All solutions
In validation
Financial Fraud Intelligence
Detect anomalies and prioritize what deserves attention.
Financial servicesCooperativesInsurance
01 / The business problem
Rule-based monitoring raises too many alerts and misses new patterns. Analysts spend their time clearing false positives instead of investigating real risk.
02 / The capability
- 01
Anomaly detection over transactions and events
- 02
Alert prioritization by risk
- 03
An explanation for every alert that analysts can understand
- 04
Model monitoring and evaluation over time
03 / The architecture
For technology and engineering teams
Feature pipelines and MLOps with MLflow, Vertex AI or SageMaker; per-alert explainability; demonstrations run exclusively on synthetic data.
- 01EventsTransactions and activity
- 02FeaturesFeature engineering
- 03ModelsAnomaly + supervised
- 04ScoringReal-time API
- 05AlertsPriority and explanation
- 06MonitoringDrift and evaluation
04 / What a pilot includes
- 1A defined fraud scenario and data scope
- 2Feature pipeline and baseline models
- 3Scoring API and an alert review view
- 4An evaluation report with limitations stated
Solution in active validation. Pilots and scope are defined with each client.