make handover easy and fast, and monitor repeat questions.",{"title":143,"detail":144},"Counting messages, not outcomes","Message volume says little about whether more students enrolled or completed tasks. Measure outcomes against a comparison group.",{"euAiAct":146,"regulations":149,"guidance":155,"controls":172,"incidents":178},{"tier":147,"basis":148},"context-dependent","An assistant that answers questions and sends reminders falls under the transparency duty of Article 50. It becomes high risk under Annex III point 3(a) if it is used to determine access or admission or to assign students to institutions, and under point 3(c) if it assesses the level of education a student will receive. Keep admission and placement decisions with staff.",[150,151,152,153,154],"eu-ai-act","gdpr","uk-gdpr","us-tcpa","nist-ai-rmf",[156,162,166],{"title":157,"issuer":158,"region":159,"url":160,"note":161},"Annex III, high risk AI systems (point 3, education and vocational training)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Lists AI that determines access or admission to educational institutions as high risk.",{"title":163,"issuer":158,"region":159,"url":164,"note":165},"Article 50, transparency obligations for providers and deployers of certain AI systems","https://artificialintelligenceact.eu/article/50/","People must be informed that they are interacting with an AI system unless this is obvious from the context.",{"title":167,"issuer":168,"region":169,"url":170,"note":171},"Protecting Student Privacy","US Department of Education, Student Privacy Policy Office","north-america","https://studentprivacy.ed.gov/","Guidance and resources on student privacy law (FERPA), relevant when the assistant uses education records to personalize messages.",[173,174,175,176,177],"AI disclosure in the first message and on the chat widget","Consent records for text messaging and an easy opt out","Content ownership and review dates per office","Data protection impact assessment covering education records used for personalization","Weekly review of unanswered questions and handovers",[],{"howToBuild":180},"On Blits.ai this is an **AI agent** grounded in a **knowledge base** per office (admissions, aid,\nregistrar, housing), built from uploaded policies and **crawled website pages** with hybrid\nretrieval, so answers match the institution's own wording. **Custom functions** read each\nstudent's open tasks from the student information system through REST calls. A scheduled\n**agentic workflow** or **agentic task** sends each reminder before its deadline through a\n**custom function** that calls the institution's messaging provider, or through the outbound\n**email** channel.\n\nStudents' replies and questions reach the same agent on the **SMS**, **WhatsApp** or **web chat**\nchannel, with multi language support for international students and families. **Human\nhandover** rules route personal cases to the right office with the conversation attached.\n**Guardrails**, including the deterministic content scanner's self harm lexicon, flag risky\nmessages, and handover rules send them to trained staff. **PII masking** keeps student data out\nof model prompts, **analytics** show the top questions and unanswered topics each week, and the\n**GDPR toolkit** handles consent and data removal requests.",[182,185,188,191],{"question":183,"answer":184},"Can a chatbot really reduce summer melt?","Georgia State University reports that in the first summer its Pounce chatbot delivered more than 200,000 answers to incoming students, and that a new student portal and Pounce together reduced summer melt by 22 percent, an additional 324 students in class on the first day. Separately, in a randomized control trial the university saw a four percent overall decrease in the share of confirmed freshmen who did not enroll, and it says those gains came from the students who had access to Pounce.",{"question":186,"answer":187},"Do students actually respond to text message nudges and surveys?","Mainstay's case study for Austin Peay State University reports that the university's first intent to enroll campaign by text, in 2019, received a 40% response rate, and that staff could see which students planned to attend within one day. The vendor says the same task would have taken over a month by paper survey. It gives no enrollment figures, so this shows engagement, not outcomes.",{"question":189,"answer":190},"How much staff time does it save?","Mainstay's case study for Adelphi University reports 81,167 messages handled by its assistant Adele in the past year, which it estimates at 1,353 staff hours assuming approximately one minute per message. That is the vendor's estimate, not a time study. Georgia State's assistant vice president of undergraduate admissions said the university would have needed 10 more full time staff to handle the volume without Pounce.",{"question":192,"answer":193},"Is an admissions chatbot high risk under the EU AI Act?","Not if it only informs and reminds; then the transparency duty applies. It is high risk under Annex III point 3(a) if it determines access or admission, so admission decisions should stay with admissions staff.",[],"2026-09-27",[197],{"date":195,"note":198},"First published","student-enrollment-and-services-assistant",[201,239,265],{"title":202,"useCases":203,"organization":204,"vendors":208,"summary":212,"stage":213,"year":214,"channels":215,"languages":216,"metrics":218,"outcomeDisclosed":226,"sources":227,"verification":234,"grade":236,"id":237,"organizationSlug":238},"Georgia State University: Pounce chatbot for incoming students and summer melt",[199],{"name":205,"anonymized":206,"country":207,"region":169,"industry":17},"Georgia State University",false,"US",[209],{"name":210,"role":211},"AdmitHub (now Mainstay)","platform","Georgia State University combined a new student portal, which guides incoming students through the steps needed before the first day of classes (such as financial aid documents, immunization records, placement exams and class registration), with \"Pounce\", an AI enhanced chatbot that answers their questions around the clock by text message. The assistant vice president of undergraduate admissions said every interaction was tailored to the specific student's enrollment task. In its first summer (2016) Pounce delivered more than 200,000 answers and the university, with the portal and the chatbot together, reduced summer melt by 22 percent, an additional 324 students in class on the first day. Separately, in a randomized control trial the university saw a four percent overall decrease in the share of confirmed freshmen who did not enroll, and it says those gains came from the students who had access to Pounce. The same executive said the university would otherwise have needed 10 more full time staff to handle the volume of messaging.","scaled",2016,[26],[217],"en",[219],{"kpi":47,"value":60,"unit":220,"qualifier":221,"period":222,"claimant":223,"quote":224,"sourceUrl":225},"count","at-least","first summer of implementation, 2016","organization","In 2016, during the first summer of implementation, Pounce delivered more than 200,000 answers to questions asked by incoming freshmen, and the university reduced summer melt by 22 percent.","https://success.gsu.edu/reduction-of-summer-melt/",true,[228,230],{"url":225,"title":229,"publisher":205,"archivedUrl":38},"Reduction of Summer Melt",{"url":231,"title":232,"publisher":233},"https://mainstay.com/about/","Mainstay: Our Story, Mission, Values, and Team","Mainstay",{"level":235,"checkedAt":195},"source-verified","B","georgia-state-university-pounce-enrollment-chatbot",null,{"title":240,"useCases":241,"organization":242,"vendors":244,"summary":246,"stage":247,"year":248,"channels":249,"languages":250,"metrics":251,"outcomeDisclosed":226,"sources":259,"verification":262,"grade":263,"id":264,"organizationSlug":238},"Adelphi University: Adele, an AI assistant for student questions and text reminders",[199],{"name":243,"anonymized":206,"country":207,"region":169,"industry":17},"Adelphi University",[245],{"name":233,"role":211},"Adelphi University launched Adele, a conversational AI assistant on the Mainstay platform, on its website in January 2022 and extended it to two way text messaging for about 6,000 current students in March 2023. Adele sends reminders about academic and financial deadlines, answers questions from a shared knowledge base, enabled generative AI in July 2025 and requests a human from the right office by email when needed. A cross office task force coordinates campaigns, and the university adopted a policy for text messaging in June 2024. The vendor reports 81,167 messages handled by the bot in the past year. Assuming approximately one minute per message, the vendor estimates this at 1,353 staff capacity hours; that is a modelled figure, not a measured saving, so it is not recorded as a metric.","production",2022,[27,26],[217],[252],{"kpi":47,"value":253,"unit":220,"qualifier":254,"period":255,"claimant":256,"quote":257,"sourceUrl":258},81167,"exact","in the past year, per the case study","vendor","81,167 messages handled by the bot","https://mainstay.com/case-study/adelphi-student-support-capacity-case-study/",[260],{"url":258,"title":261,"publisher":233},"When IT Thinks Beyond IT: How Adelphi Gained Capacity by Reducing Barriers for Students",{"level":235,"checkedAt":195},"C","adelphi-university-adele-student-assistant",{"title":266,"useCases":267,"organization":268,"vendors":270,"summary":272,"stage":247,"year":273,"channels":274,"languages":275,"metrics":276,"outcomeDisclosed":206,"sources":277,"verification":281,"grade":263,"id":282,"organizationSlug":238},"Austin Peay State University: text message chatbot for incoming students",[199],{"name":269,"anonymized":206,"country":207,"region":169,"industry":17},"Austin Peay State University",[271],{"name":233,"role":211},"Austin Peay State University introduced a Mainstay text message chatbot, \"The Gov\", in 2017 to send incoming students orientation information and nudges, and later interactive surveys. Its first intent to enroll campaign in 2019 received a 40% response rate within a day, which Mainstay's case study says would have taken more than a month by paper survey, and showed staff which students still planned to attend. According to the case study (about 2020), the chatbot was also used for housing, advising and event updates.",2017,[26],[217],[],[278],{"url":279,"title":280,"publisher":233},"https://mainstay.com/case-study/austin-peay-state-university/","Austin Peay State University cuts summer melt and sees record-breaking enrollment",{"level":235,"checkedAt":195},"austin-peay-state-university-enrollment-chatbot",0,[285],{"kpi":47,"label":286,"unit":220,"aggregate":206,"higherIsBetter":226,"n":74,"nUpTo":283,"median":287,"min":253,"max":60,"byClaimant":288,"vendorOnly":206,"points":289},"Interactions handled",140583.5,{"organization":73,"vendor":73,"regulator":283,"independent":283},[290,291],{"evidenceId":237,"organization":205,"value":60,"qualifier":221,"claimant":223,"grade":236,"pooled":226},{"evidenceId":264,"organization":243,"value":253,"qualifier":254,"claimant":256,"grade":263,"pooled":226},{"low":293,"high":294},25000,240000,[296,314,339,354],{"slug":297,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":303,"patterns":305,"audience":307,"autonomy":308,"adoptionStage":309,"segment":310,"evidenceCount":74,"publicEvidenceCount":74,"organizations":311,"bestGrade":236,"headline":238,"lastVerified":195,"indexable":226},"field-technician-copilot-and-dispatch","AI copilot for field technicians and dispatch optimization","Field technician copilot and dispatch","AI that decides whether a fault needs a site visit at all, predicts what work and parts a job will need, helps plan and update appointments, and gives technicians on site guided diagnosis and instant answers from manuals and past jobs, so more jobs are fixed on the first visit.",[302],"telecommunications",[304,20,19],"field-service",[306,23,22,24],"prediction-and-scoring","employee-facing","copilot","emerging","network",[312,313],"nbn","Openreach",{"slug":315,"title":316,"shortTitle":317,"definition":318,"status":9,"industries":319,"functions":322,"patterns":323,"audience":30,"autonomy":31,"adoptionStage":326,"segment":327,"evidenceCount":328,"publicEvidenceCount":74,"organizations":329,"bestGrade":236,"headline":332,"lastVerified":195,"indexable":226},"account-and-card-servicing-agent","AI agent for account and card servicing","Account and card servicing","An AI agent that resolves routine account and card requests end to end, such as balances, statements, card blocks and replacements, PIN resets and limit changes, across app, web, messaging and phone, and hands anything sensitive or unusual to a human with the full context.",[320,321],"banking","payments",[19,20],[22,324,325,23],"voice-agent","agentic-workflow","mainstream","front-office",4,[330,331],"Commonwealth Bank of Australia","DBS Bank",{"kpi":333,"label":334,"unit":335,"n":74,"nUpTo":283,"kind":336,"value":337,"qualifier":338,"claimant":223,"organization":331,"vendorReported":206},"containment-rate","Containment rate","percent","reported",90,"approximately",{"slug":340,"title":341,"shortTitle":342,"definition":343,"status":9,"industries":344,"functions":346,"patterns":348,"audience":30,"autonomy":31,"adoptionStage":32,"evidenceCount":349,"publicEvidenceCount":349,"organizations":350,"bestGrade":236,"headline":238,"lastVerified":195,"indexable":226},"apartment-leasing-and-resident-service-agent","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.",[345],"real-estate",[19,347,20],"sales",[22,324,325,23],3,[351,352,353],"Asset Living","AvalonBay Communities","Equity Residential",{"slug":355,"title":356,"shortTitle":357,"definition":358,"status":9,"industries":359,"functions":360,"patterns":362,"audience":30,"autonomy":31,"adoptionStage":32,"segment":327,"evidenceCount":328,"publicEvidenceCount":349,"organizations":364,"bestGrade":236,"headline":238,"lastVerified":195,"indexable":226},"card-dispute-and-chargeback-intake","AI agent for card dispute intake","Card dispute intake","A customer facing AI agent that handles the \"I do not recognise this charge\" moment: it finds the transaction, separates suspected fraud from merchant disputes and simple confusion, explains the customer's rights and timelines, collects the details and evidence the rules require, and opens a correctly classified dispute case for the operations team.",[320,321],[19,361,20],"fraud-prevention",[22,324,24,363,325],"document-processing",[330,365,366],"Klarna","Visa",{"indexable":226,"reasons":368},[],[370,375,380,388,394,400,406,413,421,428,435,441,448,455,461,466,473,479,485,491,497,502,508,513,518,525,532,537,543,551,557,563,569,574],{"id":150,"label":371,"issuer":158,"region":159,"url":372,"description":373,"useCases":374,"indexable":226},"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":151,"label":376,"issuer":158,"region":159,"url":377,"description":378,"useCases":379,"indexable":226},"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":381,"label":382,"issuer":383,"region":384,"url":385,"description":386,"useCases":387,"indexable":226},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":154,"label":389,"issuer":390,"region":169,"url":391,"description":392,"useCases":393,"indexable":226},"NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":395,"label":396,"issuer":158,"region":159,"url":397,"description":398,"useCases":399,"indexable":226},"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":152,"label":401,"issuer":402,"region":159,"url":403,"description":404,"useCases":405,"indexable":226},"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":407,"label":408,"issuer":409,"region":159,"url":410,"description":411,"useCases":412,"indexable":226},"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":414,"label":415,"issuer":416,"region":417,"url":418,"description":419,"useCases":420,"indexable":226},"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":422,"label":423,"issuer":424,"region":417,"url":425,"description":426,"useCases":427,"indexable":226},"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":429,"label":430,"issuer":431,"region":384,"url":432,"description":433,"useCases":434,"indexable":226},"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":436,"label":437,"issuer":438,"region":169,"url":439,"description":440,"useCases":434,"indexable":226},"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":442,"label":443,"issuer":444,"region":159,"url":445,"description":446,"useCases":447,"indexable":226},"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.",16,{"id":449,"label":450,"issuer":451,"region":384,"url":452,"description":453,"useCases":454,"indexable":226},"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":456,"label":457,"issuer":158,"region":159,"url":458,"description":459,"useCases":460,"indexable":226},"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":462,"label":463,"issuer":158,"region":159,"url":464,"description":465,"useCases":460,"indexable":226},"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":467,"label":468,"issuer":469,"region":169,"url":470,"description":471,"useCases":472,"indexable":226},"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":474,"label":475,"issuer":158,"region":159,"url":476,"description":477,"useCases":478,"indexable":226},"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":480,"label":481,"issuer":482,"region":169,"url":483,"description":484,"useCases":478,"indexable":226},"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":486,"label":487,"issuer":488,"region":384,"url":489,"description":490,"useCases":478,"indexable":226},"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":492,"label":493,"issuer":158,"region":159,"url":494,"description":495,"useCases":496,"indexable":226},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":153,"label":498,"issuer":499,"region":169,"url":500,"description":501,"useCases":496,"indexable":226},"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":503,"label":504,"issuer":416,"region":417,"url":505,"description":506,"useCases":507,"indexable":226},"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":509,"label":510,"issuer":158,"region":159,"url":511,"description":512,"useCases":507,"indexable":226},"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":514,"label":515,"issuer":158,"region":159,"url":516,"description":517,"useCases":507,"indexable":226},"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":519,"label":520,"issuer":521,"region":159,"url":522,"description":523,"useCases":524,"indexable":226},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":526,"label":527,"issuer":528,"region":169,"url":529,"description":530,"useCases":531,"indexable":226},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":533,"label":534,"issuer":158,"region":159,"url":535,"description":536,"useCases":531,"indexable":226},"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":538,"label":539,"issuer":158,"region":159,"url":540,"description":541,"useCases":542,"indexable":226},"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":544,"label":545,"issuer":546,"region":547,"url":548,"description":549,"useCases":550,"indexable":226},"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":552,"label":553,"issuer":554,"region":159,"url":555,"description":556,"useCases":328,"indexable":226},"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":558,"label":559,"issuer":560,"region":159,"url":561,"description":562,"useCases":328,"indexable":226},"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":564,"label":565,"issuer":566,"region":417,"url":567,"description":568,"useCases":349,"indexable":226},"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":570,"label":571,"issuer":158,"region":159,"url":572,"description":573,"useCases":349,"indexable":226},"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":575,"label":576,"issuer":577,"region":169,"url":578,"description":579,"useCases":349,"indexable":226},"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.",1790598297286]