Financial services AI with supervisory clarity
Markets and banking move fast; oversight does too. We focus on decision support, controls around customer-facing outputs, and third-party concentration.
Where AI lands first
Research synthesis, operations copilots, compliance monitoring, and developer productivity — each with different model risk and data-handling rules. Segmentation keeps your inventory honest.
Third parties and model supply chain
Cloud APIs, data vendors, and packaged copilots need explicit subprocessor governance, exit plans, and testing when versions change. Map these to your governance cadence.
Industry journeys has additional sector texture; ← hub
Frequently asked questions
How do we handle market data and licensing with LLMs?
Treat prompts and outputs like any other derived work: verify vendor terms, prevent leakage into training where prohibited, and log which datasets informed each answer.
What about customer-facing generative features?
Use conservative thresholds, disclosure where required, rapid kill switches, and human review paths for material advice or transactions.
Can small teams still meet model risk expectations?
Yes, by right-sizing documentation to materiality, automating evidence collection, and partnering for independent challenge where required — not by skipping controls.
Next step: a fixed-fee diagnostic.
Three weeks. Board-ready brief. Ranked opportunities. No discovery theatre.
Book a diagnostic →