[{"data":1,"prerenderedAt":580},["ShallowReactive",2],{"uc-fx-exposure-forecasting-and-hedging-support":3,"uc-regulations":357},{"useCase":4,"evidence":174,"blitsAiDeployments":245,"benchmarks":246,"indicative":253,"related":256,"indexability":355,"includeUnpublished":180},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":25,"channels":27,"audience":29,"autonomy":30,"adoptionStage":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":38,"indicativeValue":42,"macroEstimates":84,"feasibility":85,"implementation":96,"risk":134,"blitsAi":155,"faq":157,"related":167,"datePublished":169,"dateModified":169,"lastVerified":169,"changelog":170,"slug":173},"AI for FX exposure forecasting and hedging support","FX exposure and hedging","FX exposure forecasting with AI for hedging","Machine learning forecasts of currency exposure feed a hedging program. ASML reports forecast accuracy up from 70% to 96% for its USD material intake.","published","AI for FX exposure forecasting is a machine learning model that predicts a company's future foreign currency cash flows, so treasury can set hedges against a more accurate exposure number. Treasury still decides the hedge ratios, instruments and timing, and executes the trades.",[12,13,14,15,16],"FX exposure forecasting","AI FX hedging","currency exposure forecasting with machine learning","hedge forecast automation","FX hedging support",[18,19,20],"cross-industry","manufacturing","technology",[22,23,24],"treasury","risk-management","finance-and-accounting",[26],"prediction-and-scoring",[28],"internal-tools","back-office","assist","early-adopters","A hedging program is only as good as the exposure it hedges. When the forecast of future foreign\ncurrency flows is too low or too high, the company hedges the wrong amount and the gap shows up\nin the P&L as currency gains and losses it did not intend to carry.\n\nThe published cases describe the same starting point. ASML sells its systems in euros but pays\nsome suppliers in US dollars, so its purchase hedging program rests on a forecast of expected\nUSD material intake. According to the AFP case study, that forecast was only 70% accurate and\ncame from a manual, Excel heavy process that was labor intensive and prone to error. Microsoft\nTreasury, which hedges with FX forwards and options, used forecasts built from Excel spreadsheets\nfed by many groups across the company. Its Adam Smith Awards entry lists 80% of analysts' time\nspent on collecting and compiling data, and says the uncertainty in the forecasts led to variance\nbetween actual and forecast exposure and so to P&L volatility.\n\nASML says its model recognizes patterns and trends in historic actuals and uses them to predict\nfuture intake. The opportunity is to produce the exposure forecast automatically and more\naccurately, while hedge policy and execution stay with the treasury team. The ASML case dates from\n2024; the Microsoft case is older, with sources from 2017 to 2019.",[],"1. **Collect the actuals.** Treasury and data science gather the history of the flows to be\n   hedged, by currency, entity and period. ASML used five years of actual USD intake.\n2. **Choose the model on held out data.** ASML trained twenty open source Python algorithms on\n   the first three years and tested them on the last two, kept the four best, retested them and\n   fine tuned the winner. Microsoft built its accounts receivable forecast in Azure Machine\n   Learning, which Treasury Today says is accessible by web for any user with access.\n3. **Produce the exposure forecast without manual inputs.** The model reads patterns and trends in\n   the actuals and predicts future intake or collections per currency. EuroFinance's write up of\n   Microsoft's award entry says its currency teams use machine learning that supplies forecasts in\n   less than 15 seconds, where they used to be sourced from subsidiaries.\n4. **Hedge against the forecast.** Treasury applies its hedge policy to the forecast, for example\n   layered forward cover, and places the trades with its banks or trading platform. The model does\n   not decide ratios or execute.\n5. **Compare with actuals every period.** Each month the realised flows are added, forecast\n   error is reviewed per currency, and the model is retrained. ASML says its model improves with each month.",[36,37],"risk-reduction","employee-productivity",[39,40,41],"forecast-accuracy","productivity-gain","cost-savings",{"referenceOrg":43,"inputs":44,"formula":79,"currency":80,"period":81,"resultLabel":82,"caveat":83},"A manufacturer that buys USD denominated components and hedges EUR\u002FUSD exposure",[45,52,59,65,72],{"key":46,"label":47,"low":48,"high":49,"unit":50,"note":51},"annualUsd","Annual USD denominated purchases hedged",300000000,600000000,"USD per year","Editorial assumption for the reference company, replace with your own hedged volume.",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"errorReduction","Reduction in forecast error, as a share of the hedged volume",0.05,0.2,"fraction","Editorial assumption, set well below ASML's reported move from 70% to 96% forecast accuracy, and not converted from that figure.",{"key":60,"label":61,"low":62,"high":55,"unit":63,"note":64},"mismatchCost","Average cost of an unintended open position, as a share of its value",0.02,"fraction of the mismatched amount","Editorial assumption about the adverse currency move on the mismatch before it is corrected, replace with your own volatility view.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"analystHours","Analyst hours per year saved on collecting and compiling exposure data",200,600,"hours per year","Editorial assumption, replace with your own time study.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"hourlyCost","Loaded cost of a treasury analyst hour",60,100,"USD per hour","Editorial assumption, replace with your own loaded cost.","annualUsd * errorReduction * mismatchCost + analystHours * hourlyCost","USD","per year","Expected cost of unintended currency exposure avoided, plus analyst time released","The first term is an expected value and a year with benign currency moves will show less. It leaves out hedging transaction costs, hedge accounting effort, the cost of building and running the model, and any change in the hedge ratio policy itself. The figures are not drawn from any one cited deployment.",[],{"complexity":86,"complexityNote":87,"dataPrerequisites":88,"integrations":92},"medium","The build needs several years of clean actuals per currency from the ERP, a data science environment and agreement from treasury on what exposure is hedged and how far ahead. ASML built its model on free open source algorithms with its own data science team. Flows driven by one off events, such as acquisitions or large contracts, sit outside what history can predict.",[89,90,91],"At least three years of actual flows in each hedged currency, by entity and month","A written hedge policy that defines the exposure, the horizon and the hedge ratios","Known one off items (acquisitions, contract wins, price changes) tagged so they do not distort the pattern",[93,94,95],"ERP purchase and sales order data, and bank statements for actuals","Treasury management system or hedge register for existing positions","Bank FX trading platform for execution, kept outside the model",{"steps":97,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":124},[98,101,104,107,110],{"title":99,"detail":100},"Measure the current forecast","Record how accurate the manual forecast has been per currency over the last quarters and how many analyst hours it takes. ASML started from a 70% accuracy baseline, which gave it a clear target.",{"title":102,"detail":103},"Test several algorithms on held out data","Train on the early years and test on the later ones, as ASML did with its twenty candidates. Compare the shortlisted models against the manual forecast on the same periods before choosing one.",{"title":105,"detail":106},"Run in parallel before relying on it","Produce the model forecast next to the manual one for several cycles, review the gaps with the business, and agree with stakeholders before the hedge program switches over.",{"title":108,"detail":109},"Wire the forecast into the hedge process","Feed the exposure number into the existing hedge workflow and keep trade approval and execution with treasury under the hedge policy.",{"title":111,"detail":112},"Retrain and review every period","Add realised flows each month, track error per currency and set a threshold that triggers a model review.",[114,115,116,117],"The model forecasts exposure; hedge ratios, instruments and trade execution stay with named treasury staff","Forecast error per currency is reported next to the forecast, and a breach of the agreed band pauses reliance on the model","Known one off events are entered by treasury as overrides, and each override is logged","The model and its training data sit in the company's model inventory with an owner","Treasury reviews each period's forecast against actuals, adds one off events, and decides hedge ratios, instruments and timing. The model supplies the exposure number and no trade is placed from it without a treasury approval under the company's hedge policy.",[120,121,122,123],"Forecast accuracy per currency and horizon, against the manual forecast it replaced","Unhedged or over hedged amount at period end, as a share of the realised exposure","Analyst hours per month spent on collecting and preparing exposure data","Currency impact on earnings versus budget",[125,128,131],{"title":126,"detail":127},"History that no longer applies","A change in suppliers, pricing or payment terms breaks the pattern the model learned, and the forecast looks precise while it drifts. Review error every month and retrain after structural changes.",{"title":129,"detail":130},"Accuracy measured on the wrong thing","Reporting average accuracy hides large misses in the months that matter for the hedge. Track error per currency and per hedge date, and compare with the manual baseline.",{"title":132,"detail":133},"Forecast treated as a hedge instruction","Staff hedge exactly to the number without a buffer for forecast error. Keep ratios and layers under the hedge policy, with cover below one hundred percent of the forecast.",{"euAiAct":135,"regulations":138,"guidance":142,"controls":149,"incidents":154},{"tier":136,"basis":137},"minimal","Forecasting a company's own foreign currency flows is not listed in Annex III and makes no decision about a natural person. The model is used by treasury staff and does not interact with the public, so the obligations are limited to AI literacy (Article 4).",[139,140,141],"eu-ai-act","nist-ai-rmf","iso-42001",[143],{"title":144,"issuer":145,"region":146,"url":147,"note":148},"NIST AI Risk Management Framework","NIST","north-america","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","A structure to map, measure and manage forecast accuracy, drift and data quality risks of an internal forecasting model.",[150,151,152,153],"Model inventory entry with an owner, the training period and validation results","Hedge policy that limits the share of the forecast that may be hedged and sets approval levels","Audit trail of forecasts, treasury overrides and approved trades","Monthly comparison of forecast and realised flows reviewed by treasury and risk",[],{"howToBuild":156},"The forecasting model itself belongs in the company's data science stack, as it does in the\nASML and Microsoft cases. Blits.ai adds the layer around it for the treasury team. An **AI\nagent** answers questions such as how the forecast compares with actuals for a currency, by\nquerying a **SQL knowledge base** over the forecast and actuals tables, and **custom functions**\ncall the forecasting service. Answers can reach treasurers through the **Microsoft Teams** or\n**API channel**.\n\nAn **agentic workflow** can compare actuals with the forecast each period and flag currencies\noutside the agreed error band, then prepare a hedge proposal for the treasurer, with **human in\nthe loop approval** before anything moves forward. **Guardrails** keep the agent to treasury\ntopics, run history gives an audit trail per run, and **test suites** check answers on known\nquestions after each change. Models are selectable per agent, and the platform runs in EU or UAE\nregions for data residency.",[158,161,164],{"question":159,"answer":160},"How much more accurate can an AI exposure forecast be?","ASML reports that its USD material intake forecast went from 70% to 96% accuracy after it replaced an Excel process with a machine learning model, and says USD exposures fell by 25 to 50 million dollars a month. That is one company's own figure from its award entry. EuroFinance reports that Microsoft cut the currency impact on its other income by 25% over 18 months, with no baseline given.",{"question":162,"answer":163},"Does the AI place the hedges?","In the cited cases it forecasts the exposure that treasury hedges against. The sources describe no model that chooses ratios or executes trades, and the hedge policy and trade approval stay with treasury.",{"question":165,"answer":166},"Do you need a vendor or a data science team?","ASML says it built its model with its own data science team on free open source Python algorithms. Microsoft built its forecast with its Cortana Intelligence team in Azure Machine Learning. Both cited cases are in house builds, so these sources show no vendor involved.",[168],"treasury-cash-flow-forecasting","2026-10-13",[171],{"date":169,"note":172},"First published","fx-exposure-forecasting-and-hedging-support",[175,218],{"title":176,"useCases":177,"organization":178,"vendors":184,"summary":187,"stage":188,"year":189,"channels":190,"languages":191,"metrics":193,"outcomeDisclosed":203,"sources":204,"verification":213,"grade":215,"id":216,"organizationSlug":217},"ASML: machine learning forecast of USD material intake for FX hedging",[173],{"name":179,"anonymized":180,"country":181,"region":182,"industry":19,"wikidata":183},"ASML",false,"NL","europe","Q297879",[185],{"name":179,"role":186},"in-house","ASML sells its chip making systems in euros but pays some suppliers in US dollars, so its purchase hedging program depends on a forecast of expected USD material intake. The treasury and data science teams replaced a manual, Excel based forecast with an automated machine learning model built on an open source Python algorithm, chosen after testing twenty candidates on five years of actuals. ASML reports forecast accuracy up from 70% to 96% and a monthly reduction in USD exposures.","production",2024,[28],[192],"en",[194],{"kpi":39,"value":195,"unit":196,"qualifier":197,"period":198,"baseline":199,"claimant":200,"quote":201,"sourceUrl":202},96,"percent","exact","forecast of USD denominated material intake","70% accuracy of the previous manual, Excel based forecast","organization","With the AI model, forecast accuracy went up from 70% to 96% and USD exposures were reduced by $25-50 million monthly, making the hedging program significantly more effective.","https:\u002F\u002Fwww.financialprofessionals.org\u002Ftraining-resources\u002Fresources\u002Farticles\u002FDetails\u002Fan-in-house-ai-solution-to-improve-fx-exposure-forecasting-accuracy",true,[205,209],{"url":202,"title":206,"publisher":207,"date":208},"An In-House AI Solution to Improve FX Exposure Forecasting Accuracy","Association for Financial Professionals (AFP)","2025-02-03",{"url":210,"title":211,"publisher":212},"https:\u002F\u002Ftreasurytoday.com\u002Fasa-2024-winners\u002Fasml-improves-forecasting-with-open-source-ai\u002F","ASML improves forecasting with open-source AI","Treasury Today (Adam Smith Awards 2024)",{"level":214,"checkedAt":169},"source-verified","C","asml-fx-exposure-forecasting",null,{"title":219,"useCases":220,"organization":221,"vendors":225,"summary":227,"stage":188,"year":228,"channels":229,"languages":230,"metrics":231,"outcomeDisclosed":203,"sources":232,"verification":242,"grade":215,"id":243,"organizationSlug":244},"Microsoft Treasury: machine learning forecasts for FX hedging",[173],{"name":222,"anonymized":180,"country":223,"region":146,"industry":20,"wikidata":224},"Microsoft","US","Q2283",[226],{"name":222,"role":186},"Microsoft Treasury manages currency exposure across operations in more than 150 countries and hedges forecast exposures with FX forwards and options. Its forecasts used to come from Excel spreadsheets fed by many groups, and forecast variance fed P&L volatility. Treasury and the Cortana Intelligence team built a machine learning forecast for accounts receivable in Azure Machine Learning, and by 2019 currency teams used machine learning forecasts in balance sheet hedging. Treasury Today reports improved accuracy; EuroFinance reports a 25% lower currency impact on other income over 18 months, with no baseline given.",2017,[28],[192],[],[233,237],{"url":234,"title":235,"publisher":236},"https:\u002F\u002Ftreasurytoday.com\u002Fasa-2017-winners\u002Fbest-cash-flow-forecasting-solution-highly-commended-microsoft\u002F","Best Cash Flow Forecasting Solution Highly Commended: Microsoft","Treasury Today (Adam Smith Awards 2017)",{"url":238,"title":239,"publisher":240,"date":241},"https:\u002F\u002Fwww.eurofinance.com\u002Fnews\u002Fharnessing-technology","EuroFinance Awards: Harnessing technology","EuroFinance","2019-10-08",{"level":214,"checkedAt":169},"microsoft-treasury-fx-forecasting","microsoft",0,[247],{"kpi":39,"label":248,"unit":196,"aggregate":203,"higherIsBetter":203,"n":249,"nUpTo":245,"median":195,"min":195,"max":195,"byClaimant":250,"vendorOnly":180,"points":251},"Forecast accuracy",1,{"organization":249,"vendor":245,"regulator":245,"independent":245},[252],{"evidenceId":216,"organization":179,"value":195,"qualifier":197,"claimant":200,"grade":215,"pooled":203},{"low":254,"high":255},312000,6060000,[257,290,311,336],{"slug":168,"title":258,"shortTitle":259,"definition":260,"status":9,"industries":261,"functions":265,"patterns":267,"audience":271,"autonomy":30,"adoptionStage":31,"segment":272,"evidenceCount":273,"publicEvidenceCount":273,"organizations":274,"bestGrade":282,"headline":283,"lastVerified":289,"indexable":203},"AI cash flow forecasting for corporate treasury","Treasury cash forecasting","AI cash flow forecasting for corporate treasury is machine learning that categorises a company's cash flows and forecasts its cash positions across accounts and currencies for the treasury team. Conversational analytics can sit on top of it. Some banks offer it inside their cash management platforms, where it also answers treasurers' questions in plain language, so the team decides on funding and idle balances with better information and less spreadsheet work.",[262,18,263,264,19],"banking","logistics-and-transportation","retail-and-ecommerce",[22,24,266],"analytics-and-reporting",[26,268,269,270],"classification-and-routing","conversational-agent","agentic-workflow","employee-facing","specialized-businesses",7,[275,276,277,278,279,280,281],"Amtrak","Ant International","Bank of America","Capital A Berhad (AirAsia Group)","Domino's Pizza","JPMorgan Chase","Prysmian","B",{"kpi":40,"label":284,"unit":196,"n":285,"nUpTo":249,"kind":286,"value":287,"qualifier":288,"claimant":200,"organization":280,"vendorReported":180},"Productivity gain",2,"reported",90,"approximately","2026-09-27",{"slug":291,"title":292,"shortTitle":293,"definition":294,"status":9,"industries":295,"functions":297,"patterns":300,"audience":29,"autonomy":304,"adoptionStage":305,"evidenceCount":306,"publicEvidenceCount":306,"organizations":307,"bestGrade":215,"headline":217,"lastVerified":169,"indexable":203},"contract-obligation-tracking-agent","AI agent for contract obligation tracking and compliance","Contract obligation tracking","Contract obligation tracking uses AI to extract commitments and deadlines from signed contracts, keep them in one register and alert the owner before a deadline is missed. The commitments include service levels, renewal and notice dates, price escalators, audit rights and delivery milestones.",[18,20,296,19],"professional-services",[298,299,24],"procurement","legal",[301,302,270,303],"document-processing","rag-knowledge-assistant","summarization","supervised-agent","emerging",3,[308,309,310],"ADARx Pharmaceuticals","Haynes International","OmniTRAX",{"slug":312,"title":313,"shortTitle":314,"definition":315,"status":9,"industries":316,"functions":321,"patterns":323,"audience":271,"autonomy":326,"adoptionStage":31,"evidenceCount":306,"publicEvidenceCount":306,"organizations":327,"bestGrade":215,"headline":331,"lastVerified":289,"indexable":203},"internal-audit-copilot","Generative AI copilot for internal audit","Internal audit copilot","An internal audit copilot is AI that helps internal auditors plan and write up audits, from drafting planning materials to summarising evidence and drafting findings. It drafts planning memos and document request lists from prior audits, summarises large evidence sets, builds risk and control matrices from policies and process documents, and drafts findings and reports, with every statement traceable to evidence and a qualified auditor accountable for every conclusion.",[18,262,317,318,319,320],"insurance","government","capital-markets","wealth-and-asset-management",[23,322,24],"regulatory-compliance",[302,303,324,301,325],"content-generation","anomaly-detection","copilot",[328,329,330],"Banco Bradesco","British Columbia Investment Management Corporation","XP Inc.",{"kpi":332,"label":333,"unit":196,"n":285,"nUpTo":245,"kind":286,"value":334,"qualifier":197,"claimant":335,"organization":328,"vendorReported":203},"handling-time-reduction","Handling time reduction",55,"vendor",{"slug":337,"title":338,"shortTitle":339,"definition":340,"status":9,"industries":341,"functions":342,"patterns":344,"audience":271,"autonomy":30,"adoptionStage":31,"evidenceCount":306,"publicEvidenceCount":306,"organizations":346,"bestGrade":215,"headline":350,"lastVerified":354,"indexable":203},"ai-candidate-sourcing-and-talent-rediscovery","AI agent for candidate sourcing and talent rediscovery","AI candidate sourcing and rediscovery","AI candidate sourcing and talent rediscovery is AI that builds and works the candidate pipeline before an application arrives, for recruiters filling open roles. It matches open roles against a company's own past applicants sitting unused in the applicant tracking system, ranks and surfaces the best fits for a recruiter to approach, and optimizes career site content and outreach to attract more of the right applicants, instead of a recruiter starting each search from an empty external search or a job board.",[18,19,20],[343],"human-resources",[345,26],"recommendation-and-personalization",[347,348,349],"AtkinsRéalis","Box","Forvia",{"kpi":351,"label":352,"unit":196,"n":285,"nUpTo":245,"kind":286,"value":353,"qualifier":197,"claimant":335,"organization":347,"vendorReported":203},"processing-time-reduction","Cycle time reduction",30,"2026-09-29",{"indexable":203,"reasons":356},[],[358,364,370,377,380,387,393,400,408,415,422,428,434,441,448,455,461,467,474,479,484,491,497,504,509,514,520,525,532,537,545,551,557,563,569,574],{"id":139,"label":359,"issuer":360,"region":182,"url":361,"description":362,"useCases":363,"indexable":203},"EU AI Act","European Union","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":365,"label":366,"issuer":360,"region":182,"url":367,"description":368,"useCases":369,"indexable":203},"gdpr","GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":141,"label":371,"issuer":372,"region":373,"url":374,"description":375,"useCases":376,"indexable":203},"ISO\u002FIEC 42001","ISO and IEC","global","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":140,"label":144,"issuer":145,"region":146,"url":147,"description":378,"useCases":379,"indexable":203},"Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":381,"label":382,"issuer":383,"region":182,"url":384,"description":385,"useCases":386,"indexable":203},"uk-gdpr","UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":388,"label":389,"issuer":360,"region":182,"url":390,"description":391,"useCases":392,"indexable":203},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":394,"label":395,"issuer":396,"region":182,"url":397,"description":398,"useCases":399,"indexable":203},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":401,"label":402,"issuer":403,"region":404,"url":405,"description":406,"useCases":407,"indexable":203},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":409,"label":410,"issuer":411,"region":404,"url":412,"description":413,"useCases":414,"indexable":203},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":416,"label":417,"issuer":418,"region":146,"url":419,"description":420,"useCases":421,"indexable":203},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":423,"label":424,"issuer":360,"region":182,"url":425,"description":426,"useCases":427,"indexable":203},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":429,"label":430,"issuer":431,"region":373,"url":432,"description":433,"useCases":427,"indexable":203},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":435,"label":436,"issuer":437,"region":182,"url":438,"description":439,"useCases":440,"indexable":203},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":442,"label":443,"issuer":444,"region":146,"url":445,"description":446,"useCases":447,"indexable":203},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":449,"label":450,"issuer":451,"region":373,"url":452,"description":453,"useCases":454,"indexable":203},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":456,"label":457,"issuer":360,"region":182,"url":458,"description":459,"useCases":460,"indexable":203},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":462,"label":463,"issuer":464,"region":146,"url":465,"description":466,"useCases":460,"indexable":203},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-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":468,"label":469,"issuer":470,"region":146,"url":471,"description":472,"useCases":473,"indexable":203},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":475,"label":476,"issuer":360,"region":182,"url":477,"description":478,"useCases":473,"indexable":203},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":480,"label":481,"issuer":360,"region":182,"url":482,"description":483,"useCases":473,"indexable":203},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":485,"label":486,"issuer":487,"region":146,"url":488,"description":489,"useCases":490,"indexable":203},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",12,{"id":492,"label":493,"issuer":494,"region":373,"url":495,"description":496,"useCases":490,"indexable":203},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":498,"label":499,"issuer":500,"region":182,"url":501,"description":502,"useCases":503,"indexable":203},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":505,"label":506,"issuer":360,"region":182,"url":507,"description":508,"useCases":503,"indexable":203},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":510,"label":511,"issuer":360,"region":182,"url":512,"description":513,"useCases":503,"indexable":203},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":515,"label":516,"issuer":403,"region":404,"url":517,"description":518,"useCases":519,"indexable":203},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":521,"label":522,"issuer":360,"region":182,"url":523,"description":524,"useCases":519,"indexable":203},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":526,"label":527,"issuer":528,"region":146,"url":529,"description":530,"useCases":531,"indexable":203},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",8,{"id":533,"label":534,"issuer":360,"region":182,"url":535,"description":536,"useCases":273,"indexable":203},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":538,"label":539,"issuer":540,"region":541,"url":542,"description":543,"useCases":544,"indexable":203},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":546,"label":547,"issuer":360,"region":182,"url":548,"description":549,"useCases":550,"indexable":203},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":552,"label":553,"issuer":554,"region":182,"url":555,"description":556,"useCases":550,"indexable":203},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":558,"label":559,"issuer":560,"region":182,"url":561,"description":562,"useCases":550,"indexable":203},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":564,"label":565,"issuer":566,"region":404,"url":567,"description":568,"useCases":306,"indexable":203},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":570,"label":571,"issuer":360,"region":182,"url":572,"description":573,"useCases":306,"indexable":203},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":575,"label":576,"issuer":577,"region":146,"url":578,"description":579,"useCases":306,"indexable":203},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904357756]