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Agentic Fraud Investigation on TigerGraph

13 posts in this trend in the last 7 days • Active about 8 hours ago

Saadhan P

Sep 23

TigerGraph Fraud Sentinel

#agents #ai #machinelearning #security

5 min read

Rishi

Sep 23

Building an Autonomous Fraud Investigation Agent with TigerGraph, GraphRAG & Case Memory

#tigergraph #ai #machinelearning #frauddetection

4 min read

Hemanth sai Govindu

Sep 25

Autonomous Fraud Investigation Agent: Scaling Graph Intelligence with TigerGraph & LangGraph

#agents #ai #database #machinelearning

3 min read

Alec

Sep 24

🔍 FraudLens: Building an Agentic Fraud Investigation System with TigerGraph

#agents #ai #database #machinelearning

8 min read

Sep 24

Finding fraud rings a risk model can't see: an agentic investigator on TigerGraph

#agents #ai #data #machinelearning

6 min read

Sep 25

An Agent That Knows When It Doesn't Know - Fraud Investigation on TigerGraph

#agents #ai #database #machinelearning

5 min read

Sep 24

Building FraudAgent: Autonomous Financial Crime Intelligence with TigerGraph & LangGraph

#agents #ai #llm #machinelearning

5 min read

Sep 24

Sentinel: an agentic fraud investigator on TigerGraph

#ai #tigergraph #python #machinelearning

10 min read

Shaurya Upadhyay

Sep 24

I Didn’t Want AI to Decide Fraud. I Wanted It to Investigate It.

#agents #ai #database #machinelearning

9 min read

Anurag Kumar Singh

Sep 24

LLM Reasons, Policy Engine Decides: Autonomous Agentic Fraud Defense on TigerGraph

#tigergraph #ai #machinelearning #fintech

10 min read

spideyy

Sep 25

Building FraudGraph Agent: Autonomous Fraud Investigation & Policy-Compliant Next-Best Action with TigerGraph

#tigergraph #ai #python #machinelearning

8 min read

Sep 24

Building an Agentic Fraud Investigation Agent with TigerGraph, MCP, and GraphRAG

#ai #tigergraph #graphdatabase #machinelearning

9 min read

Syed Darain Qamar

Sep 25

The fraud model that was right about everything except fraud

#database #datascience #machinelearning #security

6 min read

Trend Analysis

Developers are building autonomous AI agents integrated with TigerGraph knowledge graphs to investigate financial fraud using the IEEE-CIS dataset. These systems combine LLM reasoning with strict policy engines and graph traversals to gather evidence, trace fraud rings, and know when to ask for human clarification before making decisions.

Core Questions Under Debate:

How can LLMs effectively query and traverse graph databases like TigerGraph for fraud detection?
What architectural patterns ensure an AI agent defers final financial decisions to a deterministic policy engine?
How do agentic systems calibrate confidence and know when they lack sufficient evidence to investigate a fraud ring?

First detected: September 25, 2026

Last updated: about 8 hours ago

Algorithm: Vector Leader Clustering (threshold: 82%)

About this trend

This trend groups posts from the last 7 days that discuss similar concepts, technologies, or ideas.