AI is coming to financial services. Let’s make sure we can trust it.

In collaboration with our development partners, we use real-world data to build trusted benchmarks for model performance in core financial services use cases. Every benchmark is designed around the KPIs business teams care about most: accuracy, cost per decision, and latency.

Not every decision should be fully automated, and not every decision requires a human to intervene. We pursue balanced research that helps teams unlock the benefits of AI in complex decision-making while preserving the value of human judgment and engagement.

Agentic systems based on LLMs are stochastic and hard to inspect, challenging assumptions behind SR 11-7 and traditional model risk management. We work with institutions and regulators to clarify what responsible adoption looks like in practice.

Foundation models are trained on public text, but financial decisioning runs on data with rich sequential and relational structure. We explore transformer-based architectures purpose-built for common data structures in financial services.

The most effective AI systems will be built using hybrid architectures. We help financial institutions navigate AI hype with clarity and choose the right tool for each task while balancing cost, risk, and performance.