Conformance Polaris - Regulatory AI for Financial Services Institutions

Polaris: from raw data to alerts you can defend

Legacy transaction monitoring is good at raising alerts and bad at everything after. It floods the queue with false positives, scores cases without saying why, and runs on data too fragmented to trust. When a supervisor, internal audit, or the MLRO asks the institution to account for a decision, it often cannot, and under direct AMLA supervision that gap stops being a nuisance and becomes a finding. Polaris closes it. One pipeline takes data from raw source systems through detection, context, and explanation to an immutable record, so every alert arrives with its own reasoning attached: what triggered it, which model and rule fired, which typology it matches, and the specific transactions to review. Every step, human or automated, is written to the audit trail as it happens.

The effect can be traced in two numbers that compliance leadership already tracks, because the model is tuned to reduce false positives and thereby reduce the flagging of legitimate customers. Average handling time per case drops significantly, because analysts spend their time deciding instead of investigating. Fewer analyst hours get burned chasing alerts that were never going to become a Suspicious Activity Report (SAR). The result is a direct, quantifiable cost-to-income argument that scales from the compliance function up to the CFO and COO. And as your institution builds trust in the system's track record, you decide where to extend automation further. As an example, this allows low-risk cases to clear automatically instead of queuing for manual review, always with the same full audit log underneath it.

Polaris is built to meet the standard set by the AML Regulation, the recast Directive, and direct AMLA supervision, and installs on top of your existing infrastructure, providing defensible, audit-ready output from week one. Because Polaris runs on T-Systems' EU-sovereign infrastructure, it also supports your institution's DORA operational resilience obligations, not because DORA is part of the AML solution itself, but because the infrastructure decision and the business decision reinforce each other. You get one platform that is both audit-ready on the business side and built on operationally resilient infrastructure.

https://www.conformance.dk/polaris-anti-money-laundring/

Bifrost: the sovereign AI gateway that decides which models see your data

Most European banks and insurers already run generative AI somewhere, whether it's Microsoft Copilot, Claude, or another model, often without a governance layer in front of it that satisfies the EU AI Act. Bifrost is that layer. It sits between your organisation and the AI models you use, so users and systems never call a model directly: every request goes through Bifrost, where your policies are automatically enforced. When a request comes in, Bifrost classifies the data involved, checks it against your own rules, and protects it before any model call, redacting or pseudonymising content where your policy requires it. Sensitive queries, anything touching customer or transaction data, stay on secure, EU-sovereign infrastructure and never reach an external model. Lower-risk queries, meanwhile, can be routed externally when that's the right call. You decide which models may be used for which data categories, not the vendor, not the individual user, and not the platform's own optimisation logic.

As a company, your employees get a fully branded prompt interface that isn't tied to a specific vendor's model. This means you decide whether to keep Copilot, add Claude, or run your own local model to maintain or improve the user experience. You get a solution where every prompt and every response, regardless of which model handled it, is captured in an immutable, auditable log: which user or application sent the request, which data category was processed, which policy was applied, which model processed the data, and what output was returned. That log meets EU AI Act requirements.

In other words, Bifrost is the control layer that makes sure customer data never leaves through the wrong door, and ensures you can prove it. One architectural decision underlies both products: sovereignty isn't a feature, it's the foundation. No data leaves European jurisdiction. No third party outside the EU touches your transaction data, your customer data, or your model outputs. This is embedded in the architecture from the ground up, built by a partner legally and operationally accountable under European law.

https://www.conformance.dk/bi-frost/