Agenda Day 2

Gain insight into today’s sessions so you can get the most out of your conference experience.

Delivering AI at scale in global payments is fundamentally different from deploying models in controlled, single-market environments. With operations spanning more than 200 countries, payment flows crossing multiple regulatory regimes, and real-time fraud risk, success depends on far more than algorithmic sophistication. It requires an AI strategy designed for extensibility, governance, and operational reality. Enhance your strategy to:

• Extend AI capabilities into existing payment and risk platforms through modular, layered architectures rather than large-scale system replacement.
• Build adaptive models that evolve with shifting fraud patterns, customer behavior, and market dynamics using parametric feature engineering and continuous feedback loops.
• Operationalize AI in compliance-heavy environments by aligning data science, MLOps, privacy, security, and regulatory teams around shared accountability.
• Develop multidisciplinary AI teams capable of supporting the full lifecycle—from data ingestion and governance through deployment, monitoring, and optimization.

Transform AI into a true driver of growth and financial inclusion

Financial services organizations generate enormous volumes of operational data across payments, custody, finance, risk, and customer operations, yet siloed systems often prevent that data from being used in real time. Organizations need a unified approach that turns everyday operational data into decision intelligence across the enterprise. Adopt practical approaches to: 

• Build unified operational data layers that break down silos and provide consistent, real-time visibility across operations, finance, risk, compliance, and customer experience. 
• Apply AI-enabled analytics to core operational data to support faster decisions, smarter prioritization, and more resilient target operating models. 
• Scale decision intelligence across the organization by embedding analytics into workflows that drive automation, performance improvement, and operational resilience. 

Turn operational data into a strategic asset by enabling faster, more informed decisions that unlock measurable value for customers, employees, and the business. 

Break into smaller groups of approximately 20 industry peers to work through a series of questions and challenges. Share knowledge on a particular topic that is most critical to your role and business.

1. The AI Risk Equation: Balancing Innovation with Compliance
   How to rapidly experiment with generative AI, predictive analytics, and automation while ensuring adherence to evolving regulations like OSFI guidelines, Bill C-27, and cross-border data rules.
2. From Data Chaos to Data Confidence: Fixing Fragmented Architectures
   Practical strategies to unify siloed data environments, improve lineage and trust, and support near real-time decision-making in a hybrid or multi-cloud infrastructure.
3. The Risk of Doing Nothing: Is playing It safe with AI, safe at all?
   Explore the organizational and personal implications of delaying AI adoption, including building confidence, reducing fear, and whether AI is the best way to future-proof organizations and careers. 
4. Monetizing Data Responsibly Without Eroding Customer Trust
   Exploring models for productizing insights, embedding analytics into services, and generating new revenue streams — while maintaining ethical boundaries and avoiding reputational risk.
5. Fraud Prevention in the Age of Real-Time Payments
   Adapting data models, integration pipelines, and anomaly detection capabilities to combat the speed and sophistication of financial fraud in instant transaction environments.

Agentic AI, AI systems that can plan, reason, and act autonomously, represent the next leap in financial technology innovation. But in a highly regulated, risk-sensitive industry, implementing such systems requires a careful blend of technical capability, governance, and business transformation. Source your plan of action by:

• Integrating AI with legacy infrastructure without disrupting mission-critical processes or data flows.
• Optimizing governance, risk, and accountability to ensure agentic AI decisions remain transparent, auditable, and aligned with regulatory expectations.
• Building human-AI collaboration models that preserve decision oversight while unlocking speed and efficiency.
• Ensuring talent and organizational readiness, including skills, structures, and cultural shifts needed to leverage autonomous agents effectively.
• Measuring ROI, evaluating both tangible business benefits and intangible gains, such as customer trust and employee productivity.

Optimize customer service and efficiency by operationalizing agentic AI responsibly to turn automation into a strategic advantage.

Many financial services organizations still treat data as a passive asset—something to be stored, governed, and reported on—rather than as an active product designed to deliver value. While the concept of data products is widely discussed, scaling them in practice introduces new challenges around ownership, pricing, accountability, operating models, and culture. Move data products from concept to enterprise capability with practical approaches to:

• Define what a data product looks like in financial services, including ownership models, lifecycle management, and accountability for quality and outcomes.
• Balance federation and centralization by designing operating models that empower domain teams while maintaining consistency, governance, and reuse.
• Measure ROI and drive adoption by aligning incentives, clarifying internal and external monetization strategies, and evolving roles from traditional analytics to dedicated data product management.

Understand how leading organizations are overcoming cultural resistance and structural constraints to treat data as a product—unlocking scalability, reuse, and measurable business value across the enterprise.

Data platforms are reaching a breaking point, unstructured data is exploding, generative AI is now a business imperative. Moving to a Lakehouse architecture that unifies structured analytics, AI model training, and event streaming under a single governance framework is essential to achieve long‑term scalability, agility, and high performance. Get practical strategies for:

• Modernize our data platform by migrating from on-premises legacy databases, warehouses and marts to a cloud Lakehouse architecture that reduces vendor lock‑in and increases flexibility.
• Design scalable batch and event‑driven ingestion pipelines capable of ingesting multiple data sources and formats reliably. 
• Govern at speed through automated lineage, masking, security, and compliance reporting across Data & AI workloads.
• Establish strong foundations for processing structured and unstructured data to enable advanced AI use cases.

Leverage traditional Machine Learning and Generative AI to extract deeper insights from your enterprise data.

Imagine an AI that can analyze every historical deal, performance trend, and reputational signal to challenge human assumptions and strengthen investment decisions. Harnessing and building trust around years of unstrcutured data is key te developing an agentic AI agent that can synthesize decades of deal data to deliver predictive, explainable intelligence to the investment committee. Leave witha. Guide on how to:

• Harness and structure unstructured data to fuel high-stakes investment decisions.
• Execute technologies enabling consumable, governed, and auditable data pipelines.
• Optimize AI architectures that are redefining governance, transparency, and accountability in institutional investing.

Deploy value adding AI and data-driven foundation for better, faster, and more confident investment decisions.