GraphGuard - Explainable Fraud Investigation Agent
GraphGuard is an explainable fraud investigation agent built with FalkorDB, Python, FastAPI, and Docker. It explores connected accounts, identifies potential fraud rings, examines circular money transfers, and records investigation actions for auditing. Using synthetic data, GraphGuard demonstrates how graph-based reasoning can help fraud analysts understand suspicious relationships and make more traceable decisions.
GraphGuard: Building an Explainable Fraud Investigation Agent with FalkorDB
Introduction
Financial fraud is not always easy to detect. Suspicious activity can be spread across multiple accounts, shared devices, identities, and money transfers. When each transaction is examined separately, important connections may remain hidden.
During Graph Hacks: Context for AI Agents, I built GraphGuard, a prototype that demonstrates how graph-based reasoning can help investigate suspicious financial activity.
Project repository: https://github.com/harshakavya-10/GraphGuard
The Problem
Fraud investigation requires more than identifying an unusual transaction. Investigators may need to understand how accounts are connected, whether several accounts form a potential fraud ring, and whether money moves through circular paths.
GraphGuard explores how connected data can help reveal these relationships and make investigation results easier to understand.
Why FalkorDB?
FalkorDB is the primary graph database used in this project. Instead of treating every account and transaction as an isolated record, the application represents entities and their relationships in a graph.
The graph stores entities such as accounts, people, devices, IP addresses, alerts, cases, and actions. Graph queries and relationship traversals help investigate connections between flagged accounts, explore potential fraud rings, and examine circular money movements.
This graph-based approach provides context for the investigation agent and helps connect its decisions to the relationships found in the data.
How GraphGuard Works
The project follows an investigation workflow:
- A user selects a flagged account for investigation.
- GraphGuard explores connected accounts and relevant relationships in FalkorDB.
- The application examines potential fraud-ring connections and money-transfer cycles.
- The agent uses the investigation results to determine whether the evidence supports a demonstration action or whether the available evidence is insufficient.
- Investigation actions and related case information are recorded in the graph to support auditing.
The application also provides a visual representation of graph relationships so users can explore how entities are connected.
Building the Prototype
I developed GraphGuard with a web interface, a FastAPI backend, and FalkorDB running through Docker Compose. The project uses synthetic data so that the investigation workflow can be demonstrated without using private financial information.
Testing different scenarios helped demonstrate that suspicious connections do not always justify an action. For example, one scenario demonstrates a freeze action and case creation, while other scenarios return insufficient evidence for an action.
These are prototype demonstrations, not validated real-world fraud predictions.
Challenges and Learnings
One of my key learnings was understanding how a graph database can provide context to an AI agent. Relationships between entities can help an agent investigate beyond individual records and produce results that are easier to trace.
I also learned about graph modeling, querying connected entities, visualizing relationships, recording investigation actions, and packaging a multi-service application using Docker Compose.
The project reinforced the importance of explainability: an investigation result is more useful when users can examine the relationships and evidence behind it.
Limitations and Future Improvements
GraphGuard currently uses synthetic data and is a prototype. Before real-world use, it would need evaluation on appropriately authorized data, stronger authentication and access controls, validated fraud-detection logic, and careful testing to reduce false positives.
Future improvements could include richer graph algorithms, more comprehensive evidence explanations, improved case management, and secure deployment for authorized users.
Conclusion
GraphGuard demonstrates how FalkorDB can serve as the foundation for an agent that investigates connected financial data. By combining graph queries, an investigation workflow, visualization, and an audit trail, the project explores a practical way to make suspicious relationships easier to investigate and explain.
Building this prototype during Graph Hacks helped me understand how graph databases can move AI agents beyond isolated records toward context-aware investigation.
Project: GraphGuard — Explainable Fraud Investigation Agent
Source code: https://github.com/harshakavya-10/GraphGuard
Technology: FalkorDB, Python, FastAPI, Docker Compose, and a web-based interface.
Data: Synthetic demonstration data.
AI assistance disclosure: AI coding assistance was used during development. The implementation and its limitations should be reviewed and understood by the project author.
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