Data Analytics & AI for Financial Services Summit | Day 1:

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

The next challenge for financial services is not deploying another AI use case — it is building the enterprise architecture that allows AI, agents, and real-time decisioning to operate securely across the business. Explore how leading institutions can connect trusted data, reusable intelligence, and production-grade AI to transform customer, risk, and operational decisions. Walk away with a roadmap to:

• Build reusable data products and governed context layers that give AI and agents trusted access to customer, transaction, risk, and operational data.
• Engineer real-time, production-ready AI architecture by connecting modern data platforms, APIs, lineage, governance, observability, and human oversight.
• Scale intelligent decisioning across customer engagement, fraud and financial crime, underwriting, research, and operations — with clear controls and measurable value built in from day one.

Accelerate growth, productivity, and risk reduction by turning fragmented AI experiments into an enterprise intelligence layer built for financial services.

As financial institutions move from experimentation to widespread AI adoption, a new challenge is emerging: the economics of running AI at enterprise scale. Models, agents, GPUs, cloud consumption, and growing data workloads can rapidly increase technology costs without necessarily increasing business value. Discover how to engineer AI for both performance and economics. Source practical tips to:

• Match the right model, infrastructure, and deployment approach to the right financial services workload — rather than defaulting to the largest model or most expensive compute.
• Apply AI FinOps, workload optimization, and hybrid infrastructure strategies to control inference, compute, and data costs as AI usage scales.
• Measure the unit economics of AI across use cases to determine what to scale, optimize, redesign, or retire based on measurable business value.

Increase AI ROI by engineering cost, performance, and business value into every AI workload from day one.

The next wave of AI will not simply answer questions — it will initiate actions, coordinate tasks, and execute increasingly complex financial services workflows. As institutions move from copilots to autonomous and multi-agent systems, discover how to redesign processes, technology, and human roles around a new generation of digital workers. Develop a blueprint to:

• Orchestrate AI agents across complex, multi-step workflows — from customer servicing and operations to fraud investigation, research, and financial decisioning.
• Engineer seamless agent-to-agent and human-to-agent handoffs, connecting AI securely with enterprise applications, APIs, and core financial systems.
• Redesign workflows around intelligent execution, determining where agents should assist, act autonomously, escalate to humans, or coordinate with other agents.

Unlock productivity, speed, and entirely new operating models by moving from AI that assists your workforce to intelligent agents that execute alongside it.

• Explore the latest data analytics technology and strategies catered to financial services with our industry-leading sponsors.
• Share your challenges with the biggest innovators in the business.
• Schedule one-to-one private meetings for personalized advice.

Traditional governance was designed for relatively predictable data pipelines, users, and applications. AI changes that equation: Data is being discovered, combined, and consumed dynamically by models, agents, and automated workflows. Financial institutions need controls that can operate at the same speed. Discover how to:

• Shift from static policies and manual approvals toward machine-readable policies, automated controls, and continuous enforcement across data and AI environments.
• Activate metadata, classification, and policy intelligence to dynamically determine who — or what — can access data, for which purpose and under what conditions.
• Embed adaptive controls directly into data products, AI workflows, and enterprise platforms, so governance follows information wherever and however it is used.

Accelerate innovation and reduce regulatory risk by transforming governance from a manual checkpoint into an intelligent, always-on control layer.

The traditional data organization is being pulled apart. Data engineering, AI, analytics, governance, and business teams increasingly share responsibility for the same products and outcomes — while financial institutions still need clear accountability and enterprise control. Explore how to redesign the data function around value, products, and execution rather than organizational silos. Walk away with a roadmap to:

• Reengineer the central data function — deciding which capabilities should be centralized, embedded in business domains, delivered as shared platforms, or combined with AI teams.
• Establish product-based ownership that connects data engineers, governance, analytics, AI, and business leaders around persistent financial services products and measurable outcomes.
• Create new accountability and funding models that give domains greater execution power while preserving enterprise standards, interoperability, and regulatory control.

Accelerate innovation and business value by replacing fragmented data silos with an operating model built for products, AI, and enterprise-wide execution.

As banks move beyond GenAI experiments toward autonomous, agent-driven use cases, one challenge keeps showing up: trust. AI agents cannot personalize experiences, detect fraud, or act autonomously if data is stale, fragmented, or poorly governed. Build a trusted data foundation for Agentic AI by:

• Delivering live, real-time data access across core banking, ERP, and fintech systems, without replication
• Establishing a shared semantic layer so humans and AI agents reason from the same business context
• Embedding governance and security by design, allowing AI to act safely within business and regulatory guardrails
• Turning trusted data into outcomes, including faster insights, reduced fraud risk, and quicker rollout of AI initiatives

Attend this session to see how banks are moving from AI ambition to trusted, production-ready Agentic AI, and why the data layer is now the deciding factor.

Traditional lineage can tell you where data came from. AI requires financial institutions to reconstruct an entire decision chain — what data was retrieved, which model was used, what context it received, how an agent reasoned through a workflow, and what action ultimately followed. Discover how to:

• Extend lineage across data, models, prompts, retrieved context, agents, tools, outputs, and downstream actions to create an end-to-end record of AI execution.
• Capture the evidence behind AI-generated decisions through model provenance, versioning, observability, and immutable audit trails that allow teams to reconstruct what happened and why.
• Operationalize AI traceability across complex production environments so risk, compliance, and technology teams can investigate decisions without introducing manual bottlenecks.

Reduce regulatory and operational risk by making every material AI decision and action traceable, reproducible, and auditable.

Financial institutions have spent years building predictive models, but an accurate prediction does not automatically produce a better business decision. The next opportunity is to combine predictive analytics, optimization, and decision science to determine what action should follow. Take away practical strategies to:

• Combine predictive models with business rules, optimization, and decision science to improve pricing, credit, retention, portfolio, and risk decisions.
• Move beyond model accuracy by measuring whether analytics actually change decisions and improve financial outcomes.
• Embed analytical models directly into business workflows, so predictions trigger consistent actions, interventions, and next-best decisions.

Increase profitability, reduce risk, and improve customer outcomes by turning advanced analytics into better financial decisions at scale.

AI literacy is rapidly moving beyond knowing how to prompt a copilot. As AI becomes embedded into everyday financial services workflows, employees need to know how to collaborate with intelligent systems, challenge their outputs, and continuously adapt how work gets done. Explore how to:

• Define the AI capabilities required across different roles — from confidently interrogating AI outputs to knowing when to delegate, challenge, escalate, or apply human judgement.
• Embed AI learning directly into workflows through role-based training, experimentation, champions, and continuous learning rather than relying on one-off enterprise training programs.
• Measure whether AI enablement is actually changing behaviour and performance through adoption, workflow redesign, productivity gains, and improved employee outcomes.

Increase workforce productivity and accelerate AI adoption by turning AI literacy from a training exercise into an enterprise capability.

Review the key solutions and takeaways from today’s sessions. Source a summary of action points to implement in your work. Discuss tomorrow’s highlights!