Agenda Day 2

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

With operations across 175 countries, Euronet has faced some of the toughest challenges in operationalizing AI for financial inclusion, fraud prevention, and central bank digital currencies. Key to this challenge is distinguishing between traditional AI and GenAI — and why GenAI is less a technical breakthrough and more a powerful new user interface for business transformation. Master the success factors to:

• Integrate AI without replacing your entire core infrastructure, embedding it into existing processes and aligning it with both technology and people.
• Build AI models that “self-heal” through parametric feature engineering tailored to specific fraud patterns and evolving market dynamics.
• Operationalize AI in a governance-heavy environment, working with privacy officers to turn data governance from a compliance burden into a trust-building advantage.
• Leverage a globally distributed talent pool to design AI models that are internationally adaptable, supporting cross-border AML and financial crime prevention.
• Drive AI adoption in markets with unique constraints, balancing innovation with the realities of infrastructure, regulation, and cultural diversity.

Impact growth and inclusion with the power of AI to accelerate innovation, enhance accessibility, and drive measurable business results.

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.

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. 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.

4. 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.

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.

Review the key solutions and takeaways from the conference. Source a summary of action points to implement in your work.