GenZAgents for fintech — AI activity audit for FCA, PRA, SEC, and MiCA scope

The regulatory pile-up

UK: FCA Supervisory Statement 1/23 on AI in financial services + PRA SS3/23 on model risk management both require firms to evidence AI usage controls. EU: MiCA Article 41 + DORA both require operational resilience documentation for AI-mediated decisions. US: SEC 2025 AI guidance to investment advisers expects "comprehensive AI activity records". Each regulator wants similar evidence; the natural answer is a unified audit layer.

Why fintech AI is high-stakes

Investment recommendations, credit decisions, fraud screening, customer-facing chatbots — all AI-mediated and all subject to regulatory scrutiny. The penalty exposure is enormous: FCA fines for inadequate controls have hit £100M+; SEC AI-disclosure fines started in 2025 (e.g. Delphia $400k, Global Predictions $175k). The "no audit trail for AI" finding is no longer a paper-tiger risk.

Specific FCA SS1/23 controls receipts satisfy

SS1/23 §3.1.4: maintain records of AI model decisions. Receipts log the model + the decision + the supervising human. §3.2.7: evidence of human oversight at appropriate decision points. Receipts capture reviewer_human_id and decision timestamps. §4.1.2: monitoring for model drift. Anomaly detection flags atypical model use. The mapping is documented in the FCA-specific evidence pack.

DORA operational resilience scope

DORA Article 6 requires ICT risk management including AI dependencies. Article 28 requires third-party-risk register including AI providers. The receipt feed becomes both the operational ICT log (Article 6) and the third-party-usage record (Article 28). One source feeds two regulatory views.

Operational scenario: credit decisioning

Your AI-assisted credit decision system uses Claude to evaluate edge cases. Without receipts: when the regulator asks "why was application X declined?", you have only the engineer's recollection. With: the receipt shows the prompt + model + supervising-human + decision. The regulator gets a verifiable record; the candidate gets a defensible answer.

Operational scenario: anti-money-laundering

AI-flagged transactions get reviewed by analysts. The receipt captures the AI flag, the analyst's human_id, the decision (escalate / clear), the timestamp. Your MLRO has the full chain; the regulator's next audit gets it in the evidence pack. SAR filings reference the receipt IDs for chain of custody.

Common questions

Does GenZAgents satisfy the SEC's 2025 AI guidance for RIAs?▾

The SEC's AI guidance asks for "policies, controls, and records of AI use". Receipts are the records side; the dashboard's ACL / anomaly features map onto the controls side; you write the policies that tie it together. Talk to your compliance counsel for jurisdiction-specific interpretation.

Can we run this on-prem for data residency?▾

Yes — Enterprise tier ships a Helm chart for full self-hosted deployment. Receipts stay in your cluster; the AI provider calls still flow to your chosen providers under your existing data-residency strategy.

How does this interact with our existing model risk management (MRM)?▾

Complementary. MRM frameworks document the model itself (validation, monitoring, governance). Receipts document the model's actual usage in production. Most MRM auditors want both; GenZAgents fills the actual-usage half.

What about the FCA's consumer duty obligations?▾

For AI-mediated customer interactions: receipts capture which AI, supervising human, decision outcomes. The audit trail supports consumer-duty fair-treatment evidence — "we can verify this customer's AI-mediated interaction was reviewed by qualified staff X at time Y".