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FinC2E — Financial Cognitive Compliance Engine

Governance-First AI for AML/KYC & Audit-Ready Decision Support (Human-in-the-Loop)

FinC2E is a governance-first AI advisory system designed for regulated, high-stakes environments where decision legitimacy, traceability, and accountability are more critical than automation or speed.

FinC2E is developed under BPM RED Academy — HumAI MightHub as a controlled, enterprise-grade cognitive layer supporting human decision-makers in compliance, risk, and audit workflows.

This model represents the cognitive reasoning layer of FinC2E and does not constitute a deployable system on its own.


Core positioning (non-negotiable)

  • Advisory-only AI — Human-in-the-Loop by design
  • No autonomous enforcement
  • No replacement of compliance officers
  • No black-box decisions
  • Responsibility always remains with the human decision-maker

FinC2E is intentionally not an autonomous compliance system.


What FinC2E does

FinC2E supports regulated teams by generating structured, reviewable reasoning, not decisions.

Primary capabilities include:

  • Case triage & prioritization support
    Risk-oriented reasoning signals for human review

  • Policy-referenced reasoning
    Outputs explicitly reference assumptions, rule context, and logic paths

  • Audit-ready narratives
    Structured explanations suitable for auditors, regulators, and committees

  • Scenario & stress-logic support
    FSAP-style views and counterfactual reasoning

  • Committee-ready summaries
    Clear, human-readable briefs for decision boards


What FinC2E is NOT

  • Not an automated blocking or freezing system
  • Not a penalty or enforcement engine
  • Not a legal authority or regulator
  • Not a “push-button compliance” product

FinC2E does not take actions.
It supports human judgment under governance constraints.


Intended users

FinC2E is designed for:

  • Banks and financial institutions (AML / KYC / CDD teams)
  • RegTech providers
  • Audit and compliance consultancies
  • Institutional risk and compliance committees
  • Government, FIU, and sovereign deployments (controlled and policy-bound)

It is not intended for consumer or retail use.


Governance & design principles

FinC2E is built around the following principles:

  1. Human accountability first
  2. Traceable reasoning over opaque accuracy
  3. Policy alignment before model optimization
  4. Auditability as a core requirement, not an add-on
  5. Controlled deployment over unrestricted access

These principles define both system behavior and engagement models.


Commercial & Institutional Use (Licensing)

FinC2E is available for commercial, institutional, and governmental use under a separate license.

Commercial licensing is required for any:

  • production use,
  • institutional deployment,
  • internal corporate use beyond evaluation,
  • integration into paid products or services.

Institutional pricing (2026 standard terms)

As of January 5th, 2026, FinC2E is offered under standard institutional pricing, aligned with increased regulatory scrutiny, AI governance obligations, and audit requirements across 2026.

Standard institutional engagement tiers:

  • Evaluation PoC (controlled scope, non-production): €187,500

  • Institutional Pilot
    (policy-bound, audited, monitored deployment)
    €750,000

  • Annual Enterprise License
    (multi-unit, multi-jurisdiction, governed deployment)
    from €1,870,500

Pricing updated to reflect expanded enterprise governance scope.

Final pricing depends on:

  • jurisdiction and regulatory context,
  • deployment model (on-prem / sovereign cloud / controlled environment),
  • governance depth, audit scope, and accountability requirements.

Indicative pricing is provided for context only.
Final commercial terms are defined exclusively through institutional evaluation and contractual engagement.


Contact for licensing and institutional engagement


Deployment models (commercial)

FinC2E may be deployed under controlled conditions via:

  • Evaluation PoC — limited scope, non-production
  • Institutional pilot — policy-defined, monitored, auditable
  • On-prem / sovereign cloud deployments — where required by regulation

Note: Any “Deploy” or hosting options shown by platform interfaces
(e.g. Hugging Face Spaces or inference tooling) refer to technical serving mechanisms only.
Commercial deployment of FinC2E is governed exclusively through licensing and contractual engagement.


Evaluation context & ecosystem alignment

FinC2E has been developed, iterated, and evaluated within the NVIDIA ecosystem, using enterprise-grade tooling aligned with regulated AI deployment standards.

Across a three-month evaluation period, the system demonstrated consistent growth in evaluator engagement and institutional interest, driven by:

  • governance-first positioning,
  • human-in-the-loop design,
  • audit-ready reasoning outputs.

Evaluation focused on:

  • decision traceability and narrative integrity,
  • stability of policy-referenced reasoning,
  • suitability for high-stakes, regulated environments.

This evaluation phase informed the system’s current commercial and institutional readiness.


Evaluation notes (for serious evaluators)

Recommended evaluation criteria:

  • Auditability of reasoning paths
  • Preservation of human accountability
  • Policy alignment and explainability
  • Integration with committee and case-management workflows
  • Reproducibility under controlled inference conditions

Feedback is welcomed from teams working in:

  • regulated AI deployments,
  • governance, audit, and explainability,
  • enterprise inference and evaluation tooling.

FinC2E is designed to integrate with enterprise inference, evaluation, and access-control tooling, including Hugging Face Enterprise environments.


Canonical reference

FinC2E — Financial Cognitive Compliance Engine
Governance-first AI for AML/KYC, risk classification, and audit-ready reasoning.
Advisory-only. Human-in-the-Loop.


Legal & compliance disclaimer

FinC2E provides decision support only and does not constitute legal advice.
All final decisions remain with qualified human reviewers and accountable officers.


© BPM RED Academy — HumAI MightHub
Engineering legitimacy into AI systems.

— Edin

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