Responsible AI
AI that touches a business’s money must be held to a higher standard than AI that drafts emails. Fintra’s position is simple: AI proposes, humans approve, everything is attributable, and we are honest about what the system cannot do. These principles are enforced by AgentFence at runtime - they are architecture, not aspiration.
Humans stay in the loop where it counts
For consequential actions - posting entries, moving money, changing payroll, filing anything - Fintra’s agents prepare and a human approves. Approval gates are enforced by AgentFence policy at the tool-call layer, so there is no code path where a model output becomes an irreversible financial action without a person signing off. Low-stakes assistance (drafts, summaries, anomaly flags) flows freely, because responsibility means calibrating oversight to risk, not slowing everything equally.
Explainability and attribution
A recommendation you can’t interrogate is a liability in finance.
- Agent suggestions ship with reasoning: why this categorization, which pattern triggered this anomaly flag.
- Every output is attributed - which agent, which model version, which data it acted on - via the trust ledger.
- Approvers see what they’re approving in full before they approve it, with diffs for financial mutations.
- Confidence is surfaced honestly: low-confidence outputs are labeled as such rather than presented with false certainty.
Testing for failure, not just success
Our model evaluations (see AI model governance) test more than accuracy: refusal behavior on out-of-policy requests, robustness to prompt injection in agent contexts, and consistency across customer segments so quality doesn’t silently vary by business type or size. Failure modes we find become policy rules in AgentFence and regression tests in the evaluation suite - every incident makes the gate stricter.
Honest limits
Fintra’s AI is an accelerant for finance work, not a replacement for professional judgment. It does not provide licensed tax, legal, or investment advice; it prepares work for human review, and we say so in the product at the moment of use - not just in terms of service. Where the system is uncertain, it is designed to escalate to a human rather than guess confidently.
Frequently asked questions
Will AI ever take a financial action I didn’t approve?
No. Consequential actions are gated by AgentFence policy at the tool-call layer - the agent literally cannot execute them without a recorded human approval. This is enforced in the runtime, not by convention.
Can I see why the AI made a recommendation?
Yes. Suggestions include their reasoning and triggering data, every output is attributed to a specific agent and model version in the trust ledger, and approvers see full diffs before approving.
How do you prevent AI errors from hitting my books?
Layered controls: human approval on all financial mutations, confidence labeling, finance-specific evaluation suites on every model change, and an append-only audit trail that makes any error traceable and correctable rather than silent.
Does Fintra’s AI give tax or legal advice?
No. Fintra prepares and accelerates work for human review and flags items worth professional attention, but it is not a licensed advisor and the product says so where it matters - at the point of use.
Questions about responsible ai?
Our security team answers due-diligence questions directly - documentation, DPAs, and evidence available on request.
Talk to our security team