FinGuard

Financial Anomaly Detection

Running in production across 3 trading desks with zero downtime since launch.

94%Anomaly detection recall

0.08%False positive rate

<500msDetection latency

$4.7MFraud prevented in Q1

The Challenge

Detect anomalous transactions and market manipulation patterns across high-frequency trading data with sub-second latency and less than 0.1% false positive rate at production scale.

Our Approach

Isolation Forest and autoencoder ensemble for unsupervised anomaly detection, with a GBM meta-learner for final scoring. Streaming architecture with Apache Kafka for real-time ingestion and Flink for windowed aggregations.

Tech Stack

Isolation ForestAutoencoderApache KafkaApache FlinkXGBoostRedis

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