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Production-ready fraud detection system using Claude 4.5 Sonnet, AutoGluon ML, Docker, and MCP - with real-time technical demo GUI

An AI-driven insurance fraud detection project that uses multiple agents combining rule-based logic and LLM analytics to identify, investigate, and explain fraudulent claims, with an interactive dashboard and natural-language query interface.

AI-powered invoice processing system using multi-agent pipeline, RAG, OCR, and LLM for automated extraction, validation, and fraud detection.

An intelligent multi-agent system for processing insurance claims with automated fraud detection, document management, and interactive dashboards. Built with Python, Streamlit, and a modular agent architecture for seamless claims processing and analysis.

Multi-Agent AI Fraud Detection System | GenAI Genesis Hackaton

On-device multi-agent system for real-time fraud and anomaly detection. Continuously learns user behavior patterns (e.g., touch, typing, app usage, movement) locally to detect unauthorized access, spoofing, or bot-like activity — all without sending sensitive data to external servers.

Multi-Agent AI System for Financial Operations | Fraud Detection | Compliance | Spend Analysis | LangGraph | FastAPI | Production-Ready

AI-driven multi-agent fraud detection system combining anomaly detection, behavioral profiling, and explainable AI (SHAP) with real-time risk scoring.

State-of-the-art Generative AI-based insurance fraud detection system with multi-agent architecture, RAG, and advanced prompt engineering

AI-Powered Financial Digital Twin: An enterprise fraud detection system using Graph Intelligence (NetworkX), Multi-Agent Orchestration, and Cryptographic Audit Trails to identify collusive fraud and compliance breaches in real-time.

Multi-agent AI system for real-time fraud detection, credit risk assessment, KYC identity verification, and sanctions screening. Built with XGBoost, LightGBM, and IsolationForest for sub-100ms latency.

AI-powered multi-agent insurance claim validation system built with LangGraph. Agents handle identity verification, document validation using RAG, policy rule checks, and fraud detection, sharing a centralized claim state. A master agent aggregates results into an explainable claim decision.

Autonomous multi-agent AI fraud detection system for Indian banking. 5 specialized agents (TMA, PRA, RAA, ABA, CLA) detect and respond to suspicious transactions in real-time using Isolation Forest ML, ChromaDB RAG, and RBI/PMLA regulatory compliance.

当黑产用上 AI 与多 Agent 协同：传统异常检测模型为何失效，以及如何通过重构边际成本来打造下一代业务风控架构。When black-hats use AI & Multi-Agent systems: Why traditional anomaly detection fails and how to rebuild next-gen fraud defense through marginal cost gaming.

Content Data for the case is available in CSV format having 6362620 rows and 10 columns. Data Dictionary: step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation). type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER. amount - amount of the transaction in local currency. nameOrig - customer who started the transaction oldbalanceOrg - initial balance before the transaction newbalanceOrig - new balance after the transaction nameDest - customer who is the recipient of the transaction oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants). newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants). isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system. isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction. Inspiration Following tasks & questions can be answered using the data, Data cleaning including missing values, outliers and multi-collinearity. Describe your fraud detection model in elaboration. How did you select variables to be included in the model? Demonstrate the performance of the model by using best set of tools. What are the key factors that predict fraudulent customer? Do these factors make sense? If yes, How? If not, How not? What kind of prevention should be adopted while company update its infrastructure? Assuming these actions have been implemented, how would you determine if they work?

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Multi-Agent Fraud Detection System

Fraud Detection Multi-Agent System

Multi-Agent Fraud Detection System

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On-Device Multi-Agent System for Behavior-Based Anomaly & Fraud Detection . Multi-agent system that runs fully on-device, continuously learning and modeling user behaviour patterns to detect anomalies or potential fraud in real-time, without sending sensitive data to external servers.

Article Multi-Agent Healthcare Fraud Detection System

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Multi Agentic System for Fraud Invoice Detection

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Autonomous Multi-Agent Fraud Detection System using IEEE-CIS

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Final Year Project: Multi-Agent AI System for Fraud Detection