Your AI agents can already reconcile accounts, triage AML alerts, and prepare payments. What’s missing is the control layer that lets risk and compliance say yes. Relevant Software builds that layer for fintechs and financial institutions.
Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027, and inadequate risk controls are one of the reasons. In financial operations, these are the gaps that stop agents most often:
We design the governance framework with your risk and compliance teams before the build: who owns each agent, which controls apply, and what evidence they need to approve it.
Every prompt, tool call, decision, and approval is traced and logged, so you can replay any action an auditor asks about.
Each agent gets a scoped set of permissions, amount limits, and approval thresholds. The agent that prepares a payment can never release it.
Every agent sits in one registry with owners, versions, health metrics, and a kill switch that stops any agent in seconds.
The platform and the models run in your data center or private cloud, with your keys.
Our AI compliance services map controls to EU AI Act, DORA, and model risk requirements, and the platform produces the documentation your supervisors expect.
Only approved models run, every release is evaluated first, and drift monitoring flags a falling accuracy rate before it reaches a ledger.
Agents act on your ERP, core banking, and payment systems through one governed gateway, inside the approval workflows your teams already use.
Every platform starts from the agents you already run or plan to launch, so each step below is shaped by your workflows and your regulator.
We map every agent, the financial process it touches (AP, reconciliation, payments, AML), the data it reads, and the actions it can take.
Each agent is tiered by financial impact and regulatory exposure. Higher tiers get stricter limits, more sign-offs, and deeper logging.
Policies become code: which systems each agent can access, amount limits, counterparties, and maker-checker rules.
Every action is traced in tamper-evident logs, with evidence exports ready for internal audit and supervisors.
Agents connect to your financial systems through a governed gateway. Then we run them in shadow mode on real cases and red-team them against prompt injection and limit bypass.
The registry, kill switch, runbooks, and dashboards transfer to your risk and IT teams, with clear owners for every agent.
We’ve built banking, wealthtech, payroll, and billing software, so we know the fintech processes your agents will touch.
Guardrails, LLM observability, AI compliance, and AI audit are delivered by one team.
ISO 27001 certified and GDPR compliant. Access controls and audit trails are built to standards your reviewers already accept.
Agent architecture is designed by Claude-certified architects, engineers certified on the models they build with.
The people designing your controls have shipped regulated systems before.
The team that builds your platform stays for the agents that follow.
We map the controls it needs, build them into your systems, and prove them on real cases before the agent goes live.
Model risk management was built for models that score or predict. An analyst still acts on the result. AI agents act themselves: they call APIs, update records, and prepare payments. That adds three things traditional MRM doesn’t cover:
The platform doesn’t replace your MRM. It extends it to agents, so your existing validation and approval processes stay the same and get new evidence to work with.
Buy when your agents live inside one vendor’s ecosystem and its built-in controls satisfy your risk team. Build, or extend a tool with a custom layer, when:
Most financial institutions end up in the middle: an off-the-shelf tool for model inventory and documentation, plus a custom layer for permissions, approvals, and integration with financial systems. We start with an audit of what you already run, so you only build what’s missing.
The same way you enforce it for people: no single actor can both prepare and approve a financial action. In practice:
We use this pattern in our multi-agent systems development work for regulated processes.
It depends on what the agents decide. Creditworthiness assessment and credit scoring are listed as high-risk, and after the Digital Omnibus, those obligations apply from 2 December 2027. Many operational agents, such as reconciliation, invoice matching, or AML alert preparation for a human investigator, fall outside that list. DORA still applies to them as ICT systems, though.
Either way, building with documentation, logging, and human oversight from the start means the platform holds up whichever classification applies. Your legal team should confirm the classification. Our AI compliance services cover the obligations beyond engineering.
Yes, and it’s one of the main reasons to have a platform at all. Agents register in a single registry regardless of how they were built, whether in-house, by a vendor, or on different models. Every agent then acts through the same governed gateway, so permissions, logging, and approvals work the same way for all of them.
We’ve connected AI systems to existing banking infrastructure before. In the AI platform we built for a European bank, data pipelines process millions of records a day with GDPR- and PSD2-compliant monitoring. The same integration approach applies when the systems are agents.
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