Your Regulator Will Not Accept a Policy Document for an Agent That Moved Money

AI governance for financial services means continuous assurance across payments, lending, and back-office AI, in one evidence trail that satisfies the FCA, SEC, PRA SS1/23, and the EU AI Act. Disseqt is the only unified AI assurance platform that tests, protects, and proves your AI.

See solutions for financial services

If you already know the workflow you need to govern, jump to the solutions in this vertical: chargeback decisioning, mortgage underwriting, payables and receivables agents, and multi-agent risk management for banks.

The problem for financial services AI

Banks did not start using AI. They started letting AI act.

An agent now reads a chargeback claim and decides whether to refund. A model scores a mortgage applicant and shapes the offer. Another agent reconciles invoices and releases payment. These are not recommendations a human signs off. They are decisions inside regulated processes, made at machine speed, thousands at a time.

That breaks the supervisory model financial services was built on. The PRA expects model risk management under SS1/23. The FCA holds firms to fair customer outcomes under the Consumer Duty. The SEC applies existing conduct and disclosure rules to AI-driven decisions. The EU AI Act classifies creditworthiness assessment as high-risk and binds it to Article 9 risk management and Article 72 post-market monitoring.

None of those regimes accept a slide deck as proof. They want evidence that the system was tested, that policy was enforced on live decisions, and that you can reconstruct what happened on any single transaction. A model reviewed at launch and again each quarter says nothing about what an autonomous agent did at 3am on one customer's account. Point-in-time review cannot govern a system that acts continuously.

The Disseqt answer, mapped to the three pillars

A financial services firm does not need six tools stitched together into something that resembles control. It needs one assurance layer that covers the full life of an AI decision. Disseqt is the only unified AI assurance platform covering testing, monitoring, policy, audit, and compliance in one place.

The three pillars are the AI Assurance Lifecycle, and a banking AI decision moves through all three.

Test and Detect

Before a payments or lending agent goes live, Test and Detect runs it against an adversarial envelope. Sixty-five ML-based validators across four families (base, RAG, agentic, MCP), 84 jailbreak techniques including single and multi-turn attacks, a Live Vulnerability Database that updates as new exploits appear, and cross-LLM benchmarking.

For a bank, that means finding the prompt injection that flips a chargeback decision, or the bias pattern that produces unfair denials, in a test harness rather than in a customer complaint. Find it in private, before someone finds it in public.

Protect and Enforce

Once live, Protect and Enforce holds the line on every decision in real time. Runtime guardrails on every output, policy enforcement on every agent decision, per-span input validation on the prompt path, drift detection, and explainability on why a decision was made.

This is the difference between governing an agent and Agentic Theatre, an agent that looks governed while quietly approving a refund it should have escalated. Banking AI governance has to live in the runtime path, not in a quarterly review.

Prove and Comply

Prove and Comply turns every test, block, and decision into evidence. Tamper-evident audit trails, compliance dashboards, and mapping to the EU AI Act (Article 9, Article 72, high-risk focus), the FCA, the SEC, and ISO/IEC 42001. Enterprise auditability is built in: SOC 2, SSO and SCIM, RBAC.

One evidence trail answers the PRA, the FCA, the SEC, and the EU AI Act, instead of four parallel compliance projects producing four inconsistent stories.

Why ML validators matter in financial services

A bank cannot afford assurance that samples one decision in a hundred. The decision it misses is the one the regulator asks about.

Disseqt validates with ML-based validators, not LLM-as-judge. That cuts the cost of validation to a level that makes continuous, real-time checking viable: around 99% less water, around 98% less CO2, and sub-50ms inline latency.

Sub-50ms matters because a payments or trading decision cannot wait half a second for a second model to grade the first one. Inline validation in under 50 milliseconds is what lets assurance run on every transaction, which is exactly what a regulator means by control.

Where this fits in the AI Assurance Lifecycle

Financial services sits at the centre of the AI Assurance category. This hub is one view of the wider discipline of AI governance, framed for banking and capital markets.

The work spans the full lifecycle: Test and Detect before launch, Protect and Enforce at runtime, and Prove and Comply for the evidence. It connects to broader AI risk management for risk and model-risk teams, and to AI compliance for the regulatory mapping. The brand-defining view of where this all sits in the stack is the assurance layer.

Solutions in this vertical

Disseqt covers the specific AI workflows financial services firms run today. Each has its own deep-dive page.

Credit card chargeback decisioning. Chargeback agents read disputes and decide refunds at volume, which exposes them to prompt injection and biased denials. See AI assurance for credit card chargeback.

Mortgage underwriting. Underwriting models shape credit offers, the textbook high-risk use case under the EU AI Act, where fairness evidence is not optional. See AI assurance for mortgage underwriting.

Payables and receivables agents. Back-office agents reconcile invoices and release payments, where a single wrong action moves real money. See AI assurance for payables and receivables agents.

Multi-agent risk management for banks. When a bank ships several agents that hand work to each other, the risk is in the seams between them. See AI risk management for banks shipping multiple agents.

Regulatory scope

This hub covers the regulators that bind financial services AI:

• EU AI Act. Creditworthiness and credit-scoring AI is high-risk, bound to Article 9 risk management and Article 72 post-market monitoring.

• FCA. The Consumer Duty and existing conduct rules apply to AI-driven customer outcomes.

• SEC. Existing conduct, disclosure, and supervision expectations extend to AI decisions in US capital markets.

• PRA SS1/23. Model risk management principles that AI models and agents now fall under.

• NIST AI RMF. The voluntary risk framework many US institutions adopt as their operating baseline.

• SOX. Where AI touches financial reporting and back-office controls.

Who this is for

This hub is for the people accountable when a bank's AI makes a decision.

Heads of AI governance and chief risk officers in tier-one banks and capital markets firms who own the answer when the board or the regulator asks who is watching the AI. Model risk and compliance leads under FCA, SEC, and PRA scrutiny who need evidence, not assurances. Engineering and AI platform teams in the FTSE 1000 and Fortune 500 shipping agents into payments, lending, and operations.

It is also for the global systems integrators and IT consulting partners standing up banking AI programmes that their clients will have to audit.