Lucas Quarta

Last week, I had the honor of joining a stellar lineup of public and private sector leaders at the #Eurofi Forum 2025 in Copenhagen to discuss how AI will transform financial services. With simplification, competitiveness and growth top of mind, the forum’s agenda – Europe’s macroeconomic outlook, evolving regulatory frameworks, digitalization, sustainable finance and technology transition – couldn’t have been timelier. I had the opportunity to share how BNY is leading the way with our commitment to AI: -Our culture is “AI for everyone, everywhere for everything” with 97% of colleagues using GenAI daily and more than one third building their own agents. -We’ve deployed over 80 AI solutions, including digital agents to repair code vulnerabilities and manage payment instructions (you can read more here: https://lnkd.in/ee64n_EP). -Through widespread AI adoption, BNY is supercharging our employees and creating measurable value across all businesses. -In our Data platform, we’ve launched two flagship AI features – Intelligent Document Processing, which automates extraction from unstructured documents, and Metadata Auto Classification, which applies clients’ taxonomies at scale. These capabilities help our clients save time, reduce manual lift and make faster, more confident decisions. None of this would be possible without a strong data foundation: single, trusted sources of truth, cloud-first architecture and metadata-driven governance. At BNY, we treat data as a strategic asset, because AI’s promise only unlocks when your data is AI-ready. Thank you to Eurofi, my fellow panelists and everyone who joined us for this important conversation on powering growth, innovation and resilience in financial services. I couldn’t be more excited to help our clients navigate the digital transitions ahead. #AI #data #Innovation #lifeatBNY #digitaltransformation

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𝐓𝐡𝐞 𝐬𝐞𝐜𝐨𝐧𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐢𝐧 𝐟𝐢𝐧𝐚𝐧𝐜𝐞: IBM 𝐚𝐧𝐝 Vanguard, 𝐭𝐡𝐞 𝐰𝐨𝐫𝐥𝐝'𝐬 𝐬𝐞𝐜𝐨𝐧𝐝-𝐥𝐚𝐫𝐠𝐞𝐬𝐭 𝐚𝐬𝐬𝐞𝐭 𝐟𝐮𝐧𝐝, 𝐮𝐬𝐞𝐝 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞 𝐚 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐩𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. ➡️ The IBM-Vanguard team explore how sampling-based variational quantum algorithms, #VQA may be able to address this challenge. These methods combine the strengths of quantum sampling with classical optimization and post-processing, enabling a new class of #heuristics algorithms to explore complex solution landscapes more efficiently than traditional methods. ➡️ The team applied the Conditional Value at Risk-based Variational Quantum Algorithm, #CVaRVQA method to an Exchange Traded Fund (#ETF) portfolio construction problem; The results were benchmarked against the classical optimization solver #CPLEX, some important informations: 1️⃣ the quantum-classical workflow consistently outperformed a purely classical local search approach, especially as problem size increased. 2️⃣ There is 3 important dimensions for bonds #risk, #industry and #duration as a optimization objectives. 3️⃣ They used 109 #qubits of the 133 available on an IBM Quantum #Heron r1 processor, executing circuits with up to 4,200 gates. The quantum samples were then refined using a classical local search algorithm to further improve solution quality. IBM Quantum #VQA #Quantum_optimization

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As organizations accelerate their AI ambitions, one truth stands out responsible AI begins with responsible data.🧬 🔮📘This article explores how traditional data governance evolves into the broader realm of AI governance, where trust, ethics, and accountability matter just as much as accuracy and performance. It reflects on how principles like data quality, lineage, and privacy now extend into model transparency, bias management, and ethical oversight guiding enterprises on a journey from managing data to governing intelligence itself. #DataGovernance #AIGovernance #ResponsibleAI #DataEthics #TrustworthyAI #AIReadiness #MetadataMatters #HumanizingAI #DataLeadership #GAFAI #DGbyGopiMaren #AITransformation #DigitalTrust #DataStrategy #GovernanceByDesign https://lnkd.in/drzkjm9g

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#5DaysofData posts! Day 1: AI-Ready Data Series - The Power of Semantic Consistency As data executives, we're all looking to harness the power of AI, but the journey starts with our data itself. For generative and agentic AI to truly thrive, our data environment needs a strategic overhaul! Today, let's talk about Semantic Consistency and Context. Imagine asking an AI to analyze "customer feedback" when some systems call them "clients," others "users," and still others track "account holders." Without a unified understanding of what a "customer" truly means across all your disparate data sources, your AI will stumble, generate irrelevant insights, or worse, make incorrect decisions. 1) Why this matters: • For Generative AI: It ensures AI understands the true meaning behind your prompts, leading to more accurate, relevant, and valuable content creation. • For Agentic AI: It empowers AI agents to reason effectively, make informed decisions, and take precise actions based on a coherent understanding of your business landscape. 2) The Strategy: Invest in creating a robust semantic layer for your data. This means defining common business terms, establishing clear relationships between data entities, and building a shared understanding across your enterprise. This also means your enterprise has clear data ownership, and a thriving data quality and data governance practice. This isn't just a technical task! It is a critical business imperative that enables your AI to speak the same language as your organization. Ready to dive deeper? Stay tuned for Day 2! #AIDataReady #DataStrategy #GenerativeAI #AgenticAI #DataTransformation #CIOs #CTOs #Executives

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MCP (Model Context Protocol) has emerged as the standard way for LLMs to connect to external tools, servers, and data sources. The protocol defines how those connections work - a shared schema for exposing resources and tool calls. That’s why it’s being adopted across OpenAI, Anthropic, LangGraph, CrewAI, Composio, and more. But here’s the reality: MCP only gives you the spec. If you’re building an app, you still need to: ▪️ tell your copilot which MCP servers to talk to ▪️ manage how responses flow into conversations ▪️ and build UI to make tool calls usable It works, but it’s a lot of wiring. That’s the gap CopilotKit🪁 (AG-UI)’s MCP Client closes. Check here: https://lnkd.in/dF9R7PrZ Point your copilot at an MCP server, and it automatically: ✔️ discovers tools and resources at runtime ✔️ makes them callable by the LLM like native actions ✔️ streams results directly into the conversation ✔️ renders them with built-in UI components Example: instead of hand-building a dashboard for SQL queries, your copilot can connect to a database MCP server, let the LLM generate queries, and then render the results in-app with a ready-made table component. No extra plumbing required. 👉 MCP defined the protocol. 👉 CopilotKit MCP Client makes it practical in your app. Setup takes one command: npx copilotkit@latest init -m MCP

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Besides Large Language Models (LLMs), we have LMM, LVM, LAM, LDM, LWM, LRM, LGM, VLM, MLLM and Large World Models (LWMs) — among others. Some of the most fascinating are World Models, capable of simulating and reasoning about environments — with applications ranging from defense and logistics to autonomous systems and videogames. https://lnkd.in/dMWywgrM #AI #GenerativeAI #BankingInnovation #WorldModels #FutureOfFinance

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