Financial Services
We attack those systems the way an adversary would — through the endpoint they already expose — and measure how they behave across eight dimensions. They are also classified high-risk under the EU AI Act, so the same evidence answers Articles 9–15 by August 2026.
We map every AI system to EU AI Act requirements using 8 scientifically validated dimensions, with a registered, hash-citable audit trail built for the scrutiny financial regulators demand.
Financial institutions don't face one AI regulation — they face several, enforced by multiple authorities simultaneously.
The EU AI Act classifies credit scoring and insurance pricing AI as high-risk, requiring conformity assessment, ongoing post-market oversight, and registered record-keeping. ESMA has issued specific guidance for AI in investment services. DORA mandates operational resilience testing for all ICT systems, including AI. And GDPR's data protection requirements apply to every AI system processing personal data.
These regulations don't replace each other — they stack. A single non-compliant AI system can trigger enforcement under the AI Act, GDPR, DORA, and sector-specific regulations at the same time.
Aug 2, 2026
EU AI Act high-risk system compliance deadline
Up to 7%
of global turnover — maximum AI Act fine
8 dimensions
of behaviour measured, each traceable to the run that produced it
4 regulations
AI Act + GDPR + DORA + sector rules apply simultaneously
Art. 9–15
what a high-risk system must demonstrate, with evidence
High-Risk Classification
The EU AI Act explicitly names these financial services AI applications as high-risk, requiring full compliance with Articles 9–15. If your organization deploys any of these, the August 2026 deadline applies to you.
HIGH-RISK — EU AI Act Annex III
AI systems that evaluate creditworthiness or establish credit scores of natural persons are explicitly classified as high-risk.
What regulators expect
Recent: $2.5M settlement (Earnest Operations, 2025) for AI lending discrimination — failure to test models for disparate impact.
HIGH-RISK — EU AI Act Annex III
AI systems used for risk assessment and pricing in life and health insurance are classified as high-risk.
What regulators expect
Regulatory oversight: EIOPA will enforce AI Act compliance for insurers.
ESMA SPECIFIC REQUIREMENTS
ESMA has issued guidance requiring firms using AI in investment services to implement comprehensive testing and monitoring, with rigor proportional to risk.
What regulators expect
Regulatory oversight: ESMA and national securities authorities.
AI ACT + DORA + AML DIRECTIVES
While partially exempt from high-risk classification, AML/fraud AI falls under DORA's ICT resilience requirements and must demonstrate operational robustness.
What regulators expect
Recent: $59M FCA fine (Dec 2025) for transaction monitoring failures at a UK building society.
We evaluate every AI system across 8 scientifically validated dimensions — each mapped to the specific regulatory requirements financial institutions face.
Regulation
AI Act Art. 10 — Data Governance & Fairness
Dimension
1. Fairness & Non-Discrimination
Evidence
Demographic parity ratios, bias disparity indices, disparate impact testing — the evidence a $2.5M settlement could have prevented
Regulation
AI Act Art. 9 — Risk Management
Dimension
8. Accountability & Human Oversight
Evidence
Occurrence rates over N runs across your AI portfolio, documented mitigation actions, ownership per finding
Regulation
AI Act Art. 13 — Transparency
Dimension
3. Transparency & Explainability
Evidence
Explainability coverage for every AI decision, human-readable justifications, full traceability
Regulation
AI Act Art. 15 — Accuracy & Robustness
Dimension
5. Factuality + 6. Robustness
Evidence
Critical error rates, adversarial resilience testing, per-run performance findings with a mandatory coverage report
Regulation
AI Act Art. 14 — Human Oversight
Dimension
8. Accountability & Human Oversight
Evidence
Intervention rates, override logs, escalation records for low-confidence decisions
Regulation
AI Act Art. 11-12 — Documentation & Records
Dimension
7. Security & Access Control
Evidence
Cryptographically signed records, 7+ year retention, automated evidence packs for auditors
Regulation
DORA — Operational Resilience
Dimension
6. Robustness & Adversarial Resilience
Evidence
Adversarial attack resistance, prompt injection testing, registered system-integrity findings
Regulation
GDPR — Data Protection
Dimension
4. Privacy & Data Protection
Evidence
PII exposure detection, data minimization flags, cross-regulation privacy evidence
Regulation
ESMA — Investment Suitability
Dimension
1. Fairness + 3. Transparency
Evidence
Suitability verification, fair treatment evidence, explainable recommendation logic
Financial Services-Specific Requirement
The EU AI Act requires financial institutions deploying high-risk AI systems to conduct a Fundamental Rights Impact Assessment (FRIA) before first use. This is not optional. It is specific to financial services and public service entities.
A FRIA must assess risks to fundamental rights — including non-discrimination, privacy, and consumer protection — and document the mitigation measures in place.
Our 8-dimension framework produces the quantitative evidence a FRIA requires: demographic parity ratios for non-discrimination, PII exposure detection for privacy, and explainability coverage for consumer protection. The assessment is documented in a registered, hash-citable audit trail.
Documented Methodology
When a financial regulator asks how you evaluate AI compliance, you need more than a vendor's proprietary algorithm.
Our 8-dimension framework was developed by PhD researchers in AI ethics, bias detection, and regulatory governance. It is documented in full, and external review is under way through university and research-centre partnerships across three domains.
This matters in financial services more than any other industry. Financial regulators have decades of experience scrutinizing methodologies. They will ask how your compliance framework was validated. "It's proprietary" is not an answer they accept.
Built from your world
Open-source probe sets are a fixed corpus. They ask every institution the same questions, so they find the failures every institution shares and miss the ones that only exist in yours. We generate probes from your own material — the articles of the regulation you operate under, the products in your catalogue, the policies you publish — so each attack only makes sense against your system.
Worked example
One probe family asks your advisory assistant about a provision that sits immediately beside a real one — a sub-article of MiFID II or DORA that reads plausibly, cites correctly, and was never written. A confident answer is not a matter of opinion. It is a fabrication about the rulebook your institution answers to.
A fixed corpus cannot build that probe, because it does not know which provisions are real in your jurisdiction, which products you actually sell, or which of your policies the assistant was given.
Why the finding holds
Whether a provision exists is settled against a content-addressed knowledge base by verifying membership, not by asking a model to decide. The probe carries its own ground truth, so the answer is either consistent with the record or it is not.
That is what makes the finding survive an argument. Risk still arrives as an occurrence rate over N runs with a coverage report — but the thing being counted is not contestable.
Proof
CloudWhere Your AI Actually Stands
Your models change with every release. Knowing where you stand is a measurement you repeat, not a certificate you file.
Best for: Financial institutions that need to understand their readiness before the August 2026 deadline.
The governance layer
Proof On-PremiseMulti-Compliance AI Readiness
Proof deployed on-premise for every AI system in production.
Best for: Financial institutions deploying AI at scale that need multi-compliance readiness across multiple regulations.
Your AI systems are classified as high-risk. The regulatory framework is in force. EBA, ESMA, and national authorities will enforce compliance. The question is not whether to act — it is whether you have defensible evidence when they ask.