Regulatory Intelligence

Monitor. Verify. Protect.

Regulatory pressure, evolving money-laundering and fraud patterns, and model risk are increasing faster than review teams can keep pace. Three engines help banks and insurers address these challenges: they compare new regulatory requirements with the internal rulebook, detect suspicious patterns in transaction and claims data, and assess pricing and risk with quantified uncertainty. Every review is documented and traceable; every decision remains with a human.

Explore the products

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Our products

Three engines, each a finished product in its own right with its own methodology – developed, implemented and, if you wish, operated by us.

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Core Regulatory Engine

Checks regulation against your rulebook – with every conclusion fully substantiated.

External requirements and your internal rulebook are maintained as linked knowledge graphs. A verification process walks this structure step by step and matches every requirement against your body of rules: for each requirement it produces a finding – full coverage, partial coverage, gap or contradiction – with source passages and a chain of verification in which every step is reasoned. Checking is continuous: new regulation is fed in and mirrored automatically on an ongoing basis, not once in a project. And where the evidence does not hold, the system flags the open point. An identified gap is a usable finding; an invented coverage would be a risk.

Four outcome categories: full coverage, partial coverage, gap, contradiction
Every verification step reasoned and logged – internal audit can repeat it
Continuous assessment instead of a one-off project

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FinCrime Engine

Finds unknown patterns without ever having seen a case.

Confirmed cases are rare and biased: anyone who learns only from them finds yesterday's patterns. The engine therefore learns what is normal from the structure of your data itself, and examines every case from several angles at once: the individual transaction, its embedding in account and payment networks, its progression over time, and domain rule knowledge. No single view decides on its own; only their combination produces the finding. What you get is not hasty probabilities but robust rankings: prioritised cases for review, with traceable reasoning and documented evidence.

Learns without training cases and finds patterns no one has yet formulated
Several views of every case – none of them decides alone
Robust rankings instead of feigned probabilities

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Pricing Engine

Calculates pricing and risks with uncertainty that is demonstrably accurate.

It treats uncertainty as a first-class quantity: premiums, reserves and capital are calculated from full probability distributions, not from point estimates. In doing so, it separates the randomness of the world (aleatoric uncertainty) from what the model does not know (epistemic uncertainty). The first belongs in the capital model, the second in model risk management. And the model has to prove its probabilities: calibration and coverage are continuously tested against unseen data and corrected – right down to portfolio level, where creeping holes in results would otherwise emerge that no quality metric reveals. This keeps the methodology defensible to regulators.

Premiums, reserves and capital from full distributions
Aleatoric and epistemic uncertainty are reported cleanly and separately
Demonstrable calibration, continuously tested against unseen data

The challenge

New regulation meets limited capacity. Every requirement has to be checked against your rulebook: reasoned, documented, audit-proof. Manual point-in-time reviews do not scale.

Fraud and money laundering are hidden within vast datasets where confirmed cases are scarce. Risk models must explain their methodology to regulators, not merely present their results.

Our answer

Three engines that prepare, verify and document. They match regulation, prioritise cases for review and calculate risks – every statement with its source, every check with documented evidence.

At the end of every chain stands a decision – and that is always made by a human. Our products provide the verified, evidenced basis for it.

What we offer

From the first assessment run to documented operation – modular, and from a single source.

✓ Continuous assessment of new regulation: four fully substantiated outcome categories.
✓ Pricing and risk models with calibrated, demonstrated uncertainty.
✓ Documented verification chains that internal audit can repeat step by step.

✓ Prioritised review cases with documented evidence for FinCrime teams.
✓ Calibration and coverage evidence for internal audit and regulators.
✓ Operation and monitoring by the same team that built them.

In brief

Four key Regulatory Intelligence terms explained briefly: regulatory coverage assessment, anomaly detection, calibrated uncertainty and documented evidence.

How does a regulatory coverage assessment work?

A regulatory coverage assessment systematically compares each new requirement with the internal rulebook and classifies the result as full coverage, partial coverage, a gap or a contradiction.

How does anomaly detection support anti-money laundering and fraud prevention?

Anomaly detection learns typical patterns in transaction and claims data and prioritises anomalies for specialist review, even without known fraud cases as training data.

What does calibrated uncertainty mean?

Calibrated uncertainty means that predicted probabilities correspond to observed frequencies over time: among comparable cases assigned an 80% probability, the event occurs around eight times out of ten.

What does documented evidence mean?

Documented evidence records the data used, findings, sources and verification steps for each review case. This enables specialist teams and internal audit to trace every finding.

FAQ's

Do the engines replace human specialist reviewers?

No, the engines do not replace specialist reviewers. They prepare review cases, verify results and document the data, findings and verification steps used. Assessment and decision-making remain with your specialist reviewers.

How does the Core Regulatory Engine assess new regulatory requirements against the internal rulebook?

The Core Regulatory Engine systematically compares new regulatory requirements with the internal rulebook. For each requirement, it documents full coverage, partial coverage, a gap or a contradiction, together with the reasoning, sources and verification steps.

What advantages does calibrated uncertainty offer over conventional models?

Calibrated uncertainty provides reliable probabilities rather than individual point estimates. When a model assigns an 80% probability to comparable cases, the outcome occurs in approximately 80% of those cases over time. This allows pricing, reserves and capital to be calculated using the full distribution of possible outcomes.