{"slug": "building-a-real-time-agentic-fraud-sentinel-using-tigergraph-fastapi-and-vercel", "title": "Building a Real-Time Agentic Fraud Sentinel using TigerGraph, FastAPI, and Vercel", "summary": "A developer built Agentic Fraud Sentinel, a real-time fraud detection system that combines TigerGraph's GraphRAG engine with a FastAPI backend and a Vercel-hosted dashboard to traverse multi-hop entity relationships and return explainable chain-of-thought risk decisions. The system matches new transactions against historical fraud cases with similarity scores and adjusts confidence dynamically, moving from a 78% pre-NBA score requiring step-up authentication to a 95% post-NBA score triggering an automated card block and SAR filing. The developer also documented a Vercel deployment failure caused by the platform auto-detecting Python files as a serverless function, resolved by forcing static-site treatment via vercel.json rewrites and empty build commands.", "body_md": "Financial fraud in the modern digital age is complex, fast, and highly networked. Traditional relational (SQL) databases struggle when analyzing multi-hop connections—such as shared IP addresses, linked device IDs, and rapid money transfers across accounts—because deeply nested JOIN operations introduce severe latency.\n\nTo tackle this, we built Agentic Fraud Sentinel: an autonomous, real-time fraud detection system powered by TigerGraph’s GraphRAG engine and a FastAPI backend. It analyzes transaction streams, traverses deep graph networks in milliseconds, and provides actionable decisions with explainable Chain-of-Thought (CoT) reasoning.\n\n🏗️ High-Level System Architecture\n\nOur solution follows a decoupled full-stack architecture to ensure low latency and high scalability:\n\n┌──────────────────────────┐          ┌──────────────────────────┐\n\n│  Frontend (Vercel)       │  HTTP    │  Backend (Render)        │\n\n│  - Single-page Dashboard │ ───────> │  - FastAPI Application   │\n\n│  - Tailwind CSS / JS     │ <─────── │  - Python 3.x            │\n\n└──────────────────────────┘          └────────────┬─────────────┘\n\n                                                   │ RESTPP APIs\n\n                                                   ▼\n\n                                      ┌──────────────────────────┐\n\n                                      │  TigerGraph Database     │\n\n                                      │  - Graph Analytics       │\n\n                                      │  - GraphRAG Traversal    │\n\n                                      └──────────────────────────┘\n\nFrontend (Vercel): Responsive dashboard providing live simulation triggers, risk score meters, and step-by-step reasoning views.\n\nBackend (Render): FastAPI service handling REST endpoints, orchestration, and business logic.\n\nGraph Engine (TigerGraph): Core graph engine running RESTPP endpoints to execute deep multi-hop queries and power GraphRAG evidence extraction.\n\n⚡ Core Features Walkthrough\n\nReal-Time Simulation & Event Triggering\n\nThe system accepts live transaction payloads via REST API endpoints. Through an interactive simulation modal, security analysts can trigger test transactions and monitor instant system responses.\n\nMulti-Hop Graph Traversal via GraphRAG\n\nWhen a transaction is flagged, TigerGraph performs rapid graph traversal across connected entity nodes (cards, devices, IPs, merchants). The system returns structured Chain-of-Thought (CoT) steps explaining why a transaction is risky.\n\nHistorical Case Matching\n\nBy querying historical fraud benchmark cases stored in the graph database, the system calculates similarity scores (e.g., 0.94 similarity to prior proxy fraud patterns) to validate new threats instantly.\n\nDynamic Scoring & Next Best Action (NBA)\n\nThe sentinel dynamically adjusts confidence scores based on graph evidence:\n\nPre-NBA Score: 78% (Requires Step-Up Auth)\n\nPost-NBA Score: 95% (Automated Action: BLOCK CARD & FILE SAR)\n\n🛠️ Key Technical Challenge: Troubleshooting Vercel Deployment\n\nDeploying a repository containing both static frontend files (index.html) and backend Python files (main.py, requirements.txt) to Vercel presented a unique engineering roadblock.\n\nThe Error\n\nUpon deployment, Vercel threw a 500 FUNCTION_INVOCATION_FAILED error.\n\nRoot Cause\n\nVercel automatically detected Python files in the root folder and attempted to build the app as a Serverless Python Function. Because no serverless wrapper was present, function execution failed.\n\nThe Solution\n\nWe decoupled the execution layer by instructing Vercel to treat the repository strictly as a static web application:\n\nCreated vercel.json for URL Rewrites:\n\nJSON\n\n{\n\n  \"rewrites\": [\n\n    { \"source\": \"/(.*)\", \"destination\": \"/index.html\" }\n\n  ]\n\n}\n\nUpdated Vercel Project Settings:\n\nSet Framework Preset to Other.\n\nOverrode Build Command and Install Command to remain empty.\n\nRedeployed without build cache.\n\nThis successfully rendered our static dashboard on Vercel while our FastAPI backend remained independently hosted on Render.\n\n🔥 Why TigerGraph?\n\nChoosing TigerGraph as our graph engine was pivotal to achieving enterprise-level fraud detection performance:\n\nUnmatched Query Speed: TigerGraph’s RESTPP endpoints execute multi-hop graph traversals with sub-second response times.\n\nGraphRAG Power: Integrating Graph-based Retrieval-Augmented Generation provides rich contextual evidence directly to AI decision workflows.\n\nScalability: Handles massive dataset connections without degradation in query latency.\n\n🚀 Conclusion & Future Roadmap\n\nThe Agentic Fraud Sentinel demonstrates how combining graph databases with modern API-first architectures enables real-time, explainable threat detection.\n\n🔗 Live Frontend Demo: [https://tiger-graph-gilt.vercel.app](https://tiger-graph-gilt.vercel.app)\n\n📂 GitHub Repository: [github.com/das09power/tiger-graph](https://github.com/das09power/tiger-graph)", "url": "https://wpnews.pro/news/building-a-real-time-agentic-fraud-sentinel-using-tigergraph-fastapi-and-vercel", "canonical_source": "https://dev.to/das09power/building-a-real-time-agentic-fraud-sentinel-using-tigergraph-fastapi-and-vercel-ifc", "published_at": "2026-09-24 17:35:35+00:00", "updated_at": "2026-09-24 17:59:22.090132+00:00", "lang": "en", "topics": ["ai-agents", "artificial-intelligence", "ai-tools", "developer-tools"], "entities": ["TigerGraph", "FastAPI", "Vercel", "Render", "Agentic Fraud Sentinel", "GraphRAG"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/building-a-real-time-agentic-fraud-sentinel-using-tigergraph-fastapi-and-vercel", "markdown": "https://wpnews.pro/news/building-a-real-time-agentic-fraud-sentinel-using-tigergraph-fastapi-and-vercel.md", "text": "https://wpnews.pro/news/building-a-real-time-agentic-fraud-sentinel-using-tigergraph-fastapi-and-vercel.txt", "jsonld": "https://wpnews.pro/news/building-a-real-time-agentic-fraud-sentinel-using-tigergraph-fastapi-and-vercel.jsonld"}}