What to look for in an AI context platform for banks

When a bank evaluates AI context platforms, the deciding questions are usually these:

• Who approves definitions?
• Does it reach the systems where bank figures actually originate, not just the warehouse?
• Can every figure be traced to its owner, its approval history and its source in a way that holds up in an audit or exam?

Many platforms are built for data teams, so a bank should also ask how much of the work finance can run itself.

These nine questions separate platforms built for finance from platforms built for data teams.

Nine questions to ask

1. Who approves a definition? Look for a named business owner who signs off, not only a data team or an automated suggestion.
2. What systems does it reach? Ask which systems it connects to today, by name: core, GL, ERP, planning tools, spreadsheets. Also ask who maintains each connection.
3. Where does data go? Ask exactly what is copied, cached or processed outside your environment. Any movement of regulated data adds risk and review time.
4. Is there an approval history? Every definition change should record who made it, when and why.
5. Can it trace a figure end to end? Pick a figure from a regulatory report and ask the vendor to trace it live.
6. Does it handle mergers? Ask how it maps two banks' definitions after an acquisition, and ask for an example.
7. Which AI tools can use it, and how? Ask which assistants it supports today and through what interface.
8. Does it know banking? Ask about CECL inputs, core conversions and risk data aggregation. BCBS 239 is formally required only of the largest banks, but it is a useful benchmark for any institution.
9. Will it pass your vendor review? Ask for security documentation, deployment options and how it respects existing access permissions.

Red flags

1. Definitions only data engineers can read.
2. Figures that depend on reports that already disagree with each other.
3. No answer to "who approved this number?"
4. A demo that only works on clean warehouse data.

How Preql approaches this

Preql is designed around several of these questions. Business owners approve the rules that matter, definitions are versioned with audit trails, and Preql works with your existing systems in the cloud or within your environment. Book a demo and see how Preql answers all nine on your own data.

FAQ

Should we run a proof of concept? Yes. Use your own data and trace three regulatory figures end to end.

How long should an evaluation take? Long enough to cover one full reporting cycle for the figures you're testing. If a call report line is included, that's a quarter.