[{"data":1,"prerenderedAt":538},["ShallowReactive",2],{"uc-predictive-retention-analytics-and-advisor-outreach":3,"uc-regulations":315},{"useCase":4,"evidence":188,"blitsAiDeployments":238,"benchmarks":239,"indicative":240,"related":243,"indexability":313,"includeUnpublished":194},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":18,"patterns":21,"channels":25,"audience":29,"autonomy":30,"adoptionStage":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":43,"macroEstimates":76,"feasibility":77,"implementation":90,"risk":131,"blitsAi":164,"faq":166,"related":179,"datePublished":183,"dateModified":183,"lastVerified":183,"changelog":184,"slug":187},"AI predictive analytics for student retention and advisor outreach","Student retention analytics","AI student retention analytics for advisors","EAB credits daily advisor use of Navigate360 with a rise in Georgia State graduation rates, and its alerts with a 73 point drop in Auburn engineering referrals.","published","A predictive model that scans each enrolled student's academic and engagement data daily against a broad set of risk factors, alerts an advisor as soon as a student drifts off track (a failing grade in a required course, a missed registration deadline, disengagement), and triggers a timely, personal outreach so a person, not just a data point, follows up while there is still time to help.",[12,13,14,15],"early alert system","predictive student success analytics","at risk student flagging","advising analytics platform",[17],"education",[19,20],"customer-service","operations",[22,23,24],"prediction-and-scoring","classification-and-routing","conversational-agent",[26,27,28],"sms","email","web-chat","customer-facing","supervised-agent","mainstream","A student can fall behind for many small reasons: a failing grade in a course required for their\nmajor, a missed registration deadline, a balance that blocks enrolment, or simply drifting away\nfrom campus life, and by the time grades or graduation data show the problem, the moment to help\nhas often passed. Advisors cannot manually track thousands of students against every risk signal\nevery day, so intervention has traditionally depended on a student asking for help or a professor\nhappening to notice.\n\nEAB's case study says that ten years before it was written, Georgia State University's six year\ngraduation rate hovered around 32%, especially low for its growing population of Pell Grant\nstudents, low income students who receive federal need based aid. More than 850 institutions\nnow use a vendor supplied early alert and advising platform,\nEAB's Navigate360, according to EAB, which shows how far predictive, alert driven advising has\nmoved from a handful of early deployments to a category of software many institutions now run.",[],"1. **Track every student daily.** The platform reads grades, attendance, financial holds,\n   registration status and other institutional data for each enrolled student against a large set\n   of predefined risk factors, refreshed daily.\n2. **Flag a deviation from the path.** When a student's data crosses a risk threshold, whether a\n   grade, a missed deadline, a balance or an engagement signal, the system generates an alert tied\n   to that specific risk, not a generic warning.\n3. **Route the alert to a person, fast.** The alert reaches the student's advisor, who is expected\n   to make contact within a short, defined window, so the flag turns into a real conversation\n   rather than sitting in a queue.\n4. **Reach the student where they are.** Outreach follows by text, email or a scheduled advising\n   meeting, phrased around the specific issue the alert raised, not a form letter.\n5. **Remove the barrier the alert found.** Some institutions pair the alerting system with a direct\n   intervention, such as a small completion grant that clears a blocking balance, when the flag is\n   financial rather than academic.\n6. **Learn from what worked.** Advisors and institutional researchers review which alerts led to a\n   successful intervention and which did not, and adjust the risk factors and outreach playbooks\n   over time.",[36,37,38],"inclusion-and-access","customer-experience","employee-productivity",[40,41,42],"interactions-handled","response-time-reduction","users-served",{"referenceOrg":44,"inputs":45,"formula":71,"currency":72,"period":73,"resultLabel":74,"caveat":75},"A university with 25,000 undergraduates",[46,51,58,65],{"key":47,"label":48,"low":49,"high":49,"unit":47,"note":50},"students","Undergraduate students tracked",25000,"Editorial assumption, broadly similar in scale to the total enrollment (25,945) EAB reports for Georgia State University.",{"key":52,"label":53,"low":54,"high":55,"unit":56,"note":57},"alertRate","Students who receive at least one risk alert per year",0.3,0.5,"fraction of students per year","Editorial assumption, replace with your own alert volume after a first term.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"minutesPerAlert","Advisor minutes per alert followed up, from outreach to resolution",15,30,"minutes per alert","Editorial assumption, replace with your own time study.",{"key":66,"label":67,"low":62,"high":68,"unit":69,"note":70},"staffCostPerHour","Fully loaded cost of an advisor hour",45,"USD per hour","Editorial assumption, replace with your own cost.","students * alertRate * minutesPerAlert / 60 * staffCostPerHour","USD","per year","Advisor time directed at students flagged as off track","Counts advisor time engaged, not saved: the goal of this use case is to spend more staff time on the students who need it, not less time overall. It leaves out the value of any resulting graduation rate gain, the cost of the platform and the advising capacity an institution must add for alerts to lead to real contact rather than a longer queue.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":85},"high","The predictive model itself is usually bought from a vendor, not built in house, but making it work needs a clean, daily feed from the student information system, a defined set of risk factors an institution actually believes in, enough advising capacity to turn every alert into a real, fast conversation, and a plan for barriers, such as an unpaid balance, that a conversation alone cannot fix.",[81,82,83,84],"Daily feed of grades, attendance, registration status and financial holds per student","A defined, reviewed set of risk factors and thresholds, owned by academic leadership","Advisor capacity and a service standard for how fast an alert becomes real contact","Consent for text messaging and clear, accessible opt out",[86,87,88,89],"Student information system","Advising CRM or case management platform","Messaging (SMS, email) for outreach","Financial aid and student accounts system, where alerts are balance related",{"steps":91,"guardrails":107,"humanInTheLoop":112,"kpisToInstrument":113,"failureModes":118},[92,95,98,101,104],{"title":93,"detail":94},"Add advising capacity before you add alerts","An alert that nobody has time to follow up on is worse than no alert at all. At Auburn University, a dedicated counselor advises every student an alert flags in the Engineering programme, per EAB's case study; plan that kind of staffing before turning the system on, not after the alert backlog appears.",{"title":96,"detail":97},"Choose risk factors leadership will act on","A long list of statistically significant risk factors is not the same as a short list advisors trust and will actually follow up on. Start with a handful tied to a clear, actionable next step.",{"title":99,"detail":100},"Pair academic alerts with a way to remove financial barriers","Some students are on track academically but held back by a modest unpaid balance rather than a grade or a deadline. A completion or fee drop grant that clears this kind of barrier once an alert or an advisor surfaces it can help; Georgia State, for example, used fee drop grants alongside its alerting platform. Decide up front whether your institution has an equivalent.",{"title":102,"detail":103},"Personalize the outreach, do not template it","Tie each message to the specific alert (a course, a deadline, a balance), respect quiet hours and consent, and give a clear, easy way to reply or opt out.",{"title":105,"detail":106},"Measure against a comparison group, not alert volume","Alert and meeting counts show activity, not impact. Where possible, compare retention or graduation outcomes between students exposed to the alert and outreach system and those who were not, on comparable cohorts.",[108,109,110,111],"No decision about admission, financial aid eligibility or academic standing made by the system alone","A defined service standard for how quickly an alert must reach a real conversation","Consent, quiet hours and an easy opt out for text message outreach","Wellbeing or crisis language in a student's reply routed to trained staff immediately, not the automated flow","Advisors decide what to say and do with every flagged student; the system surfaces the alert and can send the first outreach message, but a person owns the actual conversation and any decision that affects the student's standing, aid or enrollment. Academic leadership reviews the risk factors, thresholds and outcomes on a set schedule, not just at go live.",[114,115,116,117],"Time from alert to advisor's first real contact with the student","Retention and completion outcomes for flagged students who received outreach versus a comparison group","Advisor caseload and time spent per alert, to catch overload before it causes silent backlog","Opt out rate and complaints about the frequency or tone of outreach",[119,122,125,128],{"title":120,"detail":121},"Alerts that pile up faster than advisors can act","A predictive system can generate more flags than an advising team can meaningfully follow up on, so alerts sit unread. Staff for the volume the system will actually produce, and monitor the backlog, not just the alert count.",{"title":123,"detail":124},"Counting meetings, not outcomes","A large number of advisor meetings prompted by alerts says nothing about whether students actually stayed enrolled or graduated faster. Measure against a comparison group.",{"title":126,"detail":127},"A risk factor that quietly encodes bias","A predictive model trained on historical outcomes can learn patterns correlated with race, income or first generation status without anyone intending it. Review which factors drive alerts and whether outcomes differ by student group.",{"title":129,"detail":130},"Outreach that feels like surveillance","Students who realize they are being tracked against a set of risk factors, without being told, can react with mistrust rather than engagement. Disclose that the institution uses predictive alerts to support students, and what data that involves.",{"euAiAct":132,"regulations":135,"guidance":140,"controls":157,"incidents":163},{"tier":133,"basis":134},"context-dependent","The risk score itself, used only to inform an advisor and prompt a human conversation, sits outside Annex III. It moves toward high risk under Annex III point 3(b) if its output is used to evaluate a student's learning outcomes or to steer the learning process, rather than only to prompt a conversation; a score built mainly on grades and coursework can come close to that line. Once a student replies, the AI agent that answers them and sends outreach messages interacts directly with a natural person and falls under the Article 50 transparency duty, so the student must be told they are talking to an AI system unless this is obvious from context. Keep the actual decision about a student's support or standing with a person to stay on the lower tier.",[136,137,138,139],"eu-ai-act","gdpr","uk-gdpr","us-tcpa",[141,147,151],{"title":142,"issuer":143,"region":144,"url":145,"note":146},"Annex III, high risk AI systems (point 3, education and vocational training)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Lists evaluating learning outcomes, including when used to steer the learning process, as a high risk education use.",{"title":148,"issuer":143,"region":144,"url":149,"note":150},"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":152,"issuer":153,"region":154,"url":155,"note":156},"Protecting Student Privacy","US Department of Education, Student Privacy Policy Office","north-america","https://studentprivacy.ed.gov/","Guidance on FERPA, relevant when the platform draws on education records to generate and route a risk alert.",[158,159,160,161,162],"Disclosure to students that predictive alerts and outreach are in use, and what data feeds them","Advisor, not system, ownership of every decision affecting a student's standing or aid","A defined service standard for time from alert to human contact","Review of risk factors and outcomes by student group on a set schedule","Consent and opt out records for text message outreach",[],{"howToBuild":165},"Blits.ai does not build the predictive risk model itself; the daily scoring against a set of\nacademic and engagement risk factors normally comes from the institution's student information\nsystem or a dedicated early alert platform. What Blits.ai builds well is the outreach and case\nworkflow once a flag exists. The institution's early alert platform triggers an **agentic\nworkflow** on Blits.ai, via an API token or the **REST API** channel, each time a new alert\nfires; the workflow creates a case, notifies the assigned advisor and sends a first,\npersonalized outreach message tied to the specific alert, through **SMS**, **WhatsApp** or the\noutbound **email** channel.\n\nStudents who reply reach an **AI agent** grounded in a **knowledge base** of the institution's own\nsupport resources (tutoring, financial aid, advising hours), which answers general questions and\noffers to book a meeting, while **guardrails**, including the deterministic content scanner's\nself harm lexicon, catch wellbeing or crisis language and trigger **human handover** to trained\nstaff immediately. **PII masking** keeps student data out of model prompts, and **run history**,\na full audit trail per run plus downloadable run data, gives advising leadership an auditable\nrecord of every alert, message and outcome, which they can break down by alert type for the\nretention review the institution runs each term.",[167,170,173,176],{"question":168,"answer":169},"Does predictive alerting and outreach actually improve graduation rates?","EAB, the vendor, credits Georgia State University's advisors' daily use of its Navigate360 platform with contributing to a rise in the university's six year graduation rate since 2012, though EAB's case study does not isolate the alerting platform from the course redesign, supplemental instruction, freshmen learning communities and fee drop grants Georgia State introduced alongside it. In a separate case study, EAB credits Auburn University's use of Navigate360 alerts, followed up by a dedicated counselor, with a 73 percentage point drop in students referred out of its Engineering major within three years. Both figures come from the vendor's own case studies of named customers, not an independent study.",{"question":171,"answer":172},"How fast does an alert need to reach a student?","The sources behind this page do not state a specific response time standard. What they show is that a predictive system only works alongside enough advising capacity, such as Auburn's dedicated counselor for every flagged engineering student, to turn a flag into a real conversation; a system that generates alerts faster than advisors can act on them creates a backlog, not an intervention.",{"question":174,"answer":175},"Can this replace an academic advisor?","No. At Auburn University, per EAB, Navigate360 alerts flag at risk engineering students and a dedicated counselor then advises them; at Georgia State, EAB describes advisors using Navigate360 daily. In both cases, a person, an advisor or a dedicated counselor, owns the actual conversation, the judgment about what a student needs, and any decision that affects the student's standing or aid. The system's job is to make sure the right conversation happens sooner, not to have the conversation itself.",{"question":177,"answer":178},"Is flagging students with a predictive model regulated under the EU AI Act?","It depends on what the flag is used for and who it talks to. A risk score used only to inform an advisor sits outside Annex III unless it is used to evaluate a student's learning outcomes or steer the learning process, which moves it toward high risk under Annex III point 3(b). The AI agent that then messages the student directly falls under the Article 50 transparency duty, so the student must be told they are interacting with an AI system.",[180,181,182],"student-enrollment-and-services-assistant","academic-advising-assistant","financial-wellbeing-coach","2026-09-29",[185],{"date":183,"note":186},"First published","predictive-retention-analytics-and-advisor-outreach",[189,222],{"title":190,"useCases":191,"organization":192,"vendors":196,"summary":200,"stage":201,"year":202,"channels":203,"languages":204,"metrics":206,"outcomeDisclosed":207,"sources":208,"verification":217,"grade":219,"id":220,"organizationSlug":221},"Georgia State University: GPS Advising and Navigate360",[187],{"name":193,"anonymized":194,"country":195,"region":154,"industry":17},"Georgia State University",false,"US",[197],{"name":198,"role":199},"EAB","platform","Georgia State University joined EAB's Student Success Collaborative in 2012 and extended a data driven approach to academic advising, using EAB's Navigate360 platform alongside course redesign, supplemental instruction and fee drop grants. EAB's case study of the university states that GSU's advisors use Navigate360 daily and credits that daily use, together with the university's other student success measures, with contributing to a rise in GSU's six year graduation rate since 2012. Georgia State's own site names the wider effort GPS Advising, though EAB's case study does not use that name or describe how the platform's alerts are routed.","scaled",2026,[],[205],"en",[],true,[209,213],{"url":210,"title":211,"publisher":198,"date":212},"https://eab.com/why-eab/partner-stories/student-success-compendium/","Student Success Case Study Compendium","2026-07-15",{"url":214,"title":215,"publisher":193,"archivedUrl":216},"https://success.gsu.edu/","Student Success Initiatives at Georgia State University","https://web.archive.org/web/2026/https://success.gsu.edu/",{"level":218,"checkedAt":183},"source-verified","C","georgia-state-university-gps-advising-retention",null,{"title":223,"useCases":224,"organization":225,"vendors":227,"summary":229,"stage":201,"year":230,"channels":231,"languages":232,"metrics":233,"outcomeDisclosed":207,"sources":234,"verification":236,"grade":219,"id":237,"organizationSlug":221},"Auburn University: Navigate360 predictive alerts for at risk engineering students",[187],{"name":226,"anonymized":194,"country":195,"region":154,"industry":17},"Auburn University",[228],{"name":198,"role":199},"Auburn University, which has a 78% six year graduation rate and a 90% retention rate per EAB's case study, partnered with EAB in 2014 and implemented Navigate360 to reduce how many students in its College of Engineering were referred out of the major for falling below the required GPA. Within the Engineering programme, advising leadership uses Navigate360 alerts and cases to flag students at risk of not qualifying for the major, and a dedicated counselor then advises those students directly, enforcing positive academic behaviors. EAB's case study credits this predictive alert and advisor outreach model with a large drop in mandatory referrals out of the Engineering programme within three years.",2018,[],[205],[],[235],{"url":210,"title":211,"publisher":198,"date":212},{"level":218,"checkedAt":183},"auburn-university-navigate360-engineering-retention",0,[],{"low":241,"high":242},56250,281250,[244,259,275,296],{"slug":180,"title":245,"shortTitle":246,"definition":247,"status":9,"industries":248,"functions":249,"patterns":250,"audience":29,"autonomy":30,"adoptionStage":252,"evidenceCount":253,"publicEvidenceCount":253,"organizations":254,"bestGrade":257,"headline":221,"lastVerified":258,"indexable":207},"AI assistant for student enrollment and student services","Student enrollment assistant","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.",[17],[19,20],[24,251,23],"rag-knowledge-assistant","early-adopters",3,[255,256,193],"Adelphi University","Austin Peay State University","B","2026-09-27",{"slug":181,"title":260,"shortTitle":261,"definition":262,"status":9,"industries":263,"functions":264,"patterns":265,"audience":29,"autonomy":266,"adoptionStage":267,"evidenceCount":268,"publicEvidenceCount":268,"organizations":269,"bestGrade":257,"headline":221,"lastVerified":274,"indexable":207},"AI academic advising assistant for course selection and degree requirements","Academic advising assistant","An AI assistant that answers students' questions about degree requirements, course selection, prerequisites and majors, grounded in the institution's own catalog and advising documents, so students get quick answers to routine questions and are directed to a human advisor for anything that needs judgment, is time sensitive, or falls outside what the assistant can see.",[17],[19],[24,251],"assist","emerging",4,[270,271,272,273],"Elon University","Harvard College","University of Utah","Washburn University","2026-09-28",{"slug":182,"title":276,"shortTitle":277,"definition":278,"status":9,"industries":279,"functions":281,"patterns":283,"audience":29,"autonomy":30,"adoptionStage":252,"segment":286,"evidenceCount":287,"publicEvidenceCount":288,"organizations":289,"bestGrade":257,"headline":221,"lastVerified":258,"indexable":207},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","An in app AI assistant that the customer opens to understand their own money: it uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[280],"banking",[19,282],"marketing",[24,284,22,285],"recommendation-and-personalization","agentic-workflow","front-office",8,6,[290,291,292,293,294,295],"Bank of America","Commonwealth Bank of Australia","Hyundai Card","Royal Bank of Canada","Starling Bank","Westpac",{"slug":297,"title":298,"shortTitle":299,"definition":300,"status":9,"industries":301,"functions":303,"patterns":305,"audience":306,"autonomy":307,"adoptionStage":267,"segment":308,"evidenceCount":309,"publicEvidenceCount":309,"organizations":310,"bestGrade":257,"headline":221,"lastVerified":258,"indexable":207},"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",[22,251,24,23],"employee-facing","copilot","network",2,[311,312],"nbn","Openreach",{"indexable":207,"reasons":314},[],[316,321,326,334,341,347,353,360,368,375,382,389,395,401,408,414,420,427,432,438,444,451,456,463,468,473,478,484,491,496,504,510,516,522,527,532],{"id":136,"label":317,"issuer":143,"region":144,"url":318,"description":319,"useCases":320,"indexable":207},"EU AI Act","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",230,{"id":137,"label":322,"issuer":143,"region":144,"url":323,"description":324,"useCases":325,"indexable":207},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",207,{"id":327,"label":328,"issuer":329,"region":330,"url":331,"description":332,"useCases":333,"indexable":207},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":335,"label":336,"issuer":337,"region":154,"url":338,"description":339,"useCases":340,"indexable":207},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",92,{"id":138,"label":342,"issuer":343,"region":144,"url":344,"description":345,"useCases":346,"indexable":207},"UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":348,"label":349,"issuer":143,"region":144,"url":350,"description":351,"useCases":352,"indexable":207},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":354,"label":355,"issuer":356,"region":144,"url":357,"description":358,"useCases":359,"indexable":207},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",50,{"id":361,"label":362,"issuer":363,"region":364,"url":365,"description":366,"useCases":367,"indexable":207},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":369,"label":370,"issuer":371,"region":364,"url":372,"description":373,"useCases":374,"indexable":207},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":376,"label":377,"issuer":378,"region":154,"url":379,"description":380,"useCases":381,"indexable":207},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",22,{"id":383,"label":384,"issuer":385,"region":330,"url":386,"description":387,"useCases":388,"indexable":207},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":390,"label":391,"issuer":143,"region":144,"url":392,"description":393,"useCases":394,"indexable":207},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",17,{"id":396,"label":397,"issuer":398,"region":144,"url":399,"description":400,"useCases":394,"indexable":207},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",{"id":402,"label":403,"issuer":404,"region":154,"url":405,"description":406,"useCases":407,"indexable":207},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",16,{"id":409,"label":410,"issuer":411,"region":330,"url":412,"description":413,"useCases":61,"indexable":207},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":415,"label":416,"issuer":143,"region":144,"url":417,"description":418,"useCases":419,"indexable":207},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":421,"label":422,"issuer":423,"region":154,"url":424,"description":425,"useCases":426,"indexable":207},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":139,"label":428,"issuer":429,"region":154,"url":430,"description":431,"useCases":426,"indexable":207},"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":433,"label":434,"issuer":143,"region":144,"url":435,"description":436,"useCases":437,"indexable":207},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":439,"label":440,"issuer":441,"region":330,"url":442,"description":443,"useCases":437,"indexable":207},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":445,"label":446,"issuer":447,"region":154,"url":448,"description":449,"useCases":450,"indexable":207},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",11,{"id":452,"label":453,"issuer":143,"region":144,"url":454,"description":455,"useCases":450,"indexable":207},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":457,"label":458,"issuer":459,"region":144,"url":460,"description":461,"useCases":462,"indexable":207},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",10,{"id":464,"label":465,"issuer":363,"region":364,"url":466,"description":467,"useCases":462,"indexable":207},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",{"id":469,"label":470,"issuer":143,"region":144,"url":471,"description":472,"useCases":462,"indexable":207},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":474,"label":475,"issuer":143,"region":144,"url":476,"description":477,"useCases":462,"indexable":207},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":479,"label":480,"issuer":143,"region":144,"url":481,"description":482,"useCases":483,"indexable":207},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",9,{"id":485,"label":486,"issuer":487,"region":154,"url":488,"description":489,"useCases":490,"indexable":207},"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":492,"label":493,"issuer":143,"region":144,"url":494,"description":495,"useCases":288,"indexable":207},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":497,"label":498,"issuer":499,"region":500,"url":501,"description":502,"useCases":503,"indexable":207},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":505,"label":506,"issuer":507,"region":144,"url":508,"description":509,"useCases":268,"indexable":207},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":511,"label":512,"issuer":513,"region":144,"url":514,"description":515,"useCases":268,"indexable":207},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":517,"label":518,"issuer":519,"region":364,"url":520,"description":521,"useCases":253,"indexable":207},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":523,"label":524,"issuer":143,"region":144,"url":525,"description":526,"useCases":253,"indexable":207},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":528,"label":529,"issuer":143,"region":144,"url":530,"description":531,"useCases":253,"indexable":207},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":533,"label":534,"issuer":535,"region":154,"url":536,"description":537,"useCases":253,"indexable":207},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683492573]