{"id":451,"date":"2026-03-23T09:07:44","date_gmt":"2026-03-23T09:07:44","guid":{"rendered":"https:\/\/www.ieeesmc.org\/cai-2026\/?page_id=451"},"modified":"2026-04-20T08:49:46","modified_gmt":"2026-04-20T08:49:46","slug":"panel-4-ai-for-business","status":"publish","type":"page","link":"https:\/\/www.ieeesmc.org\/cai-2026\/panel-4-ai-for-business\/","title":{"rendered":"Panel 4: AI for Business"},"content":{"rendered":"<p style=\"font-size: 1.2em;font-weight: bold\">Abstract<\/p>\n<p>AI is rapidly moving from experimental prototypes to business-critical application. Machine Learning, LLMs and Agentic AI are  set to transform how companies optimize operations, manage personnel, forecast and influence demand, and design the client journey. In financial institutions, both front-office and internal functions like compliance and risk management are going to be impacted. This panel will explore how AI can bridge the gap between \u201cchat-boxes\u201d and real-world business impact, through managing data flows, addressing privacy and security constraints, ensuring auditability, and integrating AI into decision-making processes. The discussion will focus on trustworthiness and interpretability, rigorous stress-testing under real-world conditions, and the role of causal analysis in avoiding unintended consequences. We will touch upon approaches ranging from classical machine learning to Transformers and AI Agents and discuss best practices for AI governance and AI model risk management.<\/p>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Target Audience<\/p>\n<p>AI researchers interested in practical impact and responsible deployment; industry practitioners building AI products and decision-support systems; FinTech leaders exploring AI applications in risk, compliance, and investments; advanced students and researchers seeking applied research directions<\/p>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Presenters<\/p>\n<ul>\n<li>Massimo Morini (moderator): Massimo leads Digital Economy research at ADIA Lab. He was Chief Economist at Algorand blockchain from 2019 to 2023 and Head of Interest Rate and Credit Modelling team at Intesa Bank from 2008 to 2019. He has been Senior Advisor for the World Bank and the Monetary Authority of Singapore, and a Board member of the Blockchain consortium R3. Massimo is Professor of Finance at Bocconi University and of Blockchain and Cryptocurrencies at USI Lugano and Politecnico di Milano. He authored several papers on quantitative finance and risk management, and seminal books on Model Risk and Credit Risk, collateral and funding, beside pioneering technical articles on decentralized derivatives cited by US Congress and regulators. He has a MSc in Economics and a PhD in Mathematics.<\/li>\n<li><a href=\"https:\/\/www.adialab.ae\/bios\/professor-marcos-lopez-de-prado\">Marcos Lopez De Prado<\/a><\/li>\n<li><a href=\"https:\/\/www.adialab.ae\/bios\/professor-alex-lipton\">Alex Lipton<\/a><\/li>\n<li><a href=\"https:\/\/www.adialab.ae\/bios\/professor-alex-pentland\">Sandy Pentland<\/a><\/li>\n<li><a href=\"https:\/\/www.javierparra.net\/en\/home\/\">Javier Parra D.<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Outline and Description of the Panel<\/p>\n<ul>\n<li>Opening from Moderator<\/li>\n<li>Differences and common issues across industries and sectors<\/li>\n<li>Capabilities and limits across approaches<\/li>\n<li>Trustworthiness, stress-testing, model risk management and governance<\/li>\n<li>Data flow, privacy, interpretability and auditability<\/li>\n<li>Decision integration and human oversight<\/li>\n<li>Finance-specific use cases and issues<\/li>\n<li>Research gaps and open problems<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Abstract AI is rapidly moving from experimental prototypes to business-critical application. Machine Learning, LLMs and Agentic AI are set to transform how companies optimize operations, manage personnel, forecast and influence demand, and design the client journey. In financial institutions, both front-office and internal functions like compliance and risk management are going to be impacted. This&#8230;<\/p>\n","protected":false},"author":2627,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-451","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Panel 4: AI for Business - IEEE CAI 2026<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ieeesmc.org\/cai-2026\/panel-4-ai-for-business\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Panel 4: AI for Business - IEEE CAI 2026\" \/>\n<meta property=\"og:description\" content=\"Abstract AI is rapidly moving from experimental prototypes to business-critical application. Machine Learning, LLMs and Agentic AI are set to transform how companies optimize operations, manage personnel, forecast and influence demand, and design the client journey. In financial institutions, both front-office and internal functions like compliance and risk management are going to be impacted. 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