Fraud Detection using Agentic AI Coming up Soon
This hands-on series will teach you how to build intelligent, agent-driven fraud detection systems. You’ll learn to combine machine learning models, real-time sentiment signals, and autonomous reasoning agents to identify suspicious transactions in modern fintech environments.
• How fraud detection systems work • ML scoring models (Random Forest, XGBoost) • Time-series modeling with LSTM • Integrating sentiment & macro indicators • Building LLM-based agents with LangGraph • End-to-end fraud reasoning pipeline
• Python + Jupyter + FastAPI stack • PostgreSQL schema for transaction data • Optional: Integration with payment APIs
• Collect financial transactions (real or simulated) • Enrich with sentiment & macro signals • Store and query effectively from PostgreSQL
• Feature engineering: amount patterns, timing, user behavior • Apply XGBoost & Random Forest • Evaluate precision, recall, and fraud detection accuracy
• Model temporal fraud patterns • LSTM input preparation • Compare performance with tree models
• Use FinBERT, OpenAI, Gemini, or Mistral • Extract impact of news and signals on fraud risk • Score transactions contextually
• Build a LangGraph-based Fraud Router Agent • Reasoning chains: Is this fraud? If yes, why? • Integrate with a frontend or alerting system
Deploy a fully working, explainable fraud detection pipeline using Agentic AI — complete with real-time alerts, scoring, and decision logs.
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