[{"data":1,"prerenderedAt":668},["ShallowReactive",2],{"uc-treasury-cash-flow-forecasting":3,"uc-regulations":465},{"useCase":4,"evidence":206,"blitsAiDeployments":348,"benchmarks":349,"indicative":370,"related":373,"indexability":463,"includeUnpublished":212},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":21,"patterns":25,"channels":30,"audience":33,"autonomy":34,"adoptionStage":35,"segment":36,"problem":37,"problemStats":38,"howItWorks":39,"valueDrivers":40,"kpis":45,"indicativeValue":51,"macroEstimates":104,"feasibility":105,"implementation":116,"risk":154,"blitsAi":183,"faq":185,"related":195,"datePublished":201,"dateModified":201,"lastVerified":201,"changelog":202,"slug":205},"AI cash flow forecasting for corporate treasury","Treasury cash forecasting","AI tools sort a company's transactions and forecast its cash positions. J.P. Morgan reports Prysmian halved manual work and Domino's cut data cleanup by up to 90%.","published","Machine learning and conversational analytics, offered by some banks inside their cash management platforms, that categorise a company's cash flows, forecast positions across accounts and currencies, and answer treasurers' questions in plain language, so the treasury team decides on funding and idle balances with better information and less spreadsheet work.",[11,12,13,14],"AI cash forecasting","cash flow intelligence","treasury analytics assistant","liquidity forecasting",[16,17,18,19,20],"banking","cross-industry","logistics-and-transportation","retail-and-ecommerce","manufacturing",[22,23,24],"treasury","finance-and-accounting","analytics-and-reporting",[26,27,28,29],"prediction-and-scoring","classification-and-routing","conversational-agent","agentic-workflow",[31,32],"internal-tools","api","employee-facing","assist","early-adopters","specialized-businesses","Where cash forecasting runs on spreadsheets, someone pulls bank reports and extracts, maps\ntransactions to categories by hand and rolls the result forward, cycle after cycle. The published\ncases describe exactly this. J.P. Morgan's case study on Prysmian says its two person North\nAmerican treasury team relied on spreadsheets for cash forecasting and daily reconciliation, and\nits case study on Domino's describes manual data entry and categorisation that consumed valuable\ntime. The work is slow, depends on a few people and can produce forecasts too coarse to act on,\nso companies hold buffers of idle cash, as Amtrak did before improving its projections.\n\nMuch of the data to do better already sits in the bank's systems, because the company's payments\nand receipts pass through them. The opportunity is to categorise those flows automatically, learn their\npatterns, and give the treasurer a forecast and a way to question it, while the decision to fund,\nsweep or invest stays with the treasurer.",[],"1. **Connect the data.** Account and transaction data from the bank's platform, and optionally\n   other banks and the ERP, flow in daily.\n2. **Categorise flows.** Models sort every transaction into the company's own categories (payroll,\n   suppliers, card receipts, taxes, intercompany), and the treasury team corrects the ones that\n   are wrong.\n3. **Forecast.** Per category, account and currency, the tool projects positions over a chosen\n   horizon and shows the forecast against actuals as they arrive.\n4. **Ask in plain language.** A conversational layer turns questions such as \"show balances by\n   account for the last three months\" into queries and charts over the same data. This is the\n   least mature step: J.P. Morgan describes its treasury analytics assistant as a prototype, while\n   Bank of America offers CashPro Chat, a virtual service advisor, in its CashPro platform.\n5. **Recommend within limits.** J.P. Morgan describes, as a future direction, GenAI that gives\n   treasurers recommendations and might one day act on their behalf within parameters they set.\n   A design option that follows from this, not a feature any cited bank has shipped, is to run\n   scenarios (a delayed receipt, a currency move) and propose sweeps or investment of idle\n   balances for the treasurer to approve.",[41,42,43,44],"employee-productivity","speed","risk-reduction","revenue-growth",[46,47,48,49,50],"productivity-gain","cost-savings","hours-saved","users-served","forecast-accuracy",{"referenceOrg":52,"inputs":53,"formula":100,"currency":91,"period":101,"resultLabel":102,"caveat":103},"A mid sized company with a treasury team of five",[54,60,67,72,79,86,93],{"key":55,"label":56,"low":57,"high":57,"unit":58,"note":59},"teamSize","Treasury staff involved in forecasting",5,"people","The reference company.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"hoursPerWeek","Hours per person per week on data preparation and forecasting",6,12,"hours per person per week","Editorial assumption, replace with your own time study.",{"key":68,"label":69,"low":70,"high":70,"unit":68,"note":71},"weeks","Working weeks per year",46,"Editorial assumption.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"gain","Share of that time saved",0.25,0.4,"fraction of time","Editorial assumption, applied only to the data preparation and forecasting hours above. Set below the single cases on this page, which J.P. Morgan reports as half of one Prysmian team member's manual forecasting and reconciliation time and Domino's weekly manual data cleanup down by up to 90%.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"hourlyCost","Loaded cost of a treasury hour",60,100,"USD per hour","Editorial assumption, replace with your own loaded cost.",{"key":87,"label":88,"low":89,"high":90,"unit":91,"note":92},"releasedCash","Idle balance released for investment thanks to better forecasts",1000000,5000000,"USD","Editorial assumption. Amtrak describes investing balances it had set aside once forecasts improved; size this from your own buffers.",{"key":94,"label":95,"low":96,"high":97,"unit":98,"note":99},"netYield","Net yield on the released balance",0.02,0.04,"fraction per year","Editorial assumption, replace with your own short term investment yield.","teamSize * hoursPerWeek * weeks * gain * hourlyCost + releasedCash * netYield","per year","Treasury time released plus yield on released idle cash","Leaves out the cost of the tool, the value of avoided overdrafts or short term borrowing, and any fees the bank charges. The released cash figure depends heavily on how conservative the current buffers are.",[],{"complexity":106,"complexityNote":107,"dataPrerequisites":108,"integrations":112},"medium","The models are well understood; forecast quality depends on data coverage. Treasuries with many banks and entities need data from all of them, and categories must match how the company thinks about its cash.",[109,110,111],"Transaction history of at least a year across the main accounts","An agreed category scheme for inflows and outflows","Known large or irregular flows (tax dates, dividends, funding installments) entered as events",[113,114,115],"Bank cash management platform or multibank data aggregation","ERP and treasury management system for payables, receivables and plans","Market data for foreign exchange and investment rates",{"steps":117,"guardrails":133,"humanInTheLoop":138,"kpisToInstrument":139,"failureModes":144},[118,121,124,127,130],{"title":119,"detail":120},"Baseline the current forecast","Record how long the weekly forecast takes and how far it has been from actuals per category over the last quarters, so improvements can be measured.",{"title":122,"detail":123},"Agree categories with the treasury team","Define categories that match decisions (payroll, suppliers, receipts, taxes, intercompany), and separate large irregular flows so they do not distort the daily pattern, as Amtrak did.",{"title":125,"detail":126},"Run in parallel","Run the AI forecast next to the spreadsheet for several cycles and compare both with actuals before the team relies on it.",{"title":128,"detail":129},"Add the conversational layer on governed data","Let treasurers query the same governed data in plain language, with every answer showing the query and data it used.",{"title":131,"detail":132},"Keep actions behind approval","If the tool suggests sweeps or investments, route them as proposals with limits the treasurer sets, never as automatic instructions.",[134,135,136,137],"Forecasts are decision support; no payment, sweep or investment is executed without explicit approval","Forecast accuracy per category is shown next to the forecast, not hidden in a report","Conversational answers show the underlying query and data source","Drift monitoring on categorisation and forecast error, with an owner who acts on alerts","Treasurers review forecasts, correct categories and decide on funding, sweeps and investments. Any automated action runs only within limits the client has explicitly authorised, with a reversible audit trail. The bank's model owner monitors accuracy and drift across clients.",[140,141,142,143],"Hours per week spent on forecast preparation, before and after","Forecast error per category and horizon against actuals","Share of transactions categorised automatically without correction","Idle balances invested or buffers reduced as a result of better forecasts",[145,148,151],{"title":146,"detail":147},"Garbage categories","Early miscategorisation trains the model on wrong labels. Review categories closely in the first cycles and lock the scheme once stable.",{"title":149,"detail":150},"Blind spots from missing banks","Flows at other banks or in cash pools are missing and the forecast looks precise but is wrong. Show coverage and warn when material accounts are absent.",{"title":152,"detail":153},"Over trust in a single number","The team treats a point forecast as certain. Show ranges and scenario results, and keep liquidity buffers under human policy.",{"euAiAct":155,"regulations":158,"guidance":164,"controls":177,"incidents":182},{"tier":156,"basis":157},"limited","Forecasting a company's cash flows is not listed in Annex III and makes no decision about a natural person, so the forecasting model itself carries no obligations beyond AI literacy (Article 4). The conversational layer interacts directly with treasury staff, so under Article 50(1) they must be informed that they are dealing with an AI system unless that is obvious from the context. Without a conversational layer the use case is minimal risk.",[159,160,161,162,163],"eu-ai-act","dora","us-sr-11-7","mas-ai-risk-management","nist-ai-rmf",[165,171],{"title":166,"issuer":167,"region":168,"url":169,"note":170},"NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","A practical structure to map, measure and manage the accuracy and drift risks of a forecasting model offered to clients.",{"title":172,"issuer":173,"region":174,"url":175,"note":176},"MAS Guidelines for Artificial Intelligence (AI) Risk Management","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Consultation paper of November 2025 proposing supervisory expectations for AI inventories, risk materiality assessment, evaluation and testing, and monitoring at financial institutions.",[178,179,180,181],"Model inventory entry with an owner, validation results and drift monitoring","Documented limits for any automated sweep or investment, set by the client","Audit trail of forecasts, overrides and approved actions","Clear client terms that forecasts are informational and not advice",[],{"howToBuild":184},"On Blits.ai the forecasting model itself stays in the bank's analytics stack; Blits.ai adds the\nconversational and agentic layer around it. An **AI agent** answers treasurers' questions by\nquerying a **SQL knowledge base** over the governed transaction and forecast tables, and\n**custom functions** call the forecasting and scenario APIs. Answers can come back as charts and\ninsight panels in the web widget, or through the **API channel** inside the bank's portal.\n\nAn **agentic workflow** can check positions on a schedule and prepare a sweep or investment\nproposal, with **human in the loop approval** above a threshold the treasurer sets. **Guardrails**\nkeep the agent to treasury topics, **execution tracing** records every query behind an answer, and\n**test suites** check answers on known questions after each change. Models are selectable per\nagent, and the platform runs in EU or UAE regions for data residency.",[186,189,192],{"question":187,"answer":188},"How much manual work does AI cash forecasting remove?","The published client cases are case studies by the bank that sells the tool and should be read as such. J.P. Morgan reports that Prysmian halved the manual forecasting and reconciliation work of one treasury team member (about 10 hours a week) and saved an estimated USD 100,000 a year, and that Domino's cut weekly manual data cleanup by up to 90%. According to a trade press report of a Bloomberg interview, about 2,500 corporate clients used the tool a year after launch.",{"question":190,"answer":191},"Does the AI move money on its own?","It should not by default. Forecasts are decision support, and any sweep or investment is a proposal the treasurer approves, or runs within limits the client has explicitly authorised.",{"question":193,"answer":194},"Is this only for large corporates?","The published cases are all large companies (Prysmian, Domino's, Amtrak), so there is no public evidence yet for smaller firms. The teams can be small, though: J.P. Morgan's case study describes Prysmian's North American treasury as a team of two. For a smaller company, the deciding factors are whether its bank offers such a tool and how much of its cash flows through that bank.",[196,197,198,199,200],"corporate-client-servicing-assistant","ledger-and-payment-reconciliation","governed-text-to-sql-analytics","sme-cash-flow-underwriting","client-briefing-and-call-report-copilot","2026-09-27",[203],{"date":201,"note":204},"First published","treasury-cash-flow-forecasting",[207,234,273,297,316],{"title":208,"useCases":209,"organization":210,"vendors":214,"summary":217,"stage":218,"year":219,"channels":220,"languages":221,"metrics":223,"outcomeDisclosed":212,"sources":224,"verification":229,"grade":231,"id":232,"organizationSlug":233},"Bank of America: machine learning cash forecasting in CashPro",[205],{"name":211,"anonymized":212,"country":213,"region":168,"industry":16},"Bank of America",false,"US",[215],{"name":211,"role":216},"in-house","Bank of America launched a cash forecasting solution that uses machine learning in January 2022, offered to business clients in its CashPro platform as CashPro Forecasting. The bank reports fast adoption rather than accuracy: from the first half of 2022 to the first half of 2023, new client enrollments rose 141%, active users 105% and sign ins among those users 375%. The same release describes enhancements to CashPro Chat, a virtual service advisor in CashPro that now uses the same AI and machine learning capabilities as Erica, the bank's consumer assistant.","scaled",2022,[31],[222],"en",[],[225],{"url":226,"title":227,"publisher":211,"date":228},"https://newsroom.bankofamerica.com/content/newsroom/press-releases/2023/09/enhancements-to-bofa-s-cashpro--chat-create-greater-efficiencies.html","Enhancements to BofA's CashPro Chat Create Greater Efficiencies for Business Clients","2023-09-18",{"level":230,"checkedAt":201},"source-verified","B","bank-of-america-cashpro-forecasting","bank-of-america",{"title":235,"useCases":236,"organization":237,"vendors":240,"summary":244,"stage":245,"year":246,"channels":247,"languages":248,"metrics":249,"outcomeDisclosed":263,"sources":264,"verification":269,"grade":270,"id":271,"organizationSlug":272},"Prysmian: AI cash flow forecasting for North American treasury",[205],{"name":238,"anonymized":212,"country":239,"region":168,"industry":20},"Prysmian","IT",[241],{"name":242,"role":243},"JPMorgan Chase","platform","Prysmian, the cable manufacturer, used J.P. Morgan Payments Cash Flow Intelligence to automate cash visibility and forecasting for ten operating companies and thirteen bank accounts in North America, run by a treasury team of two. According to its treasurer, the forecast horizon grew from 30 to 91 days, manual reconciliation of more than 3,000 daily transactions was removed and answers to senior management questions came ten times faster.","production",2025,[31],[222],[250,258],{"kpi":47,"value":251,"unit":252,"currency":91,"qualifier":253,"period":254,"claimant":255,"quote":256,"sourceUrl":257},100000,"currency","approximately","per year, estimated labour cost","vendor","Contessa reports that, post-implementation, Prysmian maintains a \u003C1% error rate, saves $100,000 annually and reduced workload by 10 hours weekly","https://www.jpmorgan.com/insights/payments/data-intelligence/prysmian-ai-cash-flow-optimization",{"kpi":46,"value":259,"unit":260,"qualifier":253,"period":261,"claimant":255,"quote":262,"sourceUrl":257},50,"percent","one treasury team member's manual forecasting and reconciliation time, about 10 hours a week","Discover how Prysmian automated global cash flow forecasting, reduced manual work by 50% and saved $100K annually with J.P. Morgan Payments Cash Flow Intelligence.",true,[265],{"url":257,"title":266,"publisher":267,"date":268},"Prysmian's AI-Driven Cash Flow Optimization","J.P. Morgan","2025-06-23",{"level":230,"checkedAt":201},"C","prysmian-cash-flow-intelligence",null,{"title":274,"useCases":275,"organization":276,"vendors":278,"summary":280,"stage":245,"year":281,"channels":282,"languages":283,"metrics":284,"outcomeDisclosed":263,"sources":291,"verification":295,"grade":270,"id":296,"organizationSlug":272},"Domino's: AI cash flow categorisation and forecasting for treasury",[205],{"name":277,"anonymized":212,"country":213,"region":168,"industry":19},"Domino's Pizza",[279],{"name":242,"role":243},"Domino's treasury team adopted J.P. Morgan Payments Cash Flow Intelligence to aggregate, categorise and reconcile cash flows across a franchise model of more than 20,500 stores in 90 markets and a securitised debt structure. The team runs weekly cash reviews and forecast updates in the tool and reports forecasts that aligned closely with its 2024 budget.",2024,[31],[222],[285],{"kpi":46,"value":286,"unit":260,"qualifier":287,"period":288,"claimant":255,"quote":289,"sourceUrl":290},90,"up-to","weekly manual data cleanup","According to Domino’s Treasury Team Leader, Nancy Romain, the team’s weekly manual data cleanup efforts were reduced by up to 90%.","https://www.jpmorgan.com/insights/payments/data-intelligence/dominos-pizza-cash-flow-intelligence",[292],{"url":290,"title":293,"publisher":267,"date":294},"Domino's Unlocks Efficiency With Cash Flow Intelligence","2024-08-20",{"level":230,"checkedAt":201},"dominos-cash-flow-intelligence",{"title":298,"useCases":299,"organization":300,"vendors":302,"summary":304,"stage":245,"year":305,"channels":306,"languages":307,"metrics":308,"outcomeDisclosed":212,"sources":309,"verification":314,"grade":270,"id":315,"organizationSlug":272},"Amtrak: AI cash forecasting to invest idle balances",[205],{"name":301,"anonymized":212,"country":213,"region":168,"industry":18},"Amtrak",[303],{"name":242,"role":243},"Amtrak's treasury, which has to plan around the three large installments in which government funding arrives each year, went live with J.P. Morgan Payments Cash Flow Intelligence in May 2023 after testing it in beta. Separating daily card receipts, monthly partner receipts and infrequent federal receipts into their own categories improved projection accuracy, which let the team invest balances it had previously set aside as a buffer. No figures are published for the accuracy gain or the income.",2023,[31],[222],[],[310],{"url":311,"title":312,"publisher":267,"date":313},"https://www.jpmorgan.com/insights/payments/data-intelligence/amtrak-cash-flow-forecasting","Amtrak enhances cash forecasting using Cash Flow Intelligence","2025-05-15",{"level":230,"checkedAt":201},"amtrak-cash-flow-intelligence",{"title":317,"useCases":318,"organization":319,"vendors":320,"summary":322,"stage":218,"year":305,"channels":323,"languages":324,"metrics":325,"outcomeDisclosed":263,"sources":336,"verification":345,"grade":270,"id":346,"organizationSlug":347},"J.P. Morgan Payments: Cash Flow Intelligence for corporate treasurers",[205],{"name":242,"anonymized":212,"country":213,"region":168,"industry":16},[321],{"name":242,"role":216},"J.P. Morgan Payments offers Cash Flow Intelligence, a machine learning tool in its J.P. Morgan Access platform that categorises a corporate client's payment flows and produces cash forecasts. In a Bloomberg interview relayed by CTMfile, the bank's head of data and analytics for wholesale payments said that about a year after launch roughly 2,500 corporate clients used it free of charge, and Bloomberg reported that some had cut manual work in categorising and visualising payment flows by nearly 90%, while liquidity decisions stay with people. Separately, the bank built a prototype conversational analytics assistant that lets treasurers query their payments data in plain language.",[31],[222],[326,333],{"kpi":49,"value":327,"unit":328,"qualifier":253,"period":329,"claimant":330,"quote":331,"sourceUrl":332},2500,"count","corporate clients, about a year after launch","organization","He observed that since the AI tool was introduced about a year ago, approximately 2,500 JPMorgan corporate customers are currently using the product for free.","https://ctmfile.com/story/ai-driven-cashflow-tool-helps-corporate-clients-cut-manual-work-by-90",{"kpi":46,"value":286,"unit":260,"qualifier":253,"period":334,"claimant":330,"quote":335,"sourceUrl":332},"some corporate clients, manual work in categorising and visualising payment flows","Dubbed, Cash Flow Intelligence, the artificial intelligence (AI)-aided cashflow management tool launched by the largest US bank, JPMorgan Chase & Co. has helped some of its corporate clients vastly reduce their manual work by nearly 90%, as was reported last week by Bloomberg.",[337,341],{"url":332,"title":338,"publisher":339,"date":340},"AI-driven cashflow tool helps corporate clients cut manual work by 90%","CTMfile","2024-03-13",{"url":342,"title":343,"publisher":267,"date":344},"https://www.jpmorgan.com/insights/payments/data-intelligence/genai-virtual-analytics-assistant-treasury","Conversational Analytics, Virtual Assistants & GenAI in Corporate Treasury","2023-12-20",{"level":230,"checkedAt":201},"jpmorgan-cash-flow-intelligence","jpmorgan-chase",0,[350,360,365],{"kpi":46,"label":351,"unit":260,"aggregate":263,"higherIsBetter":263,"n":352,"nUpTo":353,"median":354,"min":259,"max":286,"byClaimant":355,"vendorOnly":212,"points":356},"Productivity gain",2,1,70,{"organization":353,"vendor":353,"regulator":348,"independent":348},[357,358,359],{"evidenceId":296,"organization":277,"value":286,"qualifier":287,"claimant":255,"grade":270,"pooled":212},{"evidenceId":346,"organization":242,"value":286,"qualifier":253,"claimant":330,"grade":270,"pooled":263},{"evidenceId":271,"organization":238,"value":259,"qualifier":253,"claimant":255,"grade":270,"pooled":263},{"kpi":47,"label":361,"unit":252,"currency":91,"aggregate":212,"higherIsBetter":263,"n":353,"nUpTo":348,"median":251,"min":251,"max":251,"byClaimant":362,"vendorOnly":263,"points":363},"Cost savings",{"organization":348,"vendor":353,"regulator":348,"independent":348},[364],{"evidenceId":271,"organization":238,"value":251,"qualifier":253,"claimant":255,"grade":270,"pooled":263},{"kpi":49,"label":366,"unit":328,"aggregate":212,"higherIsBetter":263,"n":353,"nUpTo":348,"median":327,"min":327,"max":327,"byClaimant":367,"vendorOnly":212,"points":368},"Users served",{"organization":353,"vendor":348,"regulator":348,"independent":348},[369],{"evidenceId":346,"organization":242,"value":327,"qualifier":253,"claimant":330,"grade":270,"pooled":263},{"low":371,"high":372},40700,310400,[374,396,415,432,449],{"slug":196,"title":375,"shortTitle":376,"definition":377,"status":8,"industries":378,"functions":380,"patterns":383,"audience":386,"autonomy":387,"adoptionStage":35,"segment":36,"evidenceCount":57,"publicEvidenceCount":352,"organizations":388,"bestGrade":231,"headline":390,"lastVerified":201,"indexable":263},"AI assistant for corporate and commercial client servicing","Corporate client servicing","A conversational assistant inside the corporate banking portal, app and messaging channels that answers finance and treasury teams' servicing questions, such as payment status, balances, cut off times, fees and how to submit an instruction, resolves routine requests end to end and hands the rest to a service specialist who has an AI copilot.",[16,379],"payments",[381,382],"customer-service","operations",[28,384,29,385],"rag-knowledge-assistant","summarization","customer-facing","supervised-agent",[211,389],"DBS Bank",{"kpi":391,"label":392,"unit":260,"n":353,"nUpTo":348,"kind":393,"value":394,"qualifier":395,"claimant":330,"organization":211,"vendorReported":212},"contact-deflection","Contact deflection","reported",16,"exact",{"slug":197,"title":397,"shortTitle":398,"definition":399,"status":8,"industries":400,"functions":404,"patterns":405,"audience":408,"autonomy":387,"adoptionStage":35,"segment":408,"evidenceCount":409,"publicEvidenceCount":409,"organizations":410,"bestGrade":231,"headline":272,"lastVerified":201,"indexable":263},"AI for ledger and payment reconciliation","Ledger and payment reconciliation","AI that matches entries across nostro and vostro statements, card and scheme settlement files, the general ledger and suspense accounts, proposes matches and clearing journals, and routes only the genuine breaks to an operator with a plain language explanation.",[16,379,401,17,402,403],"capital-markets","wealth-and-asset-management","government",[23,382],[29,406,407],"anomaly-detection","document-processing","back-office",4,[411,412,413,414],"Comrade Trustee Services","Ginnie Mae","National Bank of Greece (Cyprus)","World Food Programme",{"slug":198,"title":416,"shortTitle":417,"definition":418,"status":8,"industries":419,"functions":423,"patterns":425,"audience":33,"autonomy":34,"adoptionStage":35,"evidenceCount":427,"publicEvidenceCount":427,"organizations":428,"bestGrade":231,"headline":272,"lastVerified":201,"indexable":263},"Governed text to SQL analytics assistant","Governed SQL analytics","An assistant that turns a business user's plain language question into a query against governed data, runs it under that user's own data permissions and returns the table or chart together with the SQL and the tables used, so routine ad hoc questions no longer queue for the data team.",[17,16,420,19,421,422],"insurance","technology","pharma-and-life-sciences",[24,424],"it-and-engineering",[28,426,384],"code-generation",3,[429,430,431],"Bayer","LinkedIn","Uber Technologies",{"slug":199,"title":433,"shortTitle":434,"definition":435,"status":8,"industries":436,"functions":437,"patterns":441,"audience":408,"autonomy":387,"adoptionStage":35,"segment":442,"evidenceCount":409,"publicEvidenceCount":409,"organizations":443,"bestGrade":231,"headline":272,"lastVerified":448,"indexable":263},"AI cash flow underwriting for small business loans","SME cash flow underwriting","An underwriting engine that assesses a small business's repayment capacity from live bank transactions, point of sale and payment flows, receivables and accounting data instead of audited accounts, and returns a decision recommendation with the evidence and reasons behind it.",[16],[438,439,440],"lending-and-credit","underwriting","risk-management",[26,407,29,28],"lending",[444,445,446,447],"MYbank","National Australia Bank","OakNorth Bank","Sumitomo Mitsui Banking Corporation","2026-09-26",{"slug":200,"title":450,"shortTitle":451,"definition":452,"status":8,"industries":453,"functions":454,"patterns":457,"audience":33,"autonomy":459,"adoptionStage":35,"segment":36,"evidenceCount":427,"publicEvidenceCount":427,"organizations":460,"bestGrade":231,"headline":272,"lastVerified":201,"indexable":263},"AI copilot for corporate client briefings and call reports","Client briefing and call reports","An AI copilot for relationship managers, mainly in corporate and commercial banking, whose main job is preparation: before a client meeting it assembles a briefing pack from filings, news, internal notes, product holdings and upcoming maturities, and afterwards it turns the banker's notes into a structured call report and CRM update. Unlike a meeting notetaker, which centres on capturing the conversation, it centres on the credit and cross sell context around the meeting; wealth advisor tools that also prepare meetings overlap with it. The banker reviews every output.",[16,402,401],[455,456],"sales","knowledge-management",[384,385,458,29],"content-generation","copilot",[211,461,462],"Scotiabank","Standard Chartered",{"indexable":263,"reasons":464},[],[466,473,479,487,490,495,502,509,513,520,527,532,538,545,551,556,563,568,574,580,586,592,598,603,608,615,622,627,632,639,645,651,657,662],{"id":159,"label":467,"issuer":468,"region":469,"url":470,"description":471,"useCases":472,"indexable":263},"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.",197,{"id":474,"label":475,"issuer":468,"region":469,"url":476,"description":477,"useCases":478,"indexable":263},"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.",180,{"id":480,"label":481,"issuer":482,"region":483,"url":484,"description":485,"useCases":486,"indexable":263},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":163,"label":166,"issuer":167,"region":168,"url":169,"description":488,"useCases":489,"indexable":263},"Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":160,"label":491,"issuer":468,"region":469,"url":492,"description":493,"useCases":494,"indexable":263},"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":496,"label":497,"issuer":498,"region":469,"url":499,"description":500,"useCases":501,"indexable":263},"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.",64,{"id":503,"label":504,"issuer":505,"region":469,"url":506,"description":507,"useCases":508,"indexable":263},"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":162,"label":510,"issuer":173,"region":174,"url":175,"description":511,"useCases":512,"indexable":263},"MAS AI risk management guidelines","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":514,"label":515,"issuer":516,"region":174,"url":517,"description":518,"useCases":519,"indexable":263},"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":521,"label":522,"issuer":523,"region":483,"url":524,"description":525,"useCases":526,"indexable":263},"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":161,"label":528,"issuer":529,"region":168,"url":530,"description":531,"useCases":526,"indexable":263},"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":533,"label":534,"issuer":535,"region":469,"url":536,"description":537,"useCases":394,"indexable":263},"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":539,"label":540,"issuer":541,"region":483,"url":542,"description":543,"useCases":544,"indexable":263},"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":546,"label":547,"issuer":468,"region":469,"url":548,"description":549,"useCases":550,"indexable":263},"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":552,"label":553,"issuer":468,"region":469,"url":554,"description":555,"useCases":550,"indexable":263},"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":557,"label":558,"issuer":559,"region":168,"url":560,"description":561,"useCases":562,"indexable":263},"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":564,"label":565,"issuer":468,"region":469,"url":566,"description":567,"useCases":64,"indexable":263},"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.",{"id":569,"label":570,"issuer":571,"region":168,"url":572,"description":573,"useCases":64,"indexable":263},"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":575,"label":576,"issuer":577,"region":483,"url":578,"description":579,"useCases":64,"indexable":263},"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":581,"label":582,"issuer":468,"region":469,"url":583,"description":584,"useCases":585,"indexable":263},"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":587,"label":588,"issuer":589,"region":168,"url":590,"description":591,"useCases":585,"indexable":263},"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":593,"label":594,"issuer":173,"region":174,"url":595,"description":596,"useCases":597,"indexable":263},"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":599,"label":600,"issuer":468,"region":469,"url":601,"description":602,"useCases":597,"indexable":263},"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":604,"label":605,"issuer":468,"region":469,"url":606,"description":607,"useCases":597,"indexable":263},"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":609,"label":610,"issuer":611,"region":469,"url":612,"description":613,"useCases":614,"indexable":263},"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":616,"label":617,"issuer":618,"region":168,"url":619,"description":620,"useCases":621,"indexable":263},"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":623,"label":624,"issuer":468,"region":469,"url":625,"description":626,"useCases":621,"indexable":263},"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":628,"label":629,"issuer":468,"region":469,"url":630,"description":631,"useCases":63,"indexable":263},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":633,"label":634,"issuer":635,"region":636,"url":637,"description":638,"useCases":57,"indexable":263},"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":640,"label":641,"issuer":642,"region":469,"url":643,"description":644,"useCases":409,"indexable":263},"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":646,"label":647,"issuer":648,"region":469,"url":649,"description":650,"useCases":409,"indexable":263},"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":652,"label":653,"issuer":654,"region":174,"url":655,"description":656,"useCases":427,"indexable":263},"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":658,"label":659,"issuer":468,"region":469,"url":660,"description":661,"useCases":427,"indexable":263},"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":663,"label":664,"issuer":665,"region":168,"url":666,"description":667,"useCases":427,"indexable":263},"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.",1790598297574]