[{"data":1,"prerenderedAt":553},["ShallowReactive",2],{"uc-automated-scoring-of-written-responses":3,"uc-regulations":343},{"useCase":4,"evidence":192,"blitsAiDeployments":262,"benchmarks":263,"indicative":264,"related":267,"indexability":341,"includeUnpublished":197},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":21,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"problem":30,"problemStats":31,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":51,"macroEstimates":78,"feasibility":79,"implementation":90,"risk":136,"blitsAi":174,"faq":176,"related":186,"datePublished":187,"dateModified":187,"lastVerified":187,"changelog":188,"slug":191},"AI scoring of essays and written answers in assessments","Essay and written answer scoring","AI essay scoring for tests and exams","Engines score essays and written answers while humans read a sample and every unclear case, as in Texas. Massachusetts had to rescore about 1,400 essays in 2025.","published","AI that scores students' essays and short written answers against a rubric, trained on responses scored by human raters, with human raters rescoring a sample of responses and every response the engine is unsure about. In hybrid programmes such as Texas, a human score is the score of record whenever a human scores a response.",[12,13,14,15],"automated essay scoring","AI grading of constructed responses","AI marking assistant","machine scoring of written answers",[17,18],"education","government",[20],"operations",[22,23],"classification-and-routing","prediction-and-scoring",[25,26],"api","internal-tools","back-office","supervised-agent","mainstream","Written answers show what students can do in ways multiple choice cannot, but every one has to be\nread against a rubric by a trained rater. When Texas redesigned its STAAR tests to include more\nwriting, the number of constructed responses to score each year grew six to sevenfold, and the\nagency estimated that scoring them all by hand would cost 15 to 20 million US dollars more per\nyear. Human scoring is not perfectly consistent either: in a TEA study of extended essays, two\ntrained raters gave exactly the same conventions score on only 67 to 72 percent of responses.\nIn classrooms, the UK Department for Education describes feedback and marking as a burden on\nteachers.\n\nAutomated essay scoring is not new: ETS has used its engine alongside human raters on the GRE\nsince at least 2012, and TEA said in 2024 that at least 21 states use automated scoring for their\nstate assessments. What has changed is scale and scope. State assessment programmes now let an\nengine give the first score for most written answers, and the UK Department for Education funds\nAI tools meant to reduce the burden of feedback and marking on teachers. The stakes are high: a wrong score can\naffect a student's record, a school's rating and public trust, as the Massachusetts rescoring of\nabout 1,400 essays in 2025 showed.",[32,37],{"statement":33,"sourceTitle":34,"sourceUrl":35,"year":36},"The Texas Education Agency says the STAAR redesign brought 6 to 7 times more constructed responses to grade each year, and that maintaining full human scoring would have cost 15 to 20 million US dollars more per year.","Hybrid Scoring Key Questions (Texas Education Agency, March 2024)","https://web.archive.org/web/20240512091507/https://tea.texas.gov/student-assessment/testing/hybrid-scoring-key-questions.pdf",2024,{"statement":38,"sourceTitle":34,"sourceUrl":35,"year":36},"In a Texas Education Agency study of spring 2023 STAAR extended constructed responses, two human raters gave exactly the same conventions score on 67 to 72 percent of responses, depending on the item.","1. **Set the standard with humans.** Educators score a set of field test responses against the\n   rubric and agree anchor responses for every score point.\n2. **Train and qualify the engine.** The engine is trained on human scored responses for each\n   question and must agree with human raters at the same rate human raters agree with one\n   another, with a similar score distribution, before it is used live.\n3. **Score and flag.** The engine gives each response a first score and a confidence value, and\n   flags responses that are blank, too short, off topic, copied, in another language or unlike\n   anything in its training data.\n4. **Route to humans.** Flagged and low confidence responses, and a fixed share of all responses,\n   go to trained human raters. Where a human scores a response, the human score is the score of\n   record.\n5. **Monitor and correct.** Agreement between engine and humans is monitored daily, preliminary\n   results go to schools with a window to report discrepancies, and rescoring is available.",[41,42,43,44],"cost-to-serve","speed","employee-productivity","compliance",[46,47,48,49,50],"cost-reduction","processing-time-reduction","accuracy","automation-rate","hours-saved",{"referenceOrg":52,"inputs":53,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"A state assessment programme scoring 1 million written responses a year",[54,60,67],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"responses","Written responses scored per year",1000000,"responses per year","The reference programme.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"engineOnlyShare","Share of responses scored by the engine without a human read",0.6,0.75,"fraction of responses","The Texas Education Agency routes at least 25 percent of responses, plus flagged and low confidence ones, to human raters, so at most 75 percent are engine only. The low value is an editorial assumption.",{"key":68,"label":69,"low":63,"high":70,"unit":71,"note":72},"humanScoringCost","Cost of human scoring per response",1.7,"USD per response","Derived from the Texas Education Agency deck cited above: 15 to 20 million US dollars a year avoided, spread over the roughly 75 percent of 15.8 million annual responses that the engine now scores alone and that were previously scored by two humans, is about 1.3 to 1.7 USD per response (about 0.6 to 0.8 USD per single read). The low value assumes one human read per response. Replace with your own contract rates.","responses * engineOnlyShare * humanScoringCost","USD","per year","Human scoring cost avoided","Gross avoided rater cost only. It leaves out engine licensing, validation studies per question, monitoring, rescoring and appeals, and the cost of errors, which can be large in reputation and student outcomes even when rare.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":86},"high","The engine is the smaller part. The work is psychometric: per question training and validation, agreement thresholds, fairness checks across student groups, confidence routing, daily monitoring and a rescoring process that schools and families trust.",[83,84,85],"Rubrics and anchor responses approved by educators for every question","Human scored responses for each question from field tests, enough to train and validate","Student group information to test for differences between engine and human scores",[87,88,89],"Test delivery platform that captures responses","Human scoring platform for routed responses and second reads","Results reporting to schools with a discrepancy and rescore process",{"steps":91,"guardrails":107,"humanInTheLoop":113,"kpisToInstrument":114,"failureModes":120},[92,95,98,101,104],{"title":93,"detail":94},"Decide where machine scoring is appropriate","Use it for tasks scored for writing quality or a clear rubric, not for tasks where the correctness of claims or creative reasoning is what counts. Keep human scoring for any language or test version the engine has not been validated on; Texas, for example, scores its Spanish STAAR responses entirely by hand.",{"title":96,"detail":97},"Validate per question","Require the engine to agree with human raters at the same rate human raters agree with one another, with a similar score distribution, on responses the engine has not seen, for every question.",{"title":99,"detail":100},"Route by confidence and condition","Send low confidence, borderline and unusual responses to humans, plus a fixed random share of all responses as an ongoing check. Make the human score the score of record.",{"title":102,"detail":103},"Check fairness","Compare engine and human scores for student groups (for example by language background and disability) and investigate systematic differences before release.",{"title":105,"detail":106},"Build in a discrepancy window","Release preliminary results to schools with time to report issues and a clear rescoring route, and publish how the process works.",[108,109,110,111,112],"Human rescoring of a fixed share of responses and of all low confidence or flagged responses","Human score as the score of record whenever a human scores a response","Per question validation against human agreement before live use","Daily monitoring of engine and human agreement during the scoring window","A discrepancy period and rescoring process for schools and families","Educators set the rubric and anchor responses; trained human raters score every flagged, low confidence and sampled response; scoring directors monitor agreement daily; and schools can challenge preliminary scores before results are final.",[115,116,117,118,119],"Exact and adjacent agreement between engine and human scores per question","Share of responses routed to humans, by reason","Differences between engine and human scores by student group","Rescore requests and changed scores after release","Cost and time per scored response",[121,124,127,130,133],{"title":122,"detail":123},"Systematic scoring errors on a set of responses","A technical issue makes the engine score a set of essays incorrectly, as with about 1,400 Massachusetts essays in 2025, which DESE attributed to a temporary technical issue in the process. Sample human reads across the score range and give schools a discrepancy window.",{"title":125,"detail":126},"Responses unlike the training data","Writing styles, structures or vocabulary that are rare in the training responses can be scored less reliably. Route responses the engine flags as unusual to humans and check agreement for different student groups.",{"title":128,"detail":129},"Gaming the engine","Answers written to exploit surface features an engine may reward, such as length or rubric vocabulary, can score higher than their content deserves. Flag unusual responses, keep a random share of human reads and review what drives scores.",{"title":131,"detail":132},"Scoring what the engine can see","Rubrics drift toward surface features such as length and punctuation. Keep educators in charge of the rubric and review what drives scores.",{"title":134,"detail":135},"Loss of trust","Families and teachers distrust machine scores when the process is opaque. Publish how scoring works and how to request a rescore.",{"euAiAct":137,"regulations":139,"guidance":145,"controls":163,"incidents":169},{"tier":80,"basis":138},"Annex III point 3(b): AI systems intended to be used to evaluate learning outcomes in educational and vocational training institutions at all levels are high risk. Scoring that determines access to an institution or the level of education a student will receive is also covered by points 3(a) and 3(c). Schools and exam bodies that use such a system have the deployer obligations of Article 26.",[140,141,142,143,144],"eu-ai-act","gdpr","uk-gdpr","nist-ai-rmf","iso-42001",[146,152,158],{"title":147,"issuer":148,"region":149,"url":150,"note":151},"Annex III, high risk AI systems (point 3, education and vocational training)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Lists AI systems intended to evaluate learning outcomes as high risk, which brings the requirements of Chapter III, Section 2 (Articles 8 to 15) on risk management, data governance, logging and human oversight, and the deployer obligations of Article 26.",{"title":153,"issuer":154,"region":155,"url":156,"note":157},"Scoring Process for STAAR Constructed Responses","Texas Education Agency","north-america","https://tea.texas.gov/data-reports/staar/scoring-process-staar-constructed-response-1.pdf","A published description of a hybrid scoring process, including engine qualification against human agreement, confidence and condition code routing and the human score as the score of record.",{"title":159,"issuer":160,"region":149,"url":161,"note":162},"Generative artificial intelligence (AI) in education","UK Department for Education","https://www.gov.uk/government/publications/generative-artificial-intelligence-in-education/generative-artificial-intelligence-ai-in-education","The department's position on generative AI tools in schools and colleges, including its funding for AI tools that aim to reduce the burden of feedback and marking on teachers; relevant when teachers use AI to mark classroom work.",[164,165,166,167,168],"Published description of the scoring process and how to request a rescore","Per question validation records and agreement thresholds","Fairness analysis across student groups before each release","Logging of engine scores, confidence values and routing decisions","Contract terms requiring the scoring vendor to report and correct errors",[170],{"title":171,"url":172,"note":173},"AI grading issue affects hundreds of MCAS essays in Massachusetts","https://www.nbcboston.com/investigations/ai-grading-massachusetts-mcas/3807392/","In 2025 the state's testing contractor Cognia found that roughly 1,400 MCAS essays (of about 750,000 statewide) had not received the correct scores under AI scoring, which DESE attributed to a temporary technical issue in the process. District leaders, including in Lowell, raised the problem after preliminary results were released, and the essays were rescored.",{"howToBuild":175},"On Blits.ai a scoring assistant is an **agentic workflow** that reads each response and applies\nthe rubric through an **AI agent** with **structured output** (score per trait, rationale and a\nconfidence value), using the approved anchor responses in a **knowledge base** as reference.\nResponses below a confidence threshold, flagged by **guardrails** (blank or off topic) or by\n**language detection** (another language) pause for **human in the loop** confirmation, where a\ntrained rater approves or rejects the engine score. A human rescore is recorded in the\nprogramme's own scoring system, which a **custom function** can write to.\n\n**Test suites** with deterministic and LLM grading run the rubric prompt against a set of human\nscored responses and report a pass rate and per case verdicts before use; agreement statistics\nsuch as exact and adjacent agreement are calculated by the programme. **Prompt versioning** keeps\na history of prompt versions, and the **run history** and **audit trail** per run support\nrescoring and appeals. The platform is model agnostic and offers EU and UAE data residency.\nValidation against human agreement and fairness checks remain the programme's responsibility.",[177,180,183],{"question":178,"answer":179},"Is AI already used to score state tests?","Yes. Massachusetts uses AI to score MCAS essays, trained on human scored examples of each score point, with humans giving 10 percent of AI scored essays a second read. Texas scores STAAR written responses with an automated scoring engine first and routes at least 25 percent, plus low confidence and unusual ones, to human raters; TEA says its engine is not AI in the sense of a system that teaches itself, but is programmed on about 3,000 human scored responses per question.",{"question":181,"answer":182},"How accurate is AI essay scoring?","For suitable tasks, engines can agree with human raters as closely as two humans agree with each other. Texas requires this before an engine is used, and ETS cites a 2010 study in which its engine's agreement with a human rater on the TOEFL Independent and GRE Issue tasks was higher than between two human raters. Errors still happen: in 2025 about 1,400 Massachusetts essays did not receive the correct scores and were rescored.",{"question":184,"answer":185},"Is AI essay scoring high risk under the EU AI Act?","Yes. Annex III point 3(b) lists AI systems intended to evaluate learning outcomes as high risk, which brings the requirements of Chapter III, Section 2 (Articles 8 to 15) on risk management, data governance, logging and human oversight. Schools and exam bodies that use such a system also have the deployer obligations of Article 26.",[],"2026-09-27",[189],{"date":187,"note":190},"First published","automated-scoring-of-written-responses",[193,218,243],{"title":194,"useCases":195,"organization":196,"vendors":199,"summary":200,"stage":201,"year":202,"channels":203,"languages":204,"metrics":206,"outcomeDisclosed":197,"sources":207,"verification":213,"grade":215,"id":216,"organizationSlug":217},"Texas Education Agency: hybrid automated scoring of STAAR written responses",[191],{"name":154,"anonymized":197,"country":198,"region":155,"industry":18},false,"US",[],"The Texas Education Agency scores the short and extended constructed responses on the English language STAAR tests with a hybrid model: an automated scoring engine gives every response its first score, and at least 25 percent of responses per grade and subject are routed to trained human raters to monitor the engine. Responses with condition codes (for example blank, off topic, another language or vocabulary unlike the training data) or low confidence also go to humans, and a human score is always the score of record. The engine must agree with human raters as often as humans agree with each other before use; STAAR Spanish responses are scored only by humans. TEA says it used the hybrid approach to score all constructed responses in the December 2023 administration, after a study that rescored spring 2023 responses with the engine. TEA calls it an automated scoring engine and says it differs from AI that teaches itself: it is programmed on about 3,000 human scored field test responses per item.","scaled",2023,[25],[205],"en",[],[208,210],{"url":156,"title":153,"publisher":209},"Texas Education Agency, Student Assessment Division",{"url":211,"title":212,"publisher":154,"archivedUrl":35},"https://tea.texas.gov/student-assessment/testing/hybrid-scoring-key-questions.pdf","Hybrid Scoring Key Questions",{"level":214,"checkedAt":187},"source-verified","B","texas-education-agency-staar-automated-scoring",null,{"title":219,"useCases":220,"organization":221,"vendors":223,"summary":226,"stage":201,"year":227,"channels":228,"languages":229,"metrics":230,"outcomeDisclosed":197,"sources":231,"verification":241,"grade":215,"id":242,"organizationSlug":217},"ETS: automated essay scoring engine in GRE Analytical Writing",[191],{"name":222,"anonymized":197,"country":198,"region":155,"industry":17},"ETS",[224],{"name":222,"role":225},"in-house","ETS evaluates GRE Analytical Writing essays on a six point holistic scale, which includes a score from its own automated scoring engine, whose features ETS says are the result of nearly two decades of natural language processing research. All essays are also reviewed by trained analysts with essay similarity detection software and by experienced content experts. ETS cites a 2010 study (Attali, Bridgeman and Trapani) in which the engine agreed with a human rater on the GRE Issue and TOEFL Independent tasks more closely than two independent human raters agreed with each other. An ETS research report records that the engine was first implemented as a check score for the revised GRE General Test in August 2012.",2012,[25],[205],[],[232,235,238],{"url":233,"title":234,"publisher":222},"https://www.ets.org/gre/test-takers/general-test/scores/understand-scores.html","Understanding GRE General Test Scores",{"url":236,"title":237,"publisher":222},"https://www.ets.org/erater/how.html","How the e-rater Scoring Engine Works",{"url":239,"title":240,"publisher":222},"https://files.eric.ed.gov/fulltext/EJ1109327.pdf","A Study of the Use of the e-rater Scoring Engine for the Analytical Writing Measure of the GRE revised General Test (ETS RR-14-24)",{"level":214,"checkedAt":187},"ets-gre-automated-essay-scoring",{"title":244,"useCases":245,"organization":246,"vendors":248,"summary":249,"stage":201,"year":36,"channels":250,"languages":251,"metrics":252,"outcomeDisclosed":253,"sources":254,"verification":259,"grade":260,"id":261,"organizationSlug":217},"Massachusetts DESE: AI scoring of MCAS essays and the 2025 rescoring",[191],{"name":247,"anonymized":197,"country":198,"region":155,"industry":18},"Massachusetts Department of Elementary and Secondary Education",[],"Massachusetts scores MCAS essays with AI trained on human scored examples of each score point, with humans giving 10 percent of AI scored essays a second read. The problem surfaced in summer 2025, when preliminary results went to districts. In Lowell, the example NBC10 Boston reports, a teacher found that some of her third grade students' scores did not add up and the issue went to district leaders; district leaders notified DESE. The state's testing contractor, Cognia, found that roughly 1,400 essays (of about 750,000 MCAS essays statewide) had not received the correct scores, which DESE attributed to a temporary technical issue in the process; the essays were rescored, 145 districts were notified and district data was corrected in August. DESE points to the discrepancy period in which districts can report issues with preliminary results as a check on accuracy.",[25],[205],[],true,[255],{"url":172,"title":256,"publisher":257,"date":258},"'No rhyme or reason': AI grading issue affects hundreds of MCAS essays","NBC Boston","2025-09-11",{"level":214,"checkedAt":187},"C","massachusetts-dese-mcas-ai-essay-scoring",0,[],{"low":265,"high":266},360000,1275000,[268,288,306,328],{"slug":269,"title":270,"shortTitle":271,"definition":272,"status":9,"industries":273,"functions":276,"patterns":279,"audience":280,"autonomy":28,"adoptionStage":29,"segment":281,"evidenceCount":282,"publicEvidenceCount":282,"organizations":283,"bestGrade":215,"headline":217,"lastVerified":287,"indexable":253},"property-valuation-support","AI support for property valuation and appraisal","Property valuation support","AI, most often an automated valuation model, that estimates a property's market value from comparable sales, property characteristics and location data, and either offers to replace a full appraisal within set limits or gives a professional valuer a first pass estimate, the closest comparable sales and a reliability score, so the valuer's time goes to the properties that need a person's judgment.",[274,275,18],"real-estate","banking",[277,278,20],"lending-and-credit","case-management",[23,22],"employee-facing","lending",3,[284,285,286],"Fannie Mae","Riverside County Assessor-County Clerk-Recorder","Valuation Office Agency","2026-09-28",{"slug":289,"title":290,"shortTitle":291,"definition":292,"status":9,"industries":293,"functions":295,"patterns":297,"audience":280,"autonomy":28,"adoptionStage":300,"segment":301,"evidenceCount":282,"publicEvidenceCount":302,"organizations":303,"bestGrade":260,"headline":217,"lastVerified":187,"indexable":253},"fraud-alert-triage","AI agent for fraud alert triage","Fraud alert triage","An AI agent that works the fraud alert queue behind the scenes as the analyst's first pass, without contacting the customer: it enriches each alert with customer, device and payment context, closes clear false positives under documented rules, merges duplicates, and routes genuine risk to an analyst with a drafted rationale.",[275,294],"payments",[296,20],"fraud-prevention",[298,22,299,23],"agentic-workflow","summarization","early-adopters","middle-office",2,[304,305],"Coast","SEB",{"slug":307,"title":308,"shortTitle":309,"definition":310,"status":9,"industries":311,"functions":314,"patterns":316,"audience":319,"autonomy":28,"adoptionStage":300,"evidenceCount":320,"publicEvidenceCount":321,"organizations":322,"bestGrade":215,"headline":217,"lastVerified":327,"indexable":253},"outbound-reminder-and-confirmation-agent","AI agent for outbound reminders and confirmations by voice and messaging","Outbound reminders and confirmations","An AI agent that contacts customers about something they already booked or ordered (an appointment, a delivery, a reservation or a service visit) to remind them, confirm attendance and let them cancel or move it in the same conversation, by phone, SMS, WhatsApp or email. It is operational service outreach, not marketing: nothing is sold, and success is measured in kept appointments and reused slots, not in conversion.",[312,313,18],"cross-industry","healthcare",[315,20],"customer-service",[317,318,298,23],"voice-agent","conversational-agent","customer-facing",5,4,[323,324,325,326],"Sheffield Children's NHS Foundation Trust","University Hospitals Coventry and Warwickshire NHS Trust","U.S. Department of Veterans Affairs","WellSpan Health","2026-09-26",{"slug":329,"title":330,"shortTitle":331,"definition":332,"status":9,"industries":333,"functions":334,"patterns":335,"audience":319,"autonomy":28,"adoptionStage":300,"evidenceCount":282,"publicEvidenceCount":282,"organizations":337,"bestGrade":215,"headline":217,"lastVerified":187,"indexable":253},"student-enrollment-and-services-assistant","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],[315,20],[318,336,22],"rag-knowledge-assistant",[338,339,340],"Adelphi University","Austin Peay State University","Georgia State University",{"indexable":253,"reasons":342},[],[344,349,354,361,367,373,379,386,394,401,408,414,421,428,434,439,446,452,458,464,470,476,482,487,492,499,506,511,517,524,530,536,542,547],{"id":140,"label":345,"issuer":148,"region":149,"url":346,"description":347,"useCases":348,"indexable":253},"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":141,"label":350,"issuer":148,"region":149,"url":351,"description":352,"useCases":353,"indexable":253},"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":144,"label":355,"issuer":356,"region":357,"url":358,"description":359,"useCases":360,"indexable":253},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":143,"label":362,"issuer":363,"region":155,"url":364,"description":365,"useCases":366,"indexable":253},"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":368,"label":369,"issuer":148,"region":149,"url":370,"description":371,"useCases":372,"indexable":253},"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":142,"label":374,"issuer":375,"region":149,"url":376,"description":377,"useCases":378,"indexable":253},"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":380,"label":381,"issuer":382,"region":149,"url":383,"description":384,"useCases":385,"indexable":253},"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":387,"label":388,"issuer":389,"region":390,"url":391,"description":392,"useCases":393,"indexable":253},"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":395,"label":396,"issuer":397,"region":390,"url":398,"description":399,"useCases":400,"indexable":253},"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":402,"label":403,"issuer":404,"region":357,"url":405,"description":406,"useCases":407,"indexable":253},"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":409,"label":410,"issuer":411,"region":155,"url":412,"description":413,"useCases":407,"indexable":253},"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":415,"label":416,"issuer":417,"region":149,"url":418,"description":419,"useCases":420,"indexable":253},"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":422,"label":423,"issuer":424,"region":357,"url":425,"description":426,"useCases":427,"indexable":253},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":429,"label":430,"issuer":148,"region":149,"url":431,"description":432,"useCases":433,"indexable":253},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":435,"label":436,"issuer":148,"region":149,"url":437,"description":438,"useCases":433,"indexable":253},"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":440,"label":441,"issuer":442,"region":155,"url":443,"description":444,"useCases":445,"indexable":253},"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":447,"label":448,"issuer":148,"region":149,"url":449,"description":450,"useCases":451,"indexable":253},"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":453,"label":454,"issuer":455,"region":155,"url":456,"description":457,"useCases":451,"indexable":253},"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":459,"label":460,"issuer":461,"region":357,"url":462,"description":463,"useCases":451,"indexable":253},"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":465,"label":466,"issuer":148,"region":149,"url":467,"description":468,"useCases":469,"indexable":253},"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":471,"label":472,"issuer":473,"region":155,"url":474,"description":475,"useCases":469,"indexable":253},"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":477,"label":478,"issuer":389,"region":390,"url":479,"description":480,"useCases":481,"indexable":253},"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":483,"label":484,"issuer":148,"region":149,"url":485,"description":486,"useCases":481,"indexable":253},"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":488,"label":489,"issuer":148,"region":149,"url":490,"description":491,"useCases":481,"indexable":253},"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":493,"label":494,"issuer":495,"region":149,"url":496,"description":497,"useCases":498,"indexable":253},"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":500,"label":501,"issuer":502,"region":155,"url":503,"description":504,"useCases":505,"indexable":253},"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":507,"label":508,"issuer":148,"region":149,"url":509,"description":510,"useCases":505,"indexable":253},"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":512,"label":513,"issuer":148,"region":149,"url":514,"description":515,"useCases":516,"indexable":253},"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":518,"label":519,"issuer":520,"region":521,"url":522,"description":523,"useCases":320,"indexable":253},"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":525,"label":526,"issuer":527,"region":149,"url":528,"description":529,"useCases":321,"indexable":253},"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":531,"label":532,"issuer":533,"region":149,"url":534,"description":535,"useCases":321,"indexable":253},"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":537,"label":538,"issuer":539,"region":390,"url":540,"description":541,"useCases":282,"indexable":253},"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":543,"label":544,"issuer":148,"region":149,"url":545,"description":546,"useCases":282,"indexable":253},"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":548,"label":549,"issuer":550,"region":155,"url":551,"description":552,"useCases":282,"indexable":253},"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.",1790598305995]