[{"data":1,"prerenderedAt":667},["ShallowReactive",2],{"uc-deal-sourcing-and-due-diligence-assistant":3,"uc-regulations":457},{"useCase":4,"evidence":215,"blitsAiDeployments":340,"benchmarks":341,"indicative":353,"related":356,"indexability":455,"includeUnpublished":221},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":21,"patterns":25,"channels":31,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"problem":38,"problemStats":39,"howItWorks":50,"valueDrivers":51,"kpis":55,"indicativeValue":61,"macroEstimates":95,"feasibility":100,"implementation":115,"risk":161,"blitsAi":189,"faq":191,"related":204,"datePublished":210,"dateModified":210,"lastVerified":210,"changelog":211,"slug":214},"AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","AI for deal sourcing and M&A due diligence","AI that screens targets, reads data rooms and drafts diligence memos for deal teams to verify, with evidence from EQT, Freshfields, Datasite and Rogo.","published","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.",[12,13,14,15,16],"deal sourcing AI","M&A due diligence AI","private equity target screening","data room document review assistant","investment memo drafting assistant",[18,19,20],"capital-markets","wealth-and-asset-management","professional-services",[22,23,24],"analytics-and-reporting","legal","risk-management",[26,27,28,29,30],"document-processing","summarization","rag-knowledge-assistant","agentic-workflow","prediction-and-scoring",[32,33],"internal-tools","api","employee-facing","copilot","early-adopters","front-office","Deal teams spend much of their time on work that comes before judgment. On the sourcing side,\nprivate equity firms, corporate development teams and bankers track large numbers of companies\nto find the few that fit a thesis, often with analysts who assemble lists and one pagers by\nhand. Testing whether one business fits the thesis can take an analyst 20 to 25 hours, according\nto a startup founder quoted in EQT's ThinQ publication.\nGood targets are missed because nobody noticed the signal in time, and the same research is\nredone for every new mandate because lessons from past deals sit in individual inboxes.\n\nOnce a deal is live, the data room opens and the clock starts. Associates, lawyers and advisers\nread contracts, financial statements, customer agreements and corporate records to find change\nof control clauses, unusual liabilities, customer concentration and missing documents, then\nwrite it up. Sellers must redact personal data before bidders see it. All of this is repetitive,\nhigh stakes and time boxed, and fatigue raises the risk that material issues slip through.",[40,45],{"statement":41,"sourceTitle":42,"sourceUrl":43,"year":44},"Datasite's chief product officer says sellers might have up to 100,000 documents associated with a deal.","Datasite automates M&A and speeds redaction by 80%, saving customers valuable time with Azure Cognitive Services","https://www.microsoft.com/en/customers/story/1379631359425784399-datasite-banking-capital-markets-azure-cognitive-services",2021,{"statement":46,"sourceTitle":47,"sourceUrl":48,"year":49},"Testing whether a business fits a private equity firm's thesis and has value creation potential can take an analyst 20 to 25 hours, according to Clarum cofounder Anton Otaner.","AI Promises to Make Private Equity Faster as Competition Heats Up","https://eqtgroup.com/thinq/technology/first-ai-native-private-equity-firm",2025,"1. **Screen the market against the thesis.** The assistant maps companies from licensed data,\n   filings, news and the firm's own CRM, finds similar companies and ranks them against the\n   investment thesis and signals such as growth, hiring or ownership changes, with the reasons.\n2. **Build the company profile.** For a shortlisted target it drafts a profile: business model,\n   financials, competitors, ownership, management and news, with a source for every figure, and\n   adds what the firm learned from comparable past deals.\n3. **Read the data room.** When diligence starts it classifies the documents, extracts key terms\n   (change of control, exclusivity, termination, liabilities, key customers and suppliers) into a\n   structured table and flags what is missing against the diligence request list.\n4. **Flag risks and redact.** It highlights clauses and figures that deviate from the norm, and on\n   the sell side finds personal and sensitive data for batch redaction before bidders get access.\n5. **Answer questions with citations.** Deal team members ask questions across the whole data room\n   and get answers that link to the exact page, so every statement can be checked.\n6. **Draft the memo.** It drafts the diligence findings and the investment committee memo from the\n   firm's template; the deal team verifies, completes and owns every conclusion.",[52,53,54],"speed","employee-productivity","risk-reduction",[56,57,58,59,60],"productivity-gain","time-saved-per-task","hours-saved","users-served","accuracy",{"referenceOrg":62,"inputs":63,"formula":90,"currency":91,"period":92,"resultLabel":93,"caveat":94},"A mid market private equity firm that takes 15 companies a year into full due diligence",[64,70,77,84],{"key":65,"label":66,"low":67,"high":67,"unit":68,"note":69},"deals","Companies taken into full due diligence per year",15,"deals per year","The reference firm.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"hoursPerDeal","Internal hours per deal on document review, extraction and memo drafting",300,600,"hours per deal","Editorial assumption for the deal team's own time, excluding external advisers. Replace with your own time records.",{"key":78,"label":79,"low":80,"high":81,"unit":82,"note":83},"shareSaved","Share of those hours the assistant saves",0.2,0.4,"fraction of hours","Conservative against the evidence on this page (Microsoft reports that Datasite's Redaction AI cuts redaction times by up to 80 percent), because redaction is the most mechanical step and review and judgment stay with the team.",{"key":85,"label":86,"low":87,"high":73,"unit":88,"note":89},"hourlyCost","Blended cost per deal team hour",150,"USD per hour","Editorial assumption for a blended associate and principal cost. Replace with your own.","deals * hoursPerDeal * shareSaved * hourlyCost","USD","per year","Deal team time released in due diligence","Internal time only. It leaves out savings on external advisers, the value of deals found earlier or not missed in sourcing, the effect of issues caught or missed on deal value, and the cost of the assistant and data licences.",[96],{"statement":97,"sourceTitle":98,"sourceUrl":99,"year":49},"Allvue research cited by EQT found that 82 percent of private equity and venture capital firms reported using AI in some capacity by the end of 2024, up from 47 percent a year earlier.","Why Private Capital Needs to Embrace Artificial Intelligence","https://eqtgroup.com/thinq/technology/why-private-capital-needs-to-embrace-artificial-intelligence",{"complexity":101,"complexityNote":102,"dataPrerequisites":103,"integrations":109},"high","Summarizing one document is easy. The hard parts are licensed market data and entity matching for sourcing, secure access to data rooms under strict confidentiality, reliable extraction from scanned and inconsistent documents, citations for every statement, and a review process that deal teams, lawyers and investment committees accept.",[104,105,106,107,108],"A written investment thesis and screening criteria per strategy or mandate","Licensed company, financial and transaction data, plus the firm's CRM and past deal records","A diligence request list and a key terms checklist per deal type","Memo and findings templates approved by the investment committee","Confidentiality and information barrier rules per deal",[110,111,112,113,114],"Market data and company databases (for example PitchBook, S&P Global, FactSet, Preqin)","CRM or deal pipeline system","Virtual data room and document management","Document storage such as SharePoint for memos and past deal files","Collaboration tools where the deal team works",{"steps":116,"guardrails":135,"humanInTheLoop":141,"kpisToInstrument":142,"failureModes":148},[117,120,123,126,129,132],{"title":118,"detail":119},"Start with one deal type and one step","Pick the most repetitive step for your team, such as first pass contract review for one deal type or company profiles for one strategy, and measure it before widening the scope.",{"title":121,"detail":122},"Write the checklist before the prompt","Turn the diligence request list and key terms checklist into explicit fields with definitions, so the extraction is complete and comparable across deals.",{"title":124,"detail":125},"Make citations mandatory","Every extracted term, figure and memo statement links to the page it came from, and the assistant says when something is not in the data room rather than guessing.",{"title":127,"detail":128},"Test on closed deals","Run the assistant on data rooms and outcomes from past deals and compare its findings with what the team and advisers found, including the issues that mattered most.",{"title":130,"detail":131},"Protect the deal","Keep each deal's documents in a separate, access controlled space, enforce information barriers, and make sure no deal data trains or reaches an unapproved model.",{"title":133,"detail":134},"Define who signs what","Agree with the deal team, counsel and the investment committee which outputs are drafts, who reviews them and how reviewed findings are marked in the memo.",[136,137,138,139,140],"Every statement in a profile, table or memo cites its source page, with a refusal when the source is missing","Deal data isolated per deal and user, with information barriers and no training on client data","Extraction checked against a fixed checklist, with confidence flags on uncertain fields","Redaction reviewed by a person before any document is released to bidders","Screening criteria documented, so targets are not excluded for reasons nobody can explain","The deal team owns sourcing decisions, the diligence findings and the memo. Associates check extracted terms against the source, counsel reviews legal findings and redactions, and the investment committee decides on an explicitly human reviewed memo. A sample of AI findings is compared with adviser reports after each deal to calibrate trust.",[143,144,145,146,147],"Hours per deal on document review and memo drafting, before and after","Recall of material issues on a test set of closed deals, compared with the team's own review","Share of extracted terms corrected by reviewers","Time from data room opening to first findings","Share of sourced targets that reach a first meeting or a term sheet",[149,152,155,158],{"title":150,"detail":151},"A fluent memo with a wrong number","A figure is misread from a scanned document or taken from the wrong period, and it survives into the investment committee memo. Require citations and check every material figure against the source.",{"title":153,"detail":154},"Silence taken for comfort","The assistant finds nothing on an issue because the document is missing or unreadable, and the team reads that as no issue. Report gaps against the request list explicitly.",{"title":156,"detail":157},"Confidential deal data leaking","Documents from one deal reach another team, another deal or an external model. Isolate data per deal and control which models and tools may see it.",{"title":159,"detail":160},"Sourcing that only finds the obvious","Rankings built on the same data every competitor licenses surface the same companies. Combine proprietary signals and past deal knowledge, and review targets the model ranked low.",{"euAiAct":162,"regulations":165,"guidance":170,"controls":182,"incidents":188},{"tier":163,"basis":164},"context-dependent","Decision support for professional investors and advisers about companies is not a use listed in Annex III and is not a practice prohibited by Article 5. The users are deal professionals who know they are working with an AI tool, and no consumer interacts with it, so the Article 50(1) duty to disclose an AI interaction has little practical effect. Article 50(2) is different: a firm that builds the assistant itself, including on a platform such as Blits.ai and putting it into service under its own name, is the provider of that system and must mark generated text in a machine readable format, unless the system only performs an assistive function for standard editing or does not substantially alter the input data or its semantics, which may cover extraction and redaction. A firm that instead licenses a vendor product, such as Datasite or Rogo, should confirm that the vendor meets this duty. Obligations are otherwise general: AI literacy for the deal team under Article 4 and, where personal data in the data room is processed, the GDPR.",[166,167,168,169],"eu-ai-act","gdpr","uk-gdpr","eu-mar",[171,177],{"title":172,"issuer":173,"region":174,"url":175,"note":176},"Risk Outlook report: The use of artificial intelligence in the legal market","Solicitors Regulation Authority","europe","https://www.sra.org.uk/sra/research-publications/artificial-intelligence-legal-market/","The regulator of solicitors in England and Wales on the risks of AI in legal work, including accuracy, confidentiality and supervision, relevant when AI supports legal due diligence.",{"title":178,"issuer":179,"region":174,"url":180,"note":181},"Guidance on AI and data protection","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/","How data protection law applies to AI systems, relevant to the employee and customer personal data found in data rooms.",[183,184,185,186,187],"Inventory entry for the assistant with an owner, approved data sources and approved models","Access control and information barriers per deal, with logging of every query and document read","Insider list and inside information handling for deals involving listed companies","Human review and sign off recorded for every finding that enters the investment committee memo","Periodic accuracy testing on closed deals and after every model change",[],{"howToBuild":190},"On Blits.ai this is an **agentic workflow** with **human in the loop** approval. For sourcing,\nthe agent uses the built in **web search and web page browsing** tools, **custom functions**\nthat call the firm's licensed data providers and **SQL knowledge bases** over the deal pipeline\nand past deal records, and writes company profiles as **structured output** for the deal team.\nFor diligence, data room exports and past memos go into a **knowledge base** that ingests PDF,\nWord, Excel and Outlook email files, and the agent answers from passages found with **hybrid\nretrieval**. The prompt and flow design require a citation to the source document for every\nstatement.\n\nA **flow** drives the key terms checklist deal by deal, and the **agent** drafts findings and\nthe memo in the firm's template, which the team reviews and completes. **Tenant isolation**\nkeeps the firm's data apart from other customers. With a separate bot and knowledge base per\ndeal, **role based access** with per bot roles limits who sees which deal. **PII masking** hides\npersonal data at the gateway, a per tenant **audit log** records user actions, and input and\noutput **guardrails** screen for prompt injection attempts. **Test suites**\nreplay questions on closed deals after every change, and the platform is model agnostic, with\nEU and UAE data residency, so the firm can choose where its deal data is processed.",[192,195,198,201],{"question":193,"answer":194},"Can AI do due diligence on its own?","No. It can read, extract, compare and draft, and Datasite's chief product officer says its AI features can potentially compress weeks of work into days, but the findings and the decision stay with people. An article in EQT's ThinQ publication reports a consensus that screening and early diligence are ripe for automation, while confirmatory checks and negotiation are not.",{"question":196,"answer":197},"How much time does AI save in M&A due diligence?","It depends on the step. Microsoft's case study on Datasite reports that Redaction AI lets sell side bankers and lawyers reduce redaction times by up to 80 percent, a figure Datasite bases on what customers tell it. Review and memo drafting are likely to save less because every finding needs checking, so measure hours per deal before and after.",{"question":199,"answer":200},"How do private equity firms use AI for deal sourcing?","EQT, for example, says it uses its Motherbrain platform to source deals, including a model that measures how similar companies are for tasks such as competitor mapping, and an EQT partner describes tools that rank potential targets by attractiveness across a range of criteria. EQT stresses that AI supports, rather than replaces, human decision making.",{"question":202,"answer":203},"Is it safe to put data room documents into an AI tool?","Only with controls: data isolated per deal, information barriers, no training on client data, logging of every access and approved model providers in approved regions. Freshfields, for example, runs its Dynamic Due Diligence tool on Google's Gemini models as part of a strategic collaboration with Google Cloud, and says its people use Gemini, NotebookLM Enterprise and Google Workspace daily with strong governance.",[205,206,207,208,209],"vendor-due-diligence","investment-research-summarization","credit-memo-drafting-agent","procurement-contract-review","intelligent-document-processing","2026-09-27",[212],{"date":210,"note":213},"First published","deal-sourcing-and-due-diligence-assistant",[216,251,279,311],{"title":217,"useCases":218,"organization":219,"vendors":223,"summary":230,"stage":231,"year":49,"channels":232,"languages":233,"metrics":235,"outcomeDisclosed":221,"sources":236,"verification":245,"grade":248,"id":249,"organizationSlug":250},"Freshfields: Dynamic Due Diligence, a proprietary AI tool for legal reviews",[214],{"name":220,"anonymized":221,"country":222,"region":174,"industry":20},"Freshfields",false,"GB",[224,227],{"name":225,"role":226},"Google Cloud","model-provider",{"name":228,"role":229},"Freshfields Lab","in-house","Freshfields, a global law firm, built Dynamic Due Diligence (D3), a proprietary tool designed to enhance legal reviews and due diligence, and in 2025 announced that Google's Gemini models would power it. A year into the collaboration the firm reported that D3 is one of several bespoke Freshfields Lab platforms now running on Gemini, and a partner who co leads Freshfields Lab said teams and clients use Gemini daily across those platforms. The firm has not published a separate outcome for D3.","production",[32],[234],"en",[],[237,241],{"url":238,"title":239,"publisher":220,"date":240},"https://www.freshfields.com/en/our-thinking/news/news-search/2025/04/freshfields-and-google-cloud-accelerate-legal-innovation-through-strategic-ai-collaboration2","Freshfields and Google Cloud Accelerate Legal Innovation Through Strategic AI Collaboration","2025-04-08",{"url":242,"title":243,"publisher":220,"date":244},"https://www.freshfields.com/en/our-thinking/news/news-search/2026/04/freshfields-reports-google-cloud-collaboration-delivering-transformation-at-scale","Freshfields Reports Google Cloud Collaboration Delivering Transformation at Scale","2026-04-15",{"level":246,"checkedAt":247},"source-verified","2026-09-26","B","freshfields-dynamic-due-diligence",null,{"title":252,"useCases":253,"organization":254,"vendors":257,"summary":260,"stage":261,"year":262,"channels":263,"languages":264,"metrics":265,"outcomeDisclosed":221,"sources":266,"verification":277,"grade":248,"id":278,"organizationSlug":250},"EQT: Motherbrain platform for deal sourcing and investment decisions",[214],{"name":255,"anonymized":221,"country":256,"region":174,"industry":19},"EQT","SE",[258],{"name":259,"role":229},"EQT Motherbrain","EQT, a global private markets investor, has run Motherbrain, its in house data and AI team and platform, since 2016. EQT says it uses Motherbrain across the firm to source deals and help investment teams make better informed decisions, for example by measuring the similarity between companies for competitor mapping, and a partner describes tools that rank potential targets by attractiveness across a range of criteria. The platform also captures lessons from past deals and tracks the deal pipeline, and EQT stresses that AI supports rather than replaces the dealmakers' judgment. No outcome figures have been published.","scaled",2016,[32],[234],[],[267,270,274],{"url":268,"title":269,"publisher":255},"https://eqtgroup.com/about/motherbrain","A Powerful Synergy of AI and Human Expertise",{"url":271,"title":272,"publisher":255,"date":273},"https://eqtgroup.com/news/eqt-s-ai-platform-motherbrain-pushes-the-boundaries-of-the-private-markets-with-novel-algorithm-for-better-decision-making","EQT's AI platform Motherbrain pushes the boundaries of the private markets with novel algorithm for better decision making","2021-11-08",{"url":48,"title":47,"publisher":275,"date":276},"ThinQ by EQT","2025-10-24",{"level":246,"checkedAt":247},"eqt-motherbrain-deal-sourcing",{"title":280,"useCases":281,"organization":282,"vendors":287,"summary":289,"stage":261,"year":49,"channels":290,"languages":291,"metrics":292,"outcomeDisclosed":301,"sources":302,"verification":308,"grade":309,"id":310,"organizationSlug":250},"Rogo: AI platform that builds company profiles and drafts investment memos for bankers",[214],{"name":283,"anonymized":221,"country":284,"region":285,"industry":286},"Rogo","US","north-america","technology",[288],{"name":225,"role":226},"Rogo, a New York AI company serving investment banks and private equity firms, combines a firm's own memos, research and files with external sources such as SEC filings, PitchBook, S&P Global, FactSet and Preqin, and automates workflows such as company profiles, competitive benchmarking, slide decks and investment memo drafts. Google Cloud reports that moving to Gemini 2.5 Flash cut hallucination rates in Rogo's evaluation, and counts thousands of bankers and analysts on the platform.",[32],[234],[293],{"kpi":59,"value":294,"unit":295,"qualifier":296,"period":297,"claimant":298,"quote":299,"sourceUrl":300},6000,"count","at-least","investment bankers and analysts on the platform","vendor","Builds trust in the Rogo AI platform among 6,000+ investment bankers and analysts","https://cloud.google.com/customers/rogo",true,[303,305],{"url":300,"title":304,"publisher":225},"Rogo: Enabling faster time-to-insights for financial services firms with agentic AI",{"url":306,"title":307,"publisher":283},"https://rogo.ai/","Rogo, AI for the most ambitious firms in finance",{"level":246,"checkedAt":247},"C","rogo-investment-banking-research-agents",{"title":312,"useCases":313,"organization":314,"vendors":316,"summary":320,"stage":231,"year":44,"channels":321,"languages":322,"metrics":323,"outcomeDisclosed":301,"sources":331,"verification":338,"grade":309,"id":339,"organizationSlug":250},"Datasite: Redaction AI for M&A data rooms",[214],{"name":315,"anonymized":221,"country":284,"region":285,"industry":286},"Datasite",[317],{"name":318,"role":319},"Microsoft","platform","Datasite, a virtual data room provider for mergers and acquisitions, added Redaction AI to its Datasite Diligence application. It uses named entity recognition to find personal and sensitive data across the documents a seller prepares for due diligence, so bankers and lawyers on the sell side can redact in batches instead of one document at a time; machine translation helps them file documents in other languages. Microsoft reports that redaction time falls sharply.",[32],[],[324],{"kpi":56,"value":325,"unit":326,"qualifier":327,"period":328,"baseline":329,"claimant":298,"quote":330,"sourceUrl":43},80,"percent","up-to","redaction step only (share of time and resources saved on redacting data room documents before due diligence), not the end to end deal cycle","manual review and redaction document by document","Now, the lawyers and investment bankers who coordinate sales can reduce redaction times by up to 80 percent, helping to move deals forward faster and support successful outcomes.",[332,334],{"url":43,"title":42,"publisher":318,"date":333},"2021-11-10",{"url":335,"title":336,"publisher":318,"date":337},"https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/07/24/ai-powered-success-with-1000-stories-of-customer-transformation-and-innovation/","AI-powered success, with more than 1,000 stories of customer transformation and innovation","2025-07-24",{"level":246,"checkedAt":210},"datasite-redaction-ai",1,[342,348],{"kpi":59,"label":343,"unit":295,"aggregate":221,"higherIsBetter":301,"n":340,"nUpTo":344,"median":294,"min":294,"max":294,"byClaimant":345,"vendorOnly":301,"points":346},"Users served",0,{"organization":344,"vendor":340,"regulator":344,"independent":344},[347],{"evidenceId":310,"organization":283,"value":294,"qualifier":296,"claimant":298,"grade":309,"pooled":301},{"kpi":56,"label":349,"unit":326,"aggregate":301,"higherIsBetter":301,"n":344,"nUpTo":340,"median":250,"min":250,"max":250,"byClaimant":350,"vendorOnly":221,"points":351},"Productivity gain",{"organization":344,"vendor":344,"regulator":344,"independent":344},[352],{"evidenceId":339,"organization":315,"value":325,"qualifier":327,"claimant":298,"grade":309,"pooled":221},{"low":354,"high":355},135000,1080000,[357,378,400,414,430],{"slug":205,"title":358,"shortTitle":359,"definition":360,"status":9,"industries":361,"functions":367,"patterns":370,"audience":34,"autonomy":35,"adoptionStage":36,"segment":371,"evidenceCount":372,"publicEvidenceCount":372,"organizations":373,"bestGrade":248,"headline":250,"lastVerified":210,"indexable":301},"AI for third party and vendor risk due diligence","Vendor due diligence","AI that reviews a vendor's security questionnaires, SOC and assurance reports, contracts and model documentation against the organization's control requirements, researches the vendor's ownership, sanctions, financial health and adverse media, drafts the risk assessment for a human to approve and keeps the register of material service providers current with ongoing monitoring.",[362,363,364,365,366],"cross-industry","banking","insurance","government","payments",[368,24,369],"procurement","regulatory-compliance",[26,28,29,27],"second-line",4,[374,375,376,377],"U.S. Department of Justice","Internal Revenue Service","U.S. Department of Agriculture","U.S. Trade and Development Agency",{"slug":206,"title":379,"shortTitle":380,"definition":381,"status":9,"industries":382,"functions":383,"patterns":386,"audience":34,"autonomy":35,"adoptionStage":36,"segment":37,"evidenceCount":372,"publicEvidenceCount":372,"organizations":389,"bestGrade":248,"headline":394,"lastVerified":210,"indexable":301},"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.",[19,18,363],[22,384,385],"sales","knowledge-management",[27,28,387,388],"content-generation","translation",[390,391,392,393],"Citi","Deutsche Bank","Morgan Stanley","UBS",{"kpi":57,"label":395,"unit":396,"n":344,"nUpTo":340,"kind":397,"value":398,"qualifier":327,"claimant":399,"organization":391,"vendorReported":221},"Time saved per task","minutes","reported",120,"organization",{"slug":207,"title":401,"shortTitle":402,"definition":403,"status":9,"industries":404,"functions":405,"patterns":408,"audience":34,"autonomy":35,"adoptionStage":36,"segment":409,"evidenceCount":410,"publicEvidenceCount":410,"organizations":411,"bestGrade":248,"headline":250,"lastVerified":210,"indexable":301},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.",[363],[406,407,24],"lending-and-credit","underwriting",[26,29,28,387],"specialized-businesses",2,[412,413],"Banestes","DBS Bank",{"slug":208,"title":415,"shortTitle":416,"definition":417,"status":9,"industries":418,"functions":421,"patterns":423,"audience":34,"autonomy":35,"adoptionStage":36,"evidenceCount":424,"publicEvidenceCount":425,"organizations":426,"bestGrade":248,"headline":250,"lastVerified":210,"indexable":301},"AI assistant for procurement and supplier contract review","Procurement and contract review","An assistant for procurement and vendor management that reads supplier contracts and proposals, extracts the key terms, flags deviations from the organization's standard positions, drafts requests for proposal and evaluation matrices, and prepares negotiation positions, with a procurement or legal owner approving every conclusion.",[362,363,365,419,420],"retail-and-ecommerce","manufacturing",[368,23,422],"finance-and-accounting",[26,28,387,29],6,5,[427,428,375,429],"General Services Administration","Administration for Children and Families","Walmart",{"slug":209,"title":431,"shortTitle":432,"definition":433,"status":9,"industries":434,"functions":436,"patterns":439,"audience":442,"autonomy":443,"adoptionStage":444,"evidenceCount":445,"publicEvidenceCount":425,"organizations":446,"bestGrade":248,"headline":452,"lastVerified":210,"indexable":301},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[362,365,435,420],"automotive",[437,438,422],"operations","case-management",[26,440,441],"computer-vision","classification-and-routing","back-office","supervised-agent","mainstream",7,[447,448,449,450,451],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":60,"label":453,"unit":326,"n":340,"nUpTo":344,"kind":397,"value":454,"qualifier":296,"claimant":298,"organization":447,"vendorReported":301},"Accuracy",90,{"indexable":301,"reasons":456},[],[458,464,469,477,484,490,495,502,510,517,524,530,537,543,549,554,561,567,573,579,585,591,597,602,607,614,621,626,631,638,644,650,657,661],{"id":166,"label":459,"issuer":460,"region":174,"url":461,"description":462,"useCases":463,"indexable":301},"EU AI Act","European Union","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":167,"label":465,"issuer":460,"region":174,"url":466,"description":467,"useCases":468,"indexable":301},"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":470,"label":471,"issuer":472,"region":473,"url":474,"description":475,"useCases":476,"indexable":301},"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":478,"label":479,"issuer":480,"region":285,"url":481,"description":482,"useCases":483,"indexable":301},"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.",83,{"id":485,"label":486,"issuer":460,"region":174,"url":487,"description":488,"useCases":489,"indexable":301},"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":168,"label":491,"issuer":179,"region":174,"url":492,"description":493,"useCases":494,"indexable":301},"UK GDPR","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":496,"label":497,"issuer":498,"region":174,"url":499,"description":500,"useCases":501,"indexable":301},"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":503,"label":504,"issuer":505,"region":506,"url":507,"description":508,"useCases":509,"indexable":301},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":511,"label":512,"issuer":513,"region":506,"url":514,"description":515,"useCases":516,"indexable":301},"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":518,"label":519,"issuer":520,"region":473,"url":521,"description":522,"useCases":523,"indexable":301},"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":525,"label":526,"issuer":527,"region":285,"url":528,"description":529,"useCases":523,"indexable":301},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":531,"label":532,"issuer":533,"region":174,"url":534,"description":535,"useCases":536,"indexable":301},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":538,"label":539,"issuer":540,"region":473,"url":541,"description":542,"useCases":67,"indexable":301},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",{"id":544,"label":545,"issuer":460,"region":174,"url":546,"description":547,"useCases":548,"indexable":301},"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":550,"label":551,"issuer":460,"region":174,"url":552,"description":553,"useCases":548,"indexable":301},"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":555,"label":556,"issuer":557,"region":285,"url":558,"description":559,"useCases":560,"indexable":301},"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":562,"label":563,"issuer":460,"region":174,"url":564,"description":565,"useCases":566,"indexable":301},"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":568,"label":569,"issuer":570,"region":285,"url":571,"description":572,"useCases":566,"indexable":301},"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":574,"label":575,"issuer":576,"region":473,"url":577,"description":578,"useCases":566,"indexable":301},"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":580,"label":581,"issuer":460,"region":174,"url":582,"description":583,"useCases":584,"indexable":301},"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":586,"label":587,"issuer":588,"region":285,"url":589,"description":590,"useCases":584,"indexable":301},"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":592,"label":593,"issuer":505,"region":506,"url":594,"description":595,"useCases":596,"indexable":301},"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":598,"label":599,"issuer":460,"region":174,"url":600,"description":601,"useCases":596,"indexable":301},"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":603,"label":604,"issuer":460,"region":174,"url":605,"description":606,"useCases":596,"indexable":301},"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":608,"label":609,"issuer":610,"region":174,"url":611,"description":612,"useCases":613,"indexable":301},"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":615,"label":616,"issuer":617,"region":285,"url":618,"description":619,"useCases":620,"indexable":301},"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":622,"label":623,"issuer":460,"region":174,"url":624,"description":625,"useCases":620,"indexable":301},"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":627,"label":628,"issuer":460,"region":174,"url":629,"description":630,"useCases":424,"indexable":301},"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":632,"label":633,"issuer":634,"region":635,"url":636,"description":637,"useCases":425,"indexable":301},"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":639,"label":640,"issuer":641,"region":174,"url":642,"description":643,"useCases":372,"indexable":301},"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":645,"label":646,"issuer":647,"region":174,"url":648,"description":649,"useCases":372,"indexable":301},"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":651,"label":652,"issuer":653,"region":506,"url":654,"description":655,"useCases":656,"indexable":301},"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.",3,{"id":169,"label":658,"issuer":460,"region":174,"url":659,"description":660,"useCases":656,"indexable":301},"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":662,"label":663,"issuer":664,"region":285,"url":665,"description":666,"useCases":656,"indexable":301},"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.",1790598296710]