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transaction-monitoring

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Blnk Watch is a domain-specific language (DSL) for creating real-time transaction monitoring rules. It enables you to define conditions and automated actions for detecting fraud, enforcing limits, and staying compliant. A Watch script is declarative: you describe what to detect and what action to take—the engine handles evaluation at runtime.

  • Updated Mar 17, 2026
  • Go

⚡ Real-time fraud & anomaly detection system for streaming transactions. Built with Kafka Streams + Isolation Forest ML. Low-latency processing, online learning, and scalable architecture for detecting fraud patterns in transaction data. 🚨🔍

  • Updated Nov 20, 2025
  • Java

Enterprise-grade fraud & AML detection with ML and deep learning (XGBoost, LightGBM, Autoencoder, LSTM, Transformer). Real-time API, explainability (SHAP), BI export, Streamlit dashboard. PaySim-compatible.

  • Updated Feb 6, 2026
  • Python

Multi-agent fraud detection pipeline for the Reply AI Challenge, combining deterministic transaction features, communications analysis, and a LangGraph supervisor to detect suspicious transactions with Langfuse tracing and OpenRouter-powered LLM reasoning.

  • Updated Apr 17, 2026
  • Python

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