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

As data pipelines grow more distributed and dynamic, trust in data has never been more critical — or more difficult to maintain. Whether you’re building AI models, powering analytics, or reporting to regulators, bad data erodes business value and confidence. Automate data quality monitoring with actionable insights. Create a roadmap to:

• Implement end-to-end data observability to automatically detect and resolve issues in freshness, volume, schema, and lineage across your pipelines.
• Define and enforce data SLAs while integrating quality rules directly into governance workflows to align business and technical standards.
• Unify monitoring and alerting across data, infrastructure, and applications to quickly trace and resolve anomalies at the system level.
• Boost trust through user-driven metadata enrichment, trust signals, and collaboration features that help bridge technical and business teams.

Heighten confidence in your organization’s data by implementing robust observability and governance practices.

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.

As AI adoption accelerates across fraud detection, credit scoring, and customer personalization, financial institutions face mounting pressure to ensure models are transparent, trustworthy, and regulator-ready. Yet many AI systems still operate as opaque “black boxes,” leaving business leaders, auditors, and customers uncertain about how decisions are made and putting adoption at risk. Create a roadmap to:

• Drive trust and transparency by demystifying model decisions, using techniques for translating complex algorithms into clear, human-understandable reasoning.
• Balancing performance and interpretability by selecting the right modeling approach for regulatory, operational, and customer needs.
• Optimize regulatory alignment by harmonizing explainability requirements under evolving AI and data privacy regulations in multiple jurisdictions.
• Embed explainability in workflows by integrating interpretability checks into model development, deployment, and monitoring cycles.
• Drive adoption by using explainability to meet compliance and identify new business opportunities and refine customer experiences.

Master AI innovation by turning explainability from a compliance burden into a competitive advantage.

Many organizations are finding their data platform grinding under the weight of increasing regulatory requirements, growing unstructured datasets, and the sudden demand for generative AI pilots. Legacy warehouses cannot keep pace with streaming data from trading systems, fraud monitoring tools, and mobile banking channels. Move to a hybrid lakehouse architecture for structured workloads, AI model training, and event streaming — all under a single governance framework. Take back to your office strategies to:

• Modernize the core by migrating from a single on-premises warehouse to a cloud-native, hybrid lakehouse.
• Enable real-time AI to feed fraud detection and credit risk scoring models with second-by-second transaction data.
• Govern at speed through automated lineage, masking, and compliance reporting across AI workloads.
• Engineer for resilience including building multi-cloud failover between providers to meet strict uptime and regulatory standards.

Adapt your data infrastructure to enable secure, real-time AI, resilient operations, and compliance at scale.

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.