Fraud & AML intelligence

ML-driven detection with evidence you can defend.

RegNovaIQ combines adaptive models, graph intelligence, and investigator feedback to surface sophisticated fraud patterns and AML risks — mule networks, synthetic identities, and document fraud — with explainable scoring and a governed model lifecycle behind every decision.

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Fraud intelligence pillars

  • Money mule and ring detection via graph neural networks
  • Synthetic identity and document fraud analysis
  • Explainable risk scoring with model governance
  • Closed-loop investigator feedback feeding model retraining
Where it fits

Catching what rules alone miss

Sophisticated fraud hides in relationships and identities, not single transactions. RegNovaIQ layers graph analytics and machine learning over your monitoring controls to expose coordinated networks and engineered identities — then keeps learning from every analyst decision.

Network detection

Graph neural networks trace mule rings, layering chains, and shared-attribute clusters across accounts, devices, and counterparties.

Identity integrity

Synthetic identity and document fraud analysis flags engineered or manipulated identities at onboarding and in-life.

Continuous learning

Confirmed and dismissed cases flow back into model retraining, so detection sharpens as fraud tactics evolve.

Detection engine

Unified ML and rules-based intelligence

Blend statistical models with expert-defined policies and orchestrate them through a single governance framework, so detection power never comes at the cost of explainability or control.

Graph risk propagation Adaptive anomaly detection Scenario analytics Model explainability

Governed AI lifecycle

Track models end to end — from training-data lineage to approvals — with built-in audit trails, drift monitoring, and policy controls.

Model governance

A model registry with versioning, approvals, and drift monitoring built in.

Feedback loops

Analyst decisions inform model recalibration on a closed loop.

Evidence ledger

Traceable, immutable evidence for every fraud decision.

Capabilities in depth

Detection, explanation, and governance in one place

Mule network detection

Graph neural networks reveal money-mule rings and layering structures that span many accounts and never show up in a single-transaction rule.

Graph Intelligence

Synthetic identity analysis

Detect engineered identities and document manipulation by correlating identity attributes, device signals, and behavioral patterns.

Identity Fraud

Closed-loop feedback

Investigation outcomes are wired back into model retraining orchestration so the system improves with every disposition.

Continuous Learning

Streaming features

Real-time feature computation with freshness SLAs and lineage tracking feeds models the latest signals, not stale aggregates.

Feature Pipeline

Explainable scoring

Each risk score exposes its contributing features and reasoning, so analysts and regulators can see exactly why a case scored as it did.

Explainability

SAR generation

Automated SAR generation assembles evidence chains and analyst notes into regulator-ready filings with approval workflows.

Regulatory Ops
Engines behind it

Graph, ML, and governance, working together

Fraud detection draws on the platform's shared engines so detection, simulation, and root-cause analysis operate over one resolved entity graph rather than disconnected models.

AURA — unified risk assessment Digital Twin — scenario modeling Causal AI — root-cause analysis GAIA — model governance

Operational excellence

Investigation workflows scale with risk volume: related alerts cluster into cases, cross-channel intelligence connects the dots, and every step is captured for audit.

Case orchestration Cross-channel intelligence Regulator readiness
Mitigate fraud with confidence

Deploy explainable fraud intelligence at scale

Work with RegNovaIQ to design a governed fraud and AML analytics program.

Engage our fraud team