Listen as Maveric Systems explores how shadow tools, agentic workflows, and embedded AI can create consequential decisions outside traditional model-risk governance.

Explore why vendor benchmarks and documentation are insufficient for banking AI assurance, and how institution-specific testing, contractual controls, and ongoing monitoring can strengthen third-party risk management.

Discover why agentic AI governance requires decision-chain accountability, traceable actions, clear authority boundaries, and stronger controls over autonomous banking workflows.

Explore what SR 26-2 means for banking model risk management, where generative and agentic AI create additional governance challenges, and why system-level controls matter.

Explore why isolated AI projects struggle to scale and how a shared architecture built around data, model, system, and outcome trust can support sustainable banking AI adoption.

Understand why reliable datasets alone cannot guarantee trustworthy AI decisions, and how query-level validation strengthens evidence quality, policy compliance, and decision assurance in core banking.

Explore how AI-enabled core banking is evolving beyond transaction processing toward contextual intelligence, governed inference, and more informed banking decisions.

Discover how regional banks can prioritize AI investments, balance early business value with governance, and build reusable capabilities that support responsible AI adoption.