BackHackers AI uses ensemble machine learning models to detect fraudulent transactions with 99.7% accuracy — in under 80ms.
Monitor every transaction as it flows through our detection engine — block fraud before funds move.
XGBoost, LightGBM, and deep neural networks work in parallel — each model votes, a meta-learner decides.
Redis-cached feature stores and vectorized inference pipelines deliver decisions before users notice latency.
SHAP values and LIME explanations surface every signal — your compliance team will love the audit trail.
Map relationships between accounts, devices, and IPs to uncover fraud rings that rule-based systems miss entirely.
Continuous online learning with concept drift detection automatically adjusts models as fraud patterns evolve.
Anomaly detection with autoencoders catches novel attack patterns that have never been seen in training data.
Transaction event streams via Kafka. 340M+ events/day ingested with sub-5ms p99 latency.
200+ real-time features: velocity, geo-distance, device fingerprint, behavioral biometrics.
7-model ensemble with gradient boosting, LSTM sequences, and GNN embeddings in parallel.
Stacked generalization layer combines model outputs into a single calibrated risk probability.
Block / Review / Allow with configurable thresholds. Full SHAP explanation logged per decision.
Enter transaction details below and watch the ML engine evaluate risk in real time.