Rules And FLM

Loci rules are represented with FLM, the Fraud Language Model. FLM is an explainable control format for fraud and AML logic. It captures what a rule is trying to detect, which signals it uses, how it scores evidence, and when it should trigger.

FLM gives teams the clarity of rules with a more flexible, model-like evaluation pattern through MADIE.

Why FLM Exists

Traditional rule engines are often brittle and opaque once they grow. Black-box models can be powerful but difficult to govern. FLM sits between those extremes:

  • Analysts can understand the control intent.
  • Engineers can inspect the fields, thresholds, aggregations, and outputs.
  • Reviewers can approve or reject changes before deployment.
  • MADIE can evaluate the control consistently in real time.

Authoring Rules

Rules can be created in several ways depending on the deployment:

  • Manual authoring in the rule editor.
  • AI-assisted authoring from a fraud scenario, policy, or analyst prompt.
  • Discovery-assisted authoring from observed patterns and historical behavior.
  • Imported or generated FLM reviewed by an operator before deployment.

Human review remains important. AI can accelerate discovery and drafting, but production deployment should be governed.

Active And Inactive Rules

A rule's status determines whether it participates in live decisions.

  • Active rules are evaluated in the live decision path.
  • Inactive or paused rules can be edited and tested without changing customer-impacting decisions.

Inactive testing is the preferred workflow for tuning a rule before activation.

Shadow Evaluation And Historical Replay

Loci supports testing selected inactive rules against:

  • A manually entered transaction payload.
  • Stored historical transactions over a selected date range, where available.
  • One rule or multiple rules at the same time.

This gives teams a safe pre-deployment testing workflow: test before launch, compare outcomes, inspect matched evidence, and deploy only when the control is ready.

The payload is sent through the live engine in shadow or evaluation mode, allowing teams to safely test rules with real data before activating the rule.

MADIE Evaluation

MADIE evaluates FLM controls in real time. It behaves more like a control-intelligence engine than a simple if-then rules list because rules can combine signals, aggregations, historical windows, thresholds, exclusions, and score-like behavior.

This lets teams evaluate explainable controls consistently while preserving the evidence needed for review and audit.

Governance

A healthy rule lifecycle includes:

  • Drafting.
  • Peer or maker-checker review.
  • Inactive testing.
  • Approval for activation.
  • Live deployment.
  • Monitoring for false positives and missed fraud.
  • Periodic retirement or revision.

API Reference