[{"data":1,"prerenderedAt":131},["ShallowReactive",2],{"uc-org-bny":3},{"organization":4,"includeUnpublished":13,"evidence":14},{"slug":5,"name":6,"country":7,"region":8,"industry":9,"records":10,"useCases":11,"indexable":12},"bny","BNY","US","north-america","capital-markets",4,3,true,false,[15,54,81,105],{"title":16,"useCases":17,"organization":19,"vendors":20,"summary":26,"stage":27,"year":28,"channels":29,"languages":31,"metrics":33,"outcomeDisclosed":12,"sources":43,"verification":46,"grade":49,"id":50,"useCaseTitles":51},"BNY: Eliza AI platform resolves client transaction inquiries faster",[18],"settlement-fail-prediction-and-exception-management",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9},[21,23],{"name":6,"role":22},"in-house",{"name":24,"role":25},"Microsoft","platform","BNY built its own AI platform, Eliza, on Microsoft Azure and Microsoft Foundry, and uses it across operations. In a Microsoft customer story, BNY's Head of AI Enablement says more than ten percent of the inquiries clients raise about BNY's transactions were resolved or assisted by AI, with eighty percent faster processing of those inquiries. Microsoft's summary calls them client settlement inquiries; BNY's own words are broader, so the link to settlement exception work is adjacent rather than direct. The same story describes a digital employee that repairs incomplete payment instructions and an agentic workflow for client onboarding research.","production",2026,[30],"internal-tools",[32],"en",[34],{"kpi":35,"value":36,"unit":37,"qualifier":38,"period":39,"claimant":40,"quote":41,"sourceUrl":42},"processing-time-reduction",80,"percent","exact","client transaction inquiries resolved or assisted by AI","organization","More than ten percent of these inquiries were resolved or assisted by AI and that has resulted in eighty percent faster processing of these inquiries.","https://www.microsoft.com/en/customers/story/27322-bny-microsoft-365-copilot",[44],{"url":42,"title":45,"publisher":24},"Frontier Firm BNY resolves client inquires 80% faster with Microsoft AI powered Eliza",{"level":47,"checkedAt":48},"source-verified","2026-09-27","C","bny-eliza-client-settlement-inquiries",[52],{"slug":18,"title":53},"AI for settlement fail prediction and post trade exception management",{"title":55,"useCases":56,"organization":58,"vendors":59,"summary":62,"stage":27,"year":28,"channels":63,"languages":64,"metrics":65,"outcomeDisclosed":12,"sources":74,"verification":76,"grade":49,"id":77,"useCaseTitles":78},"BNY: Eliza AI platform speeds up client onboarding research",[57],"business-onboarding-and-ubo-discovery",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9},[60,61],{"name":6,"role":22},{"name":24,"role":25},"BNY built its own AI platform, Eliza, on Microsoft Azure and Microsoft Foundry. In a Microsoft customer story, Saed Shonnar, Head of AI Enablement at BNY, says twenty five percent of the bank's new client onboardings this year were assisted with AI, resulting in a twenty percent faster onboarding process on average. A second BNY executive describes the research element of onboarding, the document processing and decision making behind verifying a new client, as the common challenge the AI addresses. The same story describes a digital employee that repairs incomplete payment instructions and faster processing of client settlement inquiries, which are reported as separate use cases.",[30],[32],[66,71],{"kpi":67,"value":68,"unit":37,"qualifier":38,"period":69,"claimant":40,"quote":70,"sourceUrl":42},"automation-rate",25,"new client onboardings this year","Twenty-five percent of all of our new onboardings were assisted with AI this year and that has resulted in a twenty percent faster onboarding process on average for these clients,",{"kpi":35,"value":72,"unit":37,"qualifier":38,"period":73,"claimant":40,"quote":70,"sourceUrl":42},20,"new client onboardings this year, average",[75],{"url":42,"title":45,"publisher":24},{"level":47,"checkedAt":48},"bny-eliza-onboarding-research",[79],{"slug":57,"title":80},"AI for business onboarding (KYB) and beneficial ownership discovery",{"title":82,"useCases":83,"organization":85,"vendors":86,"summary":89,"stage":27,"year":28,"channels":90,"languages":91,"metrics":92,"outcomeDisclosed":12,"sources":98,"verification":100,"grade":49,"id":101,"useCaseTitles":102},"BNY: Eliza digital employee repairs over 10% of payment instructions worldwide",[84],"payment-investigations-and-exceptions",{"name":6,"anonymized":13,"country":7,"region":8,"industry":9},[87,88],{"name":6,"role":22},{"name":24,"role":25},"BNY built its own AI platform, Eliza, on Microsoft Azure and Microsoft Foundry. In a Microsoft customer story, a BNY executive describes a digital employee that repairs missing or incomplete payment instructions so the payment can proceed without a time consuming manual review. The same executive says that digital employee now handles over ten percent of BNY's payment repair issues worldwide. The same story describes an agentic workflow for client onboarding research and faster processing of client settlement inquiries, which are reported as separate use cases.",[30],[32],[93],{"kpi":67,"value":94,"unit":37,"qualifier":95,"period":96,"claimant":40,"quote":97,"sourceUrl":42},10,"at-least","payment repair issues worldwide","Today, that digital employee handles over ten percent of our payment repair issues around the world.",[99],{"url":42,"title":45,"publisher":24},{"level":47,"checkedAt":48},"bny-eliza-payment-repair-digital-employee",[103],{"slug":84,"title":104},"AI for payment investigations and exceptions",{"title":106,"useCases":107,"organization":108,"vendors":109,"summary":113,"stage":114,"year":115,"channels":116,"languages":117,"metrics":118,"outcomeDisclosed":13,"sources":119,"verification":126,"grade":127,"id":128,"useCaseTitles":129},"BNY Mellon: machine learning to predict US Treasury settlement fails, with Google Cloud",[18],{"name":6,"anonymized":13,"country":7,"region":8,"industry":9},[110,112],{"name":111,"role":25},"Google Cloud",{"name":6,"role":22},"On 4 February 2021 BNY, then branded BNY Mellon, announced a collaboration with Google Cloud to predict settlement failures in the US Treasury market, where it provides clearance and settlement. It trains models on millions of trades on Google Cloud's data analytics and machine learning services. BNY Mellon's Clearance and Collateral Management head described the aim as helping clients predict approximately 40% of settlement failures in Fed eligible securities with 90% accuracy. The release describes a solution in development; no production results were published.","announced",2021,[],[32],[],[120,124],{"url":121,"title":122,"publisher":6,"date":123},"https://www.bny.com/corporate/global/en/about-us/newsroom/company-news/bny-mellon-and-google-cloud-collaborate-to-help-transform-us-treasury-market-settlement-and-clearance-process.html","BNY Mellon and Google Cloud Collaborate to Help Transform U.S. Treasury Market Settlement and Clearance Process","2021-02-04",{"url":125,"title":122,"publisher":111,"date":123},"https://www.googlecloudpresscorner.com/22021-02-04-BNY-Mellon-and-Google-Cloud-Collaborate-to-Help-Transform-U-S-Treasury-Market-Settlement-and-Clearance-Process",{"level":47,"checkedAt":48},"B","bny-mellon-treasury-settlement-fail-prediction",[130],{"slug":18,"title":53},1790598320650]