*IN-PERSON* What Every Risk Leader Needs to Know About AI Decisions

You don't need to understand how AI models work—you need to know how to govern them. This executive session is for risk, compliance, and business leaders who are being asked to approve AI use cases but don't have a framework for what "good" looks like. We'll skip the technical jargon and focus on what executives actually need: the right questions to ask, the governance structures that work, and how to turn robust AI controls into a competitive advantage.

Register here: https://luma.com/5jy0g76m?utm_source=MU

Position AI as regulated infrastructure, not a shiny feature. This session is designed for leaders who own customer trust, regulatory relationships, and P&L in credit and fraud-heavy businesses.

What You'll Be Able to Do:

• Ask your data science team the 5 questions that separate robust AI from regulatory liability
• Explain to your board why you approved (or rejected) an AI use case—in plain English
• Recognize when a vendor's "explainable AI" claims are real vs. marketing fluff
• Build governance structures that don't become bottlenecks as AI use cases scale

Walk away with practical frameworks to approve use cases and hold technology teams accountable, transforming AI governance from compliance burden into sales advantage.

This Session Is NOT:

• A deep dive into machine learning algorithms
• A tutorial on building AI models
• Vendor demos or sales pitches

This Session IS:

• A practical guide for business leaders who need to govern AI without becoming data scientists
• Real scenarios from credit, fraud, and AML—and how executives responded
• Frameworks you can use Monday morning to evaluate your team's AI proposals

Who Should Attend:

• Risk and compliance leaders tired of sitting in AI meetings they don't understand
• Executives who need to approve AI use cases but don't know what questions to ask
• Business leaders who own customer trust and regulatory relationships—not the tech stack
• Anyone who's been told "don't worry, the model handles it" and thought "that's not good enough"

Specific roles include: Chief Risk Officers, Compliance Leaders, VP/SVP Credit Risk & Fraud Operations, General Counsel, Regulatory Affairs, Product Leaders at Lending and Fraud Prevention Vendors

No technical background required. If you can read a P&L, you can govern AI.

Core Topics:

• Risk Landscape in AI-Powered LOS & Fraud - How AI changes fraud detection and credit assessment—and where false positives become business landmines. Scenario work: reconstructing AI decisions for regulators
• Governance Models That Work - Compare centralized vs. hybrid structures. Define decision rights, responsible AI roles, and steering committees
• Explainability & Audit Trails - Why black-box models are liabilities. What complete audit trails look like: data sources, thresholds, overrides, and human-in-the-loop intervention
• Continuous Monitoring & Incident Response - Leadership playbook for drift, bias detection, and what to do when AI fails—including client communication and regulatory reporting
• From Risk to Differentiator - How demonstrable governance becomes part of your sales narrative with conservative clients

You'll Leave With:

• AI Risk & Compliance Charter template (roles, forums, decision thresholds)
• One-page AI model risk scorecard for pre-launch review
• Customer/regulator messaging on explainable, resilient AI systems

Featured Speaker:
Rahul Garg - Engineering Manager & AI Architect at Celestial Systems. With deep expertise in building production AI systems for regulated industries, Rahul specializes in translating complex AI capabilities into governance frameworks that business leaders can actually use—focusing on explainability, audit trails, and operational resilience in high-stakes environments. His work bridges the gap between what AI systems can do and what risk leaders need to approve, monitor, and defend those systems to regulators and customers.
Space is limited to ensure meaningful conversation and personalized guidance.

Reserve your spot: https://luma.com/5jy0g76m?utm_source=MU