Finance & Banking
Agentic AI for banking — continuous close, reconciliation, risk reporting and compliance automation built to satisfy both regulators and internal audit.
What makes banking hard for AI
Model risk management already has a framework
Banks have long-standing model governance. AI systems must enter it rather than run alongside it, which means documented validation, ongoing monitoring and independent review — not a separate innovation track.
The close is a hard deadline
Reporting timetables do not move. Anything in the critical path needs predictable latency and a fallback that a controller trusts.
Data lineage must be demonstrable
A number in a regulatory return has to be traceable to source. Systems that transform data without recording lineage create audit findings regardless of accuracy.
Where the value concentrates
- Continuous close. Reconciliation, accrual drafting and variance investigation running continuously rather than in a month-end surge.
- Regulatory reporting. Evidence assembly and consistency checking across returns.
- Credit and risk analysis. Document extraction and summarisation supporting, not replacing, the analyst.
- Client onboarding. KYC and periodic review at scale.
What the economics look like
Compressing the close is the most visible outcome, but the durable benefit is a reduction in manual reconciliation and the error rate that comes with it. Finance teams that move to a continuous rhythm generally find the close stops being an event at all.
How we build it
We build into your model risk framework from the start: validation evidence, monitoring, and lineage recorded as the system runs.
Related work
Starting smaller
Smaller institutions typically begin with reconciliation — bounded, high-volume, and with an unambiguous definition of correct.
Questions we get from banks
How does this fit our model risk framework?
It enters it. Treat the AI system as a model: documented validation, ongoing performance monitoring, independent review and a defined owner. The main addition is continuous evaluation, because unlike a traditional model the behaviour can change without a code release when a provider updates a version.
Can we trust an agent to post journal entries?
Agents should draft and evidence; posting authority follows your existing delegation rules. In practice the value is in the preparation and reconciliation work, not in removing the controller's approval.
How is lineage maintained?
Every transformation records source, rule applied and timestamp, so a figure in a return traces back to origin. This is not optional — untraceable numbers create audit findings independently of whether they are correct.
What is a realistic first outcome?
Reconciliation is the usual starting point: high volume, unambiguous correctness, and it shortens the close measurably within one or two cycles.