Banks and insurers face the same regulatory pressure, but they rarely agree on how to fund it, staff it or reuse the data it produces. The host reads a two-sided choice, the room moves to one side, and each person makes their case to whoever is standing beside them. By the end, the room can see which approaches are becoming standard and where institutions are still divided.

• Compare your regulatory data approach with sell-side and insurance peers
• See which regulatory data investments peers are reusing across the business
• Meet peers dealing with the same examiners and deadlines

Financial crime teams are weighing where rules-based models still outperform LLMs and where LLMs unlock new efficiency. This case study shows how one institution combined both to reduce alert volumes and speed investigations.

• Automate triage to reduce false positives in customer screening
• Apply LLMs to accelerate adverse media reviews and case handling
• Govern rules-based and LLM models under one control framework

Cyber threats, outages and large migrations all put critical data at risk, and regulators increasingly expect firms to prove they can recover quickly. Panelists share how they modernize their platforms while protecting the continuity of business-critical data and analytics.

• Decide which data and analytics workloads belong in the cloud
• Design recovery and continuity plans for business-critical data
• Meet regulatory resilience expectations while modernizing

Asset managers have spent years using data to manage risk, and they’re now turning the same data and AI toward growing assets, winning investors and reaching the right advisors, often with analytics teams that haven’t grown. The host reads a two-sided choice, the room moves to one side, and each person makes their case to whoever is standing beside them. By the end, the room can see where buy-side peers are betting on data for growth and how lean teams are making AI do more of the work.

• Compare how peers use data and AI to support growth in assets under management, not just risk management
• See how lean analytics teams use AI to scale without adding headcount
• Leave with contacts who made the opposite call to you

Investment firms often run fragmented platforms that were never designed to work together or to serve AI. This case study shows how one investment data architecture team is consolidating onto a modern platform while keeping governance and metadata intact.

• Sequence platform migration to limit disruption to investment operations
• Carry metadata, lineage and governance across platforms
• Reduce platform and duplicated pipeline costs

As AI models become commodities available to every firm, the lasting advantage may be the data they run on. Yet most asset managers rely on the same licensed feeds as their competitors, often under contracts written before AI. Panelists debate where real advantage still comes from, and how to protect it.

• Identify which proprietary data (client, operational, historical) creates advantage competitors can't buy
• Confirm what licensed vendor data you may legally use to train or ground AI, and renegotiate before deployment
• Invest in the quality, lineage and ownership that turn in-house data into a defensible asset

Andrew Swinney

Chief Marketing and Data Officer
Leader Bank

See Bio

Milind Joshi

Chief Technology Officer
Foxquilt

See Bio

Years of investment in governance and quality have produced trusted data assets that many institutions still treat only as a compliance cost. Leading firms are now packaging that data into internal and client-facing products, partner data sharing and new services. Panelists share how they identify, price and govern data that creates revenue.

• Identify governed data assets with internal and external commercial value
• Build the governance, entitlement and privacy controls that make data sharing safe
• Measure the revenue and cost impact of data products to secure further investment

Patty Aenchbacher

Director, Enterprise Data Governance
American Century Investments

See Bio

As financial institutions accelerate AI adoption and become increasingly dependent on interconnected data, technology, and third-party ecosystems, risks can no longer be managed effectively in isolation. Data protection, privacy, cybersecurity, technology controls, third-party dependencies, AI governance, and regulatory compliance are deeply interconnected, with implications extending beyond compliance obligations to business resilience, financial exposure, and strategic decision-making.

Drawing on more than 20 years of experience spanning software engineering, enterprise data and AI, cybersecurity and technology risk, privacy, third-party risk management, enterprise risk management, investment, and capital management, this session explores an integrated approach to understanding and managing risk across the enterprise. It examines how organizations can establish effective governance, controls, and oversight while balancing regulatory requirements, data protection, privacy, business priorities, financial considerations, and innovation.

• Connect the risk landscape and make risk-informed business decisions: Assess data, privacy, cybersecurity, technology, third-party, AI, regulatory, and business risks collectively rather than through disconnected risk management processes.
• Strengthen third-party risk management: Assess third-party risks holistically, identify critical dependencies and concentration risks, and determine appropriate oversight, mitigation strategies, and risk treatment.
• Navigate the opportunities and risks of Agentic AI: Evaluate the business opportunities and emerging risks of Agentic AI, including data privacy, security, autonomy, accountability, and regulatory considerations, and establish appropriate governance, controls, and oversight to enable responsible adoption.

Too many institutions discover data risk issues only when auditors or examiners find them. This session shows how first-line teams can continuously monitor their data controls, so issues surface and get fixed early and evidence for audits and exams is ready at any time.

• Identify the data controls and risk indicators worth monitoring continuously
• Automate evidence collection so audit and exam requests no longer trigger a scramble
• Escalate and fix control failures in the first line before they become findings

Cindy Tu

Vice President, Head of Tech & Cyber Governance
LPL Financial

See Bio

(Choose two – 30 minutes each)

Pick two topics and join a small group of peers for an off-the-record discussion led by a financial services practitioner. Each table focuses on one problem, and you switch tables after 30 minutes so you can cover two priorities in one session.

• Compare how peer institutions are tackling the same challenge
• Leave with practical approaches to take back to your team
• Build relationships with peers working on the same priorities

#1 Save Hours on Every Audit Cycle: How Internal Audit Teams Are Governing and Building With AI

Chloe Cheng, Executive Director, Head of Data & AI, Internal Audit, Mizuho Americas

#2 Get It Funded: Build the Business Case That Wins Budget for Data and AI Programs

Mo Bayan, Director, Business Intelligence & Data Enablement, Sun Life

#3 Source It Right: Enforce Authoritative Data Sources So AI Outputs And Regulatory Reports Can Be Trusted

Juan Santos, SVP, Data Governance & Management Enablement Director, KeyBank

#4 Ship Faster With AI Coding Tools: Separate What Actually Works From What Executives Expect

Kevin Hickey, Head of Enterprise Architecture, Voya Financial

#5 Shine A Light On Shadow AI: See Which Agents Use Which Data And Bring Them Under Control Without Slowing Adoption

Juan Santos

SVP, Data Governance & Management Enablement Director
Keybank

See Bio

Data programs often stall chasing the perfect model, platform or framework while the business waits. This session makes the case for pragmatism: delivering usable, well-governed data in small steps, choosing good-enough standards over perfect ones, and knowing when to stop polishing and ship.

• Focus data work on the decisions and processes the business needs to improve now
• Deliver in small, governed increments instead of multi-year big-bang programs
• Know when good enough is good enough, and where standards must not slip

Eric Gonzalez

CEO
Omnificity

See Bio