Every transaction is classified as Normal or Risk with a confidence score, providing clear and immediately actionable results.
Gradient Boosting, Random Forest, Decision Tree, and Logistic Regression work together. The best model is selected automatically based on recall performance.
Detects structuring, dormant account abuse, new account exploitation, sanctions violations, CTRF flags, cross-border risks, rapid turnover, and high-value anomalies.
Amount transformations, profile risk indicators, geographic flags, channel encoding, and custom risk binning combine for superior detection accuracy.
Instant analysis with no external dependencies. The system evaluates each transaction in milliseconds and returns a full compliance report.
Trained to minimize false negatives. The system prioritizes catching fraud over avoiding false alarms, achieving recall rates above 99% on test data.
Enter transaction details: customer profile, type, amount, channel, and location.
The system generates 15 features including fraud indicators, amount transformations, and risk scores.
Four machine learning models analyze the transaction. The best model's prediction is used as the primary result.
Specific fraud patterns are identified based on the transaction's indicators and risk profile.
The transaction is classified as Normal or Risk with a percentage-based confidence score.
A detailed report is generated with recommended actions, next steps, and compliance documentation.