[{"data":1,"prerenderedAt":536},["ShallowReactive",2],{"uc-earnings-call-and-text-analysis-for-investment-signals":3,"uc-regulations":312},{"useCase":4,"evidence":165,"blitsAiDeployments":212,"benchmarks":213,"indicative":214,"related":217,"indexability":310,"includeUnpublished":171},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":19,"patterns":21,"channels":24,"audience":26,"autonomy":27,"adoptionStage":28,"segment":29,"problem":30,"problemStats":31,"howItWorks":32,"valueDrivers":33,"kpis":37,"indicativeValue":40,"macroEstimates":73,"feasibility":74,"implementation":86,"risk":124,"blitsAi":146,"faq":148,"related":158,"datePublished":160,"dateModified":160,"lastVerified":160,"changelog":161,"slug":164},"AI analysis of earnings calls and other text for investment signals","Earnings call signal analysis","AI earnings call analysis for investors","AI reads earnings call transcripts and filings to score companies. BlackRock fine tuned its own model; T. Rowe Price built an LLM quality framework.","published","AI, a large language model, either fine tuned or prompted within a structured scoring framework, that reads earnings call transcripts, filings and other unstructured company disclosures at scale to score a company on a dimension such as business quality or exposure to a market theme, or to forecast how markets will react to what was said, so a portfolio manager or analyst has a systematic, repeatable signal to weigh alongside traditional quantitative data.",[12,13,14,15],"earnings call transcript analysis","AI investment signal extraction","LLM equity research signals","textual alpha signals",[17,18],"wealth-and-asset-management","capital-markets",[20],"analytics-and-reporting",[22,23],"summarization","prediction-and-scoring",[25],"internal-tools","employee-facing","copilot","emerging","front-office","Every earnings season, thousands of companies hold calls and file disclosures within a few\nweeks of each other. An analyst covering even a modest sector cannot read every transcript\nclosely, let alone track how the same language choices played out across market cycles. Early\ntext based signals in investing counted positive and negative words in a document to build a\nsentiment score, a method that misses tone, hedging, and how a sentence's meaning depends on\nthe words around it.\n\nThe qualities that experienced fundamental analysts weigh, such as competitive position,\npricing power, and whether an executive's tone on a call has shifted, have historically depended\non individual judgment and were hard to measure systematically across a full coverage universe.\nA model that reads text the way a careful analyst does, at the scale of a whole index, gives\nportfolio managers a new, repeatable input, but only if its output is checked against experience\nrather than trusted on its own.",[],"1. **Ingest text at scale.** Earnings call transcripts, filings, and other public disclosures for\n   the coverage universe are collected as they are published, alongside historical market\n   reaction data for training and testing.\n2. **Score with a fine tuned model or a structured prompt framework, not a general chatbot.** A\n   large language model is either fine tuned on a narrow, specific task, such as predicting the\n   market's reaction to an earnings call, or run through a carefully engineered prompt and\n   scoring framework, such as scoring a defined dimension of business quality, rather than used\n   as an open ended assistant.\n3. **Run it systematically.** The model scores the full coverage universe on a set schedule\n   (for example weekly), producing a consistent, comparable output per company rather than a one\n   off read of a single transcript.\n4. **Check it against what is already known.** The new score is compared with existing\n   quantitative measures and with analysts' own view, so the team can see where the two agree\n   and, more importantly, where and why they diverge.\n5. **Keep a person in charge of the decision.** A portfolio manager or analyst defines the\n   question, reviews the model's reasoning and the passages behind it, corrects or overrides\n   results, and decides what, if anything, changes in the portfolio.",[34,35,36],"revenue-growth","speed","employee-productivity",[38,39],"productivity-gain","time-saved-per-task",{"referenceOrg":41,"inputs":42,"formula":68,"currency":69,"period":70,"resultLabel":71,"caveat":72},"An equity research team covering 500 companies",[43,49,56,63],{"key":44,"label":45,"low":46,"high":46,"unit":47,"note":48},"analysts","Analysts and associates on the team",20,"people","The reference team.",{"key":50,"label":51,"low":52,"high":53,"unit":54,"note":55},"transcriptsPerAnalystPerYear","Earnings call transcripts read per analyst per year",100,200,"transcripts per analyst per year","Editorial assumption for quarterly reporting across a broad coverage list, replace with your own coverage volume.",{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"hoursSavedPerTranscript","Hours of reading and note taking saved per transcript",0.25,0.5,"hours per transcript","Editorial assumption, replace with your own time study; the model still needs a human read of the passages it flags.",{"key":64,"label":65,"low":52,"high":53,"unit":66,"note":67},"hourlyCost","Fully loaded analyst cost per hour","USD per hour","Editorial assumption, replace with your own fully loaded cost.","analysts * transcriptsPerAnalystPerYear * hoursSavedPerTranscript * hourlyCost","USD","per year","Value of analyst time released from first pass transcript review","Values time saved on first pass reading only. It leaves out the value, positive or negative, of any investment decision the signal influences, the cost of building and running the models, and the analyst time spent reviewing the model's output instead.",[],{"complexity":75,"complexityNote":76,"dataPrerequisites":77,"integrations":81},"high","Reading text is the easy part; making the score trustworthy is not. BlackRock fine tuned a proprietary model on more than 400,000 earnings call transcripts before relying on it in production. T. Rowe Price instead built a prompt engineered scoring framework on top of an LLM and calls its results preliminary, still pending additional out of sample testing. A lighter version, using a vendor's ready made transcript summaries or sentiment scores, is medium complexity but gives up some of the precision of either approach.",[78,79,80],"A machine readable feed of earnings call transcripts and filings for the coverage universe","Historical market reaction or outcome data to fine tune, or to build and test a prompt based scoring framework, against","The team's existing quantitative factor or quality scores, to compare the new signal with",[82,83,84,85],"Transcript and filings data provider","Portfolio and research management system","Quantitative factor and risk model platform","Model validation and monitoring tooling",{"steps":87,"guardrails":103,"humanInTheLoop":108,"kpisToInstrument":109,"failureModes":114},[88,91,94,97,100],{"title":89,"detail":90},"Pick one narrow, well defined task first","Choose a single, specific question, such as forecasting the market reaction to an earnings call or scoring one dimension of quality, rather than building a general purpose research chatbot. A narrow task is easier to fine tune, test, and trust.",{"title":92,"detail":93},"Fine tune, or engineer a prompt framework, on your own outcome data","Either fine tune the model on historical transcripts paired with what actually happened afterwards, or build and iterate a structured prompt and scoring framework against the same data, so it learns the association for your universe rather than relying on a general purpose model's broad, unfocused training.",{"title":95,"detail":96},"Compare against the existing quantitative view","Run the new score alongside current factor or quality scores on the same universe and look closely at the cases where they disagree; that is usually where the new signal earns, or loses, its keep.",{"title":98,"detail":99},"Test out of sample before anyone relies on it","Hold back a period the model has not seen, walk the test forward in time, and check for look ahead bias and overfitting before the signal reaches a live portfolio process.",{"title":101,"detail":102},"Build the review step in from day one","Give the portfolio manager or analyst the passages behind every score, not just the number, and make it easy to challenge, correct, or override the model's read.",[104,105,106,107],"Every score is traceable to the transcript or filing passage that produced it","No trade or position change executes from a raw model score without a portfolio manager's decision","New signals are tested out of sample and checked for look ahead bias and overfitting before use","The model is fine tuned or run through a structured scoring framework on a narrow, specific task rather than used as an open ended assistant for investment advice","A portfolio manager or analyst defines the question the model answers, reviews the reasoning and source passages behind every score, and decides what, if anything, changes in the portfolio. The model flags evidence; it does not decide or execute a trade on its own.",[110,111,112,113],"Agreement and disagreement rate between the new score and the existing quantitative measure, reviewed by the team","Out of sample performance of the signal, retested on a rolling basis as new data arrives","Analyst hours spent on first pass transcript review, before and after","Rate and reasons for analyst overrides of the model's score",[115,118,121],{"title":116,"detail":117},"Look ahead bias","A model trained on data through the present can implicitly \"know\" what happened after the call it is scoring. Test strictly on a point in time history and walk forward only.",{"title":119,"detail":120},"Overfitting to the current market regime","A prompt or scoring framework tuned on recent data may not hold up once conditions change. Retest out of sample as new data arrives and watch for a drop in agreement with the quantitative baseline.",{"title":122,"detail":123},"Treating a text score as settled fact","A model's read of tone or quality can be wrong, or shaped by careful wording on the call. Keep the source passage next to every score and corroborate before sizing a position on it alone.",{"euAiAct":125,"regulations":128,"guidance":133,"controls":140,"incidents":145},{"tier":126,"basis":127},"minimal","An internal research tool that scores companies for a firm's own portfolio managers is not listed in Annex III and is not a practice prohibited by Article 5. It carries no Article 50 transparency duty: those disclosure obligations, including the Article 50(2) marking duty on providers of systems that generate text, apply to content or interactions shown to a customer or the public, and this tool's output never leaves the firm's own research process; the portfolio manager, not the model, remains accountable for any resulting investment decision.",[129,130,131,132],"eu-ai-act","us-sr-11-7","mas-ai-risk-management","iso-42001",[134],{"title":135,"issuer":136,"region":137,"url":138,"note":139},"SR 11-7, guidance on model risk management","Federal Reserve and OCC","north-america","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory expectations for validating, documenting, and monitoring quantitative models. Binding on Fed and OCC supervised banking organizations, including bank owned asset managers and broker dealers; other firms, such as the SEC registered advisers on this page, often use it as the reference standard for model validation but instead fall under Advisers Act compliance obligations.",[141,142,143,144],"Model inventory entry with an accountable owner in the investment or quant research team","Documented fine tuning data or prompt and scoring framework design, intended use, and known limitations for each model","Scheduled out of sample retesting, with a defined threshold for retiring or retraining a signal","Portfolio manager sign off recorded for any position materially influenced by a model score",[],{"howToBuild":147},"On Blits.ai this runs as an **agentic workflow** on a schedule: an agent reads new transcripts\nand filings from a **knowledge base** with hybrid retrieval, or from a **SQL knowledge base**\nwhere metadata is already structured, and uses **structured output** to return a fixed schema\n(the score, its passage citations, and a plain language rationale) per company, so results are\nconsistent and comparable across a run.\n\nWriting the score to the research or portfolio system waits for **human in the loop approval**,\nso a portfolio manager or analyst reviews the reasoning before anything downstream changes.\n**Test suites** replay historical transcripts with known outcomes before each release to catch\ndrift, **workflow run history** and its full audit trail give the team a record of every run to\nreview against the existing quantitative measures, and the platform is **model agnostic**, so\nthe team can switch the underlying model per task, or connect its own custom model through\n**Hugging Face** or **bring your own key**, and keep the data in the EU or UAE region.",[149,152,155],{"question":150,"answer":151},"Can an AI model really predict how a stock will react to an earnings call?","BlackRock reports using a fine tuned model for exactly this. It says the large language models it uses for security analysis are trained and fine tuned on narrow, curated datasets for specific investment tasks, such as forecasting the market reaction following a corporate earnings call, rather than being general purpose chatbots. Treat any such signal as one input that is tested out of sample, not a forecast to size a position on by itself.",{"question":153,"answer":154},"How is this different from an AI research summarization assistant?","A research summarization assistant condenses a firm's own published research and house view for advisors and analysts to use with clients. This use case instead extracts a new, systematic signal, such as a quality or resilience score, directly from company disclosures for a portfolio manager's own investment process, and it is judged by how well the signal performs out of sample, not by how faithfully it repeats a published view.",{"question":156,"answer":157},"What is the biggest risk in building this in house?","Look ahead bias and overfitting. T. Rowe Price's own published case study on a large language model quality framework names both risks explicitly and describes its results as preliminary, pending further out of sample testing, which is the right level of caution for a signal like this.",[159],"investment-research-summarization","2026-09-29",[162],{"date":160,"note":163},"First published","earnings-call-and-text-analysis-for-investment-signals",[166,193],{"title":167,"useCases":168,"organization":169,"vendors":173,"summary":176,"stage":177,"year":178,"channels":179,"languages":180,"metrics":182,"outcomeDisclosed":183,"sources":184,"verification":188,"grade":190,"id":191,"organizationSlug":192},"T. Rowe Price: large language model quality and resilience scoring framework",[164],{"name":170,"anonymized":171,"country":172,"region":137,"industry":17},"T. Rowe Price",false,"US",[174],{"name":170,"role":175},"in-house","T. Rowe Price's Integrated Equity team built a large language model framework, engineered through an iterated prompt and scoring process rather than a fine tuned model, that assesses qualitative business quality by combining the model with the firm's own proprietary research and public information, then scores every company in the small and mid cap Russell 2500 Index on a weekly batch run. The team compared the new score with its existing quantitative quality score on the same universe: the two agreed on most companies but diverged on others, and T. Rowe Price used those disagreements to study a recent rally in lower quality stocks. A second case study on the same page applies a similarly built LLM prompt framework to score software companies on their resilience to AI disruption. T. Rowe Price calls the first analysis preliminary and flags look ahead bias and overfitting as open risks; for both analyses together it says they remain in the early stages of development and require additional validation.","pilot",2026,[25],[181],"en",[],true,[185],{"url":186,"title":187,"publisher":170},"https://www.troweprice.com/en/us/investment-institute/insights/how-ai-can-open-new-avenues-for-systematic-investment-research","How AI can open new avenues for systematic investment research",{"level":189,"checkedAt":160},"source-verified","B","t-rowe-price-llm-quality-framework",null,{"title":194,"useCases":195,"organization":196,"vendors":198,"summary":200,"stage":201,"year":202,"channels":203,"languages":204,"metrics":205,"outcomeDisclosed":183,"sources":206,"verification":210,"grade":190,"id":211,"organizationSlug":192},"BlackRock: fine tuned large language models for earnings call market reaction and thematic baskets",[164],{"name":197,"anonymized":171,"country":172,"region":137,"industry":17},"BlackRock",[199],{"name":197,"role":175},"BlackRock Systematic says it has used AI and machine learning in its investment process for nearly two decades and now uses large language models fine tuned on narrow, specific investment tasks rather than general purpose chatbots. One model is trained on more than 400,000 earnings call transcripts covering over 17,000 public firms, combined with two decades of historical market data, to learn an association between what is said on a call and the market's subsequent reaction. A separate tool, the Thematic Robot, blends the same kind of text analysis of transcripts and other sources with proprietary data so a portfolio manager can build an equity basket around an emerging market theme, with the manager defining the theme, reviewing the model's reasoning, and correcting or overriding its output.","production",2024,[25],[181],[],[207],{"url":208,"title":209,"publisher":197},"https://www.blackrock.com/institutions/en-apac/insights/thought-leadership/ai-investing","How AI is Transforming Investing",{"level":189,"checkedAt":160},"blackrock-earnings-call-market-reaction-model",0,[],{"low":215,"high":216},50000,400000,[218,247,271,293],{"slug":159,"title":219,"shortTitle":220,"definition":221,"status":9,"industries":222,"functions":224,"patterns":227,"audience":26,"autonomy":27,"adoptionStage":231,"segment":29,"evidenceCount":232,"publicEvidenceCount":232,"organizations":233,"bestGrade":190,"headline":238,"lastVerified":246,"indexable":183},"AI summaries of investment research and the house view","Research summaries","An AI assistant that condenses long research reports, overnight market moves and the house view into short, sourced briefings for advisors and analysts, answers \"what is our view on X\" on demand, and adapts approved research for different client segments and languages, with every figure traced to the original research.",[17,18,223],"banking",[20,225,226],"sales","knowledge-management",[22,228,229,230],"rag-knowledge-assistant","content-generation","translation","early-adopters",4,[234,235,236,237],"Citi","Deutsche Bank","Morgan Stanley","UBS",{"kpi":39,"label":239,"unit":240,"n":212,"nUpTo":241,"kind":242,"value":243,"qualifier":244,"claimant":245,"organization":235,"vendorReported":171},"Time saved per task","minutes",1,"reported",120,"up-to","organization","2026-09-27",{"slug":248,"title":249,"shortTitle":250,"definition":251,"status":9,"industries":252,"functions":254,"patterns":257,"audience":26,"autonomy":27,"adoptionStage":231,"segment":29,"evidenceCount":260,"publicEvidenceCount":232,"organizations":261,"bestGrade":190,"headline":266,"lastVerified":246,"indexable":183},"deal-sourcing-and-due-diligence-assistant","AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","An AI assistant that screens the market for acquisition or investment targets, builds company profiles, and speeds up due diligence by reading data room documents, extracting key terms and risks and drafting the investment or diligence memo, for the deal team to verify and decide.",[18,17,253],"professional-services",[20,255,256],"legal","risk-management",[258,22,228,259,23],"document-processing","agentic-workflow",5,[262,263,264,265],"Datasite","EQT","Freshfields","Rogo",{"kpi":38,"label":267,"unit":268,"n":212,"nUpTo":241,"kind":242,"value":269,"qualifier":244,"claimant":270,"organization":262,"vendorReported":183},"Productivity gain","percent",80,"vendor",{"slug":272,"title":273,"shortTitle":274,"definition":275,"status":9,"industries":276,"functions":278,"patterns":280,"audience":26,"autonomy":27,"adoptionStage":231,"segment":282,"evidenceCount":260,"publicEvidenceCount":260,"organizations":283,"bestGrade":190,"headline":289,"lastVerified":292,"indexable":183},"insurance-pricing-and-actuarial-copilot","AI copilot for insurance pricing and actuarial analysis","Pricing and actuarial copilot","AI that speeds up the work of pricing and actuarial teams, from automated, transparent risk and demand model building to natural language analysis of rate filings, experience data and reserving diagnostics, while actuaries select the models, sign off the rates and own the professional judgment.",[277],"insurance",[279,256,20],"product-and-pricing",[23,281,259,22],"code-generation","pricing",[284,285,286,287,288],"Accelerant Holdings","Europ Assistance","Generali France","Kinsale Capital Group","MAIF",{"kpi":38,"label":267,"unit":290,"n":241,"nUpTo":212,"kind":242,"value":260,"qualifier":291,"claimant":245,"organization":286,"vendorReported":171},"multiplier","exact","2026-09-26",{"slug":294,"title":295,"shortTitle":296,"definition":297,"status":9,"industries":298,"functions":299,"patterns":302,"audience":303,"autonomy":27,"adoptionStage":231,"segment":304,"evidenceCount":305,"publicEvidenceCount":306,"organizations":307,"bestGrade":309,"headline":192,"lastVerified":246,"indexable":183},"portfolio-reporting-and-commentary","AI generated client portfolio reports and commentary","Portfolio commentary","AI that drafts each client's periodic portfolio commentary and report narrative (performance, attribution, what drove returns, positioning and outlook) in plain language and in the client's language, where every figure comes from the portfolio system of record and a reviewer approves the text before delivery.",[17,223],[20,300,301],"customer-service","operations",[229,22,230],"back-office","middle-office",3,2,[236,308],"Quilter","C",{"indexable":183,"reasons":311},[],[313,320,326,333,340,347,353,360,367,374,378,385,391,397,404,411,417,424,430,436,442,449,454,461,466,471,476,482,489,495,502,508,514,520,525,530],{"id":129,"label":314,"issuer":315,"region":316,"url":317,"description":318,"useCases":319,"indexable":183},"EU AI Act","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",230,{"id":321,"label":322,"issuer":315,"region":316,"url":323,"description":324,"useCases":325,"indexable":183},"gdpr","GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",207,{"id":132,"label":327,"issuer":328,"region":329,"url":330,"description":331,"useCases":332,"indexable":183},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":334,"label":335,"issuer":336,"region":137,"url":337,"description":338,"useCases":339,"indexable":183},"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":341,"label":342,"issuer":343,"region":316,"url":344,"description":345,"useCases":346,"indexable":183},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",71,{"id":348,"label":349,"issuer":315,"region":316,"url":350,"description":351,"useCases":352,"indexable":183},"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":316,"url":357,"description":358,"useCases":359,"indexable":183},"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":131,"label":361,"issuer":362,"region":363,"url":364,"description":365,"useCases":366,"indexable":183},"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":368,"label":369,"issuer":370,"region":363,"url":371,"description":372,"useCases":373,"indexable":183},"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":130,"label":375,"issuer":136,"region":137,"url":138,"description":376,"useCases":377,"indexable":183},"SR 11-7 model risk management","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",22,{"id":379,"label":380,"issuer":381,"region":329,"url":382,"description":383,"useCases":384,"indexable":183},"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":386,"label":387,"issuer":315,"region":316,"url":388,"description":389,"useCases":390,"indexable":183},"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":392,"label":393,"issuer":394,"region":316,"url":395,"description":396,"useCases":390,"indexable":183},"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":398,"label":399,"issuer":400,"region":137,"url":401,"description":402,"useCases":403,"indexable":183},"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":405,"label":406,"issuer":407,"region":329,"url":408,"description":409,"useCases":410,"indexable":183},"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":412,"label":413,"issuer":315,"region":316,"url":414,"description":415,"useCases":416,"indexable":183},"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":418,"label":419,"issuer":420,"region":137,"url":421,"description":422,"useCases":423,"indexable":183},"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":425,"label":426,"issuer":427,"region":137,"url":428,"description":429,"useCases":423,"indexable":183},"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":431,"label":432,"issuer":315,"region":316,"url":433,"description":434,"useCases":435,"indexable":183},"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":437,"label":438,"issuer":439,"region":329,"url":440,"description":441,"useCases":435,"indexable":183},"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":443,"label":444,"issuer":445,"region":137,"url":446,"description":447,"useCases":448,"indexable":183},"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":450,"label":451,"issuer":315,"region":316,"url":452,"description":453,"useCases":448,"indexable":183},"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":455,"label":456,"issuer":457,"region":316,"url":458,"description":459,"useCases":460,"indexable":183},"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":462,"label":463,"issuer":362,"region":363,"url":464,"description":465,"useCases":460,"indexable":183},"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":467,"label":468,"issuer":315,"region":316,"url":469,"description":470,"useCases":460,"indexable":183},"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":472,"label":473,"issuer":315,"region":316,"url":474,"description":475,"useCases":460,"indexable":183},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":477,"label":478,"issuer":315,"region":316,"url":479,"description":480,"useCases":481,"indexable":183},"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":483,"label":484,"issuer":485,"region":137,"url":486,"description":487,"useCases":488,"indexable":183},"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":490,"label":491,"issuer":315,"region":316,"url":492,"description":493,"useCases":494,"indexable":183},"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":496,"label":497,"issuer":498,"region":499,"url":500,"description":501,"useCases":260,"indexable":183},"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":503,"label":504,"issuer":505,"region":316,"url":506,"description":507,"useCases":232,"indexable":183},"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":509,"label":510,"issuer":511,"region":316,"url":512,"description":513,"useCases":232,"indexable":183},"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":515,"label":516,"issuer":517,"region":363,"url":518,"description":519,"useCases":305,"indexable":183},"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":521,"label":522,"issuer":315,"region":316,"url":523,"description":524,"useCases":305,"indexable":183},"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":526,"label":527,"issuer":315,"region":316,"url":528,"description":529,"useCases":305,"indexable":183},"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":531,"label":532,"issuer":533,"region":137,"url":534,"description":535,"useCases":305,"indexable":183},"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.",1790683488328]