Contents
CMA 2025-level market overview
Enterprise-Grade Platforms (Full Automation)
These are the most advanced tools — designed for large banks, financial institutions, and regulated industries.
| Platform | Highlights | Strengths | Price Level |
|---|---|---|---|
| ServiceNow GRC / IRM | Built-in regulatory change management, control mapping, workflow automation | Deep integration with IT systems, audit trails, AMLR/DORA ready | High |
| MetricStream | Strong AML/Compliance modules, AI-driven mapping, excellent reporting | Used by Tier-1 banks and insurers | High |
| RSA Archer | Mature risk & control library, mapping engine, API connectors | Very strong in SOX, AML, and data protection frameworks | High |
| NAVEX IRM | Cloud-based, intuitive control mapping and regulatory tracking | Easier to configure for smaller compliance teams | Medium |
| OneTrust GRC / ESG / Privacy | Modular approach (AML, DORA, GDPR, ESG); auto-mapping via AI | Best-in-class regulatory content library (Thomson Reuters, Compliance.ai) | High |
Why choose this tier:
If you are a regulated financial entity (bank, PSP, CASP) that must evidence AMLR / DORA / MiCAR readiness to auditors or BaFin, these systems provide the strongest audit defensibility.
Downside: Licensing and configuration can easily exceed €100k–€250k per year.
Mid-Tier Platforms (Best Price-Performance Ratio)
These are flexible, moderately priced, and often sufficient for most financial institutions and fintechs.
| Platform | Highlights | Strengths | Price Level |
|---|---|---|---|
| LogicManager | Clean interface, built-in control mapping, free regulatory templates | Easy to implement for AML and DORA frameworks | Medium |
| StandardFusion | Strong mapping automation for ISO, SOC, AML policies | Cloud-native, fast setup | Low |
| Vanta / Drata / Scrut Automation | Originally SOC 2 / ISO automation, now expanding into AML / DORA | Excellent for evidence automation & continuous monitoring | Low |
| Hyperproof | Control mapping across multiple frameworks; integrations with Jira, Slack, SharePoint | Ideal for hybrid AML / InfoSec teams | Low |
| Compliance.ai | Focused on automated regulatory updates and mapping rulesets | Great if your primary goal is change-tracking | Low |
Why choose this tier:
You want automation and credible mapping without the cost or complexity of an enterprise rollout.
Expect €1 000–€4 000 per month, depending on users and modules.
Open-Source and Free / Low-Cost Options
Perfect for building a prototype or internal pilot before investing in a commercial system.
| Stack / Tool | What It Does | How It Helps in Control Mapping | Cost |
|---|---|---|---|
| OpenRegulatory Toolkit (GitHub) | Free YAML/JSON model for regulation→control mapping | You can ingest AMLR texts and build your own mapping engine | Free |
| OpenGRC (OASIS / OpenControl) | YAML-based open framework for compliance mapping | Used by US FedRAMP, easily extended to AMLR | Free |
| Open-Source Stack (PostgreSQL + LangChain + Weaviate) | Custom control mapping automation (semantic search + citations) | Full flexibility — ideal if you already have dev resources | Free + development time |
| Notion / Airtable + Zapier | Build lightweight manual mapping workflows | Simple for small AML teams with few frameworks | 10–50 €/month |
| Microsoft 365 (SharePoint + Power Automate + Lists) | Automate basic mapping and evidence storage | Works well if you follow the Power Automate AML blueprint | Included in M365 Enterprise |
Best free-to-start combination:
SharePoint + Power Automate + Excel/Lists + AI semantic search via Azure OpenAI or LangChain.
You can model obligations (AMLR Articles) and link them to your internal controls — exactly as in the AMLR-focused blueprint we’ve discussed earlier — with zero extra licensing cost if your institution already runs Microsoft 365.
RegTech Platforms Adding Control Mapping Modules
Some AML-specific vendors are adding control-mapping functions to their KYC or monitoring suites.
| Platform | Focus | Notes |
|---|---|---|
| Lucinity | AML case management with AI audit traceability | Working on cross-mapping controls to AMLA/AMLR articles |
| Napier Continuum | AML monitoring + compliance workflow automation | Provides risk-control linkage for regulatory obligations |
| Clausematch / CUBE / Ascent RegTech | Policy management + regulatory mapping | Strong in automatic “law-to-control” extraction, AML-relevant |
These are promising if you already use their AML monitoring stack — you can add mapping capabilities without a separate GRC system.
Recommendation by Use Case
| Organisation Type | Best Fit | Reason |
|---|---|---|
| Large Bank / Group | ServiceNow, MetricStream, OneTrust | Audit strength, enterprise control inventory |
| Mid-Size Financial Institution | LogicManager, Hyperproof, StandardFusion | Balanced cost and functionality |
| Fintech / CASP / PSP | Vanta, Drata, Scrut Automation | Lightweight automation, API connectivity |
| Consulting / Advisory Pilot | OpenRegulatory + Power Automate stack | Quick to build, good proof of concept |
| Academic / Research / Prototype | OpenGRC or OpenControl | 100 % free, YAML-based, transparent data model |
Cost Spectrum Summary
| Tier | Typical Annual Cost | Suitable For |
|---|---|---|
| Enterprise GRC | €100 000–€250 000+ | Large banks, global groups |
| Mid-Tier SaaS | €15 000–€60 000 | Mid-size AML teams |
| Lightweight / API-Driven | €5 000–€15 000 | Fintech, crypto, RegTech |
| Open Source / DIY | €0–€5 000 (mainly staff time) | Pilots, innovation teams |
How to choose?
When evaluating, consider:
- Regulatory Breadth: Does it include AMLR, 6AMLD, GwG, DORA, MiCAR, and FATF sources?
- Evidence Management: Can it attach, hash, and version control proof artefacts?
- Change Monitoring: Does it automatically track EU delegated acts and BaFin guidance?
- Integration: Can it connect with your existing systems (SharePoint, Case Management, Screening, Training LMS)?
- Audit Trail: Does every mapping and edit carry user/time/diff metadata?
- Data Sovereignty: Is hosting EU-based and GDPR-compliant?
Trends 2025–2027
By 2027—when AMLR becomes enforceable—many platforms will integrate Large Language Models to assist human reviewers:
- Auto-tagging AMLR articles (“EDD”, “PEP”, “Recordkeeping”).
- Semantic similarity mapping (e.g., AMLR Art. 47 ↔ § 8 GwG).
- Impact diff when new guidance appears.
However, regulators insist on human oversight: every automated match must be reviewable and traceable (“no black box compliance”).