Let agents work in regulated systems. Keep the authority to stop them.

Kontext helps banks, fintechs, and insurers put AI agents behind runtime authorization: policy at the action boundary, scoped credentials, approvals for money movement, and an audit trail regulators can read.

Kontext for Financial Services. Banks, fintechs, and insurers are putting AI agents next to payment rails, ledgers, and customer records. Kontext decides what those agents may actually do at the moment they try to do it, and leaves a record you can hand to an auditor.

Why finance is different

In a bank, a wrong agent action is not a wrong answer. It is a real payment, a real limit change, or a real export of customer records.

Maker-checker assumes two humans. An agent acting on a developer's or analyst's delegated authority collapses separation of duties into a single step.
An agent calling a third-party API is ICT usage, but it rarely appears in the third-party register that DORA expects you to maintain.
Long-lived keys in .env files, notebooks, and agent config are an audit finding on their own, before anything goes wrong.

What Kontext gives financial institutions

Decide before the money moves: evaluate the user, agent, session, tool, resource, and parameters before a call reaches a payment rail, ledger, or core banking system.
Keep a human in the loop where it counts: require approval for the narrow set of actions that change balances, limits, or customer records, and let everything else run without friction.
Broker credentials per task: keep API keys and OAuth tokens out of the agent runtime and release short-lived scoped credentials only after policy passes.
Produce evidence, not anecdotes: record who acted, which agent ran, what was requested, which policy matched, and why it was allowed, denied, narrowed, or escalated.

Top risks in finance

These are action risks, not output risks. The question is not whether the model said something wrong — it is what the agent was allowed to do next.

Unauthorized money movement: an agent initiates, modifies, or approves a transfer beyond the mandate its user actually has.
Bulk data extraction: a reporting or support agent pulls account and transaction records far past the task it was given.
Credential sprawl: long-lived keys to core banking, payment, or ledger APIs sit in agent config, notebooks, shell history, and logs.
Collapsed separation of duties: one agent session both prepares and approves a change, with no second party in the path.
Unregistered third-party reach: an agent calls an ICT provider that never entered the register, or touches an in-scope system outside a change window.
Untraceable action: something changed in a production system and nothing records which person, agent, or policy stood behind it.

How we help

Risk owns the policy; enforcement happens at the tool call. Second line writes the boundary once, and it is applied wherever agents actually run: developer machines, internal copilots, MCP tools, provider APIs, and the SaaS systems operations teams already use.

Choose policies: start from common boundaries for money movement, customer data, and production systems, or write your own. Every policy version is immutable and attributable.
Observe: run in observe mode. Kontext records what it would allow, ask about, or deny on every tool call, so you see real agent behavior without changing what runs today.
Enforce: switch to enforce when the evidence supports it. Kontext verifies the actor, evaluates the action against policy, assesses runtime risk, then allows, denies, narrows, asks for approval, or issues a scoped credential before the external system is touched.

Good first policies

Require human approval for anything that moves money, changes a limit, or writes to the ledger.
Deny agent reads of production customer records; allow aggregate queries instead.
Block bulk export of transaction and account data, whatever tool asks for it.
Issue short-lived scoped credentials per task rather than leaving keys in the agent environment.
Escalate tool calls that touch in-scope systems outside an approved change window.

Evidence

Most agent programs can describe their controls but cannot evidence them per action. A decision record closes that gap for questions control owners already get: which third-party systems agents reached and under whose authority (DORA ICT and third-party register), whether a sequence of actions can be reconstructed (MiFID II record-keeping and equivalents), whether least privilege is enforced for non-human actors (EBA ICT and security risk guidelines), where AI controls are evidenced (NIST AI RMF), and which agents can export customer data (SOC 2 and ISO 27001 control catalogs).

Kontext produces the decision record these controls depend on. It does not certify compliance, and nothing here is legal advice.

Get started

Start by seeing what agents already do in your environment, then move money movement, customer data, and production systems behind runtime authorization. Talk to sales or read the deployment docs.