AI in Finance Summit New York
AI in Finance Summit New York Pure Storage is a platinum sponsor of the AI in Finance Summit in New York on April 15th and 16th. Stop by our booth to learn about how Pure can help with your enterprise AI initiatives and be sure to check out Michael Cornwell's keynote address on the 15th. At the AI in Finance Summit, where cutting-edge research meets application in financial services, explore exclusive insights and advanced technical use cases presented by AI experts and data scientists from the Banking, Financial Services, and Insurance sectors.Financial Services Sessions at Accelerate 2026
Accelerate is almost here! 🙌 Check out our special track designed to connect with FSI professionals. Hear from Everpure experts about the latest trends and strategies for data management success in Financial Services. If this sounds right up your alley, we’ve curated a list of must-attend breakout sessions focused on all things Financial Services. Check out these don't miss sessions: Why 95% of AI Pilots Fail at Scale, and How a Finance Sector MSP Beat the Odds Sovereign by Design: Delivering a Cloud Operating Model within the Firm’s Own Borders Cyber Defense in a Flash: Spotting the Smoke Before the Fire Will you be joining us in person? Drop a comment below with the session you're most interested in and what you hope to learn!AI & Fintech: Can We Put Our Trust Into Autonomous Payments?
There are two distinct areas defining the transformation of the financial industry today and one of them is embracing AI, but not without risk. As Agentic AI automates payments and lending, algorithms can amplify bias, threatening the entire credibility banking is built upon. To tackle these widespread challenges, the industry is taking on a more collaborative approach by joining banks, fintechs and policymakers alike. Expert Amanda Estiverne-Colas, Director and Head of Payments Practice at Perficient, says human oversight and empathy are non-negotiables in our financial responsibility to keep banking human amidst the changes. Read the entire article: To Build Fintech for Everybody, Combine the Speed of a Startup with the Soul of a Community Bank Our community question is: What technical measures (like data cleansing) should IT/Storage teams prioritize to ensure AI models remain trustworthy and fair? Click through to read the entire article above and let us know your thoughts around it in the comments below!Accelerate Breakout Replay: Leveraging the Power of AI to Gain Competitive Advantage in Capital Markets
See how Pure Storage + KX power real-time GenAI insights in capital markets, using ultra-low latency vector databases and fast data access. Speakers: Mike Russo Rob Glanzman https://www.purestorage.com/video/webinars/leveraging-the-power-of-ai-competitive-advantage/6375341256112.htmlSTAC Summit, London
STAC Summit - London Come visit us at STAC Summit London on April 29th! Pure Storage will be sponsoring and presenting so join our session or stop by our table. STAC Summits bring together CTOs and other industry leaders responsible for solution architecture, infrastructure engineering, application development, machine learning/deep learning engineering, data engineering, and operational intelligence to discuss important technical challenges in trading and investment. Topics tend to cluster in four areas: Quant technology. Architectures for big data and big compute workloads such as model training, strategy research & backtesting, tick analytics, alt data processing, market and credit risk, portfolio optimization, fraud detection, and compliance. Low-latency infrastructure. Architectures for fast decision making and trade execution, primarily in automated trading. Networks, FPGA, servers, software, and other technologies for market data, trading and matching algorithms, execution, and trade-time risk. Command and control. Monitoring of trade flows, time synchronization, and orchestration and management of on-prem and cloud-based infrastructure. AI/ML. Architectures and infrastructure for models and data exploration, training, and inference with deep neural networks (especially LLMs) and statistical machine learning on-prem and in the cloud. While applied AI may be part of quant technology and low-latency topics, there will be a separate, dedicated AI Track at the London STAC Summit that focuses on the AI infrastructure needs of finance, from banking and insurance to trading and investment —from the model to the metal™.What’s getting in the way of AI moving into production?
Hi everyone! A lot of companies are experimenting with AI. Fewer are getting those projects into production. This Data Wire article looks at what can slow that down in financial services, including data readiness, governance, orchestration, and resilience. What are you seeing? What’s the biggest hurdle between an AI pilot and production? Would love to hear what others are seeing in the field.Accelerate Breakout Replay: GenAI in Finance: Simplified, Scalable, and Ready for Real Time
This session covers simplified deployment, RAG workflows, reliable networking, real-time insights, and scalable AI compute for secure, low-latency finance AI. Speakers: Mike Russo Philip Ninan Jon Owings Ryan Avery, WWT Ed Chapman, Arista Kevin Curtis, Supermicro Prabhu Ramamanmoorthy, NVIDIA Nataraj Dasgupta, KX Systems https://www.purestorage.com/video/webinars/gen-ai-in-finance-ready-for-real-time/6375812645112.htmlBuilding the data foundations for the era of AI-powered banking
Everpure sponsored MoneyLIVE North America in Chicago on September 14 and 15. Mike Russo and Richard Galvez co-hosted a roundtable on building the data foundations for the era of AI-powered banking. Richard also joined a panel on whether data and AI can be harnessed effectively against fraud. Both sessions circled the same constraint. In banking, the hard part is rarely the model. It is whether the data underneath it is governed, current and trustworthy enough to put in front of a customer or a regulator. What is holding up your first production use case, the model or the data under it?AI in Production: What Does “It Works” Mean?
Would love your take on this Data Wire piece with MikeRusso and Ricard Galvez on moving AI from pilot into production in capital markets. One question from Richard really stuck with me: what does “it works” actually mean? A model can pass its evaluation and still not make the work better, faster, or less expensive. Pilots measure whether the model performs. Production has to measure whether the work improved. That second test is harder, and it often does not have a clear owner. So I’m curious: on your team, what does “it works” mean for AI?CX BFSI Exchange | Banking & Financial Services
Everpure is heading to Miami. We’re a Silver Sponsor at the CX Banking and Financial Services Exchange, October 13–14. Dona Munsch, VP of CX Platforms and Delivery at Everpure, will join a panel on a big AI question: Can you trust the output if you can’t trust the data? The discussion will dig into what happens when customer data is fragmented across inconsistent systems, and what a validated data foundation needs to look like before AI can deliver reliable results. Attending? Drop a comment and let’s connect in the Silver Sponsor zone.
