Behavioral Biometrics and Pattern Profiling

We use AI to analyze user behavior—keystroke patterns, device usage, transaction frequency—and build dynamic profiles. Tools like BioCatch, BehavioSec, and custom LSTM models help detect identity fraud and bot activity.

Real-Time Transaction Scoring

Implement streaming fraud detection engines that assign risk scores to each transaction in milliseconds using tools like AWS Fraud Detector, Feedzai, and Spark Structured Streaming. High-risk events are flagged for automated blocking or manual review.

Graph-Based Fraud Ring Detection

Uncover collusion and fraud networks using graph analytics platforms like Neo4j, Linkurious, and TigerGraph. We model relationships across accounts, IPs, and devices to identify hidden rings and synthetic identities.

Anomaly Detection with Unsupervised Learnings

Leverage clustering, isolation forests, and autoencoders to flag deviations from normal activity in high-volume datasets. We use Python-based ML frameworks (e.g., PyOD, Scikit-learn, TensorFlow) for unsupervised fraud modeling.

Multichannel & Multimodal Monitoring

Detect fraudulent behavior across web, mobile, POS, IVR, and IoT channels using unified data pipelines powered by Kafka, Snowflake, and Azure Stream Analytics. Fuse structured and unstructured signals for full coverage.

Explainability, Compliance, and Auditability

Ensure fraud decisions are transparent and defensible using SHAP, LIME, and built-in explainability in ML frameworks. We generate traceable alerts and audit logs to comply with regulations like PCI-DSS, SOC 2, and AML directives.