AI agents for financial services.
Every automated decision in financial services has to be defensible long after it was made. iLeaf builds agents where the reasoning is recorded alongside the result, business policy stays explicit rather than absorbed into a model, and anything adverse routes to a person with the evidence already assembled.
What this looks like in financial services
- Loan origination support
- Document collection, eligibility pre-checks and applicant follow-up, with decisions left to your policy engine.
- Fraud triage
- Anomalies surfaced with the signals that triggered them, flagged for review rather than silently declined.
- Collections outreach
- Voice and message follow-up at volume, escalating to a person on any sign of hardship or dispute.
- Contract and obligation queries
- Graph-RAG over agreements so terms and renewal dates are answered with a citation to the clause.
Questions we get asked
How do we prove a decision to a regulator?
Every run records inputs, model and prompt versions, reasoning, cost and outcome, retained so any decision can be reconstructed. Business rules are kept explicit and separate from the model, so policy can be shown directly rather than inferred from behaviour.
Would you let an agent decline an application?
Not unattended. Automation handles the clear-approve path and information gathering; adverse outcomes route to a person. That keeps accountability with an identifiable human, which is what regulators and insurers generally require.
AI & Agents for financial services— let’s talk.
Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.