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FraudNet is best understood as a proposed architecture, not a verified production product: a bounded AI agent can coordinate graph queries, document retrieval, and case workflows to help investigators examine connected fraud signals. TigerGraph GraphRAG documents agentic retrieval and MCP tool connections, but neither an LLM summary nor a graph pattern establishes that fraud occurred. Keep evidence gathering, deterministic policy decisions, and consequential actions distinct.
What a graph-native investigation agent does
Fraud investigations often depend on relationships that are hard to see one record at a time: a transaction may connect an account, card, device, merchant, IP address, or counterparty, and those entities may link to other transactions or cases. A graph query can traverse those connections across multiple hops. Retrieval over case narratives, typologies, rules, and policy documents can add context about how similar patterns were handled.
In this architecture, the agent coordinates bounded investigation steps and presents the resulting evidence for review. Graph traversal supplies relationships; GraphRAG retrieves relevant graph and document context; an MCP server exposes selected tools to the agent. The model can summarize findings or suggest next steps, but it should not independently determine fraud or execute consequential actions.
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The following is a recommended architecture, not a claim that TigerGraph ships a complete regulated fraud workflow. Give each case a durable identifier and preserve the inputs, evidence, and decisions needed to reproduce how it was handled.
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Accept the alert and establish case state
Validate the alert’s required fields, record its source and timestamps, assign a case identifier, and retain the original alert. Treat the alert as the starting point for an investigation, not as a confirmed finding.
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Gather connected evidence
Use parameterized, access-controlled graph tools to retrieve the seed transaction and linked entities—such as accounts, cards, devices, merchants, IP addresses, and counterparties. Return source-system identifiers and relevant time windows with results so an investigator can inspect their origin.
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Analyze candidate patterns
Run defined traversals or graph analytics for signals such as shared devices, transaction velocity, or repeated counterparties. Label these as candidate patterns. Shared identifiers may be stale, reused, or incorrectly joined, so validate important edges against their source systems and expose entity-resolution uncertainty.
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Retrieve graph and documentary context
Use GraphRAG to find relevant structural graph results and documents, such as prior case narratives, typologies, rules, and policy material. A graph query, vector search, or community search may be more or less useful depending on the question; the retrieval method should be inspectable rather than treated as a universal answer.
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Synthesize findings with provenance
Require the agent to distinguish observed graph facts, documentary statements, calculated metrics, and hypotheses. Attach entity IDs, query or tool references, source documents, and time windows to claims. It should identify missing or contradictory evidence rather than smoothing uncertainty into a confident narrative.
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Evaluate policy and route actions
Run explicit, versioned policy logic separately from model-generated recommendations. Require the institution’s defined approval checks for consequential actions, including interventions that could affect customers. A model’s suggested action is not the policy engine’s decision.
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Persist the case record
Record evidence, tool calls, model outputs, policy results, reviewer decisions, and final disposition under appropriate access controls and retention rules. This makes later review more than a replay of the model’s final prose.
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What TigerGraph GraphRAG documents
The TigerGraph GraphRAG repository describes an agentic engine that can select among structural graph queries, vector search, and community search, as well as configured external MCP tools. It also describes citing retrieved chunks and queries and tracing retrieval steps. Those are repository-documented capabilities; verify the current release, dependencies, providers, and license for a specific deployment.
The repository describes two agent styles:
| Style | How it works | Practical trade-off |
|---|---|---|
| Planned | The agent outlines a retrieval plan, executes its steps, then synthesizes an answer. | The plan makes the intended sequence visible before execution, but a plan may not adapt as readily to unexpected results. |
| Reactive | The agent chooses each next retrieval step in response to earlier results. | It can adapt to findings as they arrive, but complex investigations may take more steps and tokens. |
The repository says its administration traces record plans or steps and which retrieved chunks were selected. Traces help with inspection; they do not, by themselves, validate the accuracy of retrieved evidence or the agent’s interpretation.
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The repository’s support statement is specific to that project: “Supported Backend: TigerGraph is the only Vector and Graph DB supported in this project.” It also says hybrid search is officially supported, while other retrieval methods and the agentic chat engine are provided as-is for self-service use. These statements describe the repository’s support boundary, not every TigerGraph product or commercial service.
How MCP should expose investigation tools
MCP is a connection boundary between an agent and tools, not a guarantee that a tool is safe, authorized, or correct. TigerGraph GraphRAG’s repository documents MCP configuration for HTTP or stdio transport, HTTP authentication headers, allowed-tool globs, and enabling or disabling servers.
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|---|---|
| HTTP | Plan server ownership, authentication, network exposure, and which tools an agent is allowed to call. |
| stdio | Plan how the local process is launched, managed, and restricted, and who owns its runtime environment. |
Expose only the tools required for the investigation, authenticate connections, restrict tool names with an allowlist, and test returned evidence. Keep read-only investigation tools separate from tools that update cases or trigger interventions. If write tools are needed, place them behind explicit policy checks and approval controls.
Choosing a retrieval and platform approach
TigerGraph is not the only way to build a graph-based fraud workflow. Google’s official codelab demonstrates a separate AML and fraud approach using BigQuery’s property graph and GQL traversal, vector search, LangChain, and Gemini. It describes vector search finding seed entities before graph traversal follows multi-hop money trails.
| Choice | What to assess |
|---|---|
| TigerGraph or a graph capability in an existing cloud warehouse | Data location, connectivity to source systems, administration, and fit with the systems already in use. |
| Fixed pipeline, planned retrieval, or reactive retrieval | Predictability, adaptability, traceability, and token and latency overhead on representative cases. |
| HTTP or stdio MCP connection | Deployment ownership, authentication, network boundaries, allowed-tool restrictions, and operational support. |
| Model-generated suggestion or deterministic policy decision | Whether policy is applied reproducibly and whether human approval is required before irreversible or customer-impacting action. |
The Google Cloud codelab estimates a 35-minute lab and less than $2.00 USD in pay-as-you-go service and query costs for its tutorial; its publication date is not shown on the inspected page. Those are tutorial estimates, not a production cost forecast. The codelab requires a Google Cloud project with billing enabled, and costs should be checked against current service pricing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What an independent project example shows—and does not show
The FraudGraph AI repository describes a TigerGraph, LangGraph, and hybrid GraphRAG project built for the Hacker House Goa 2026 challenge. Its authors report ten MCP investigation tools, deterministic rules labelled R1–R10, and a workflow that gathers graph and document evidence before policy evaluation. The repository also describes an eight-node bounded state machine and a twenty-case benchmark. These are features and evaluation-design details of that project, not established properties of FraudNet or TigerGraph GraphRAG generally.
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How to evaluate a proposed implementation
Evaluate the full investigation task, not just whether a model can produce a plausible summary. Use representative historical cases with documented labels and controls against information leakage. Compare fixed, planned, and reactive retrieval on the same cases where those options are available.
- Evidence completeness: Were relevant entities, transactions, and documents found and cited?
- Evidence fidelity: Can each material statement be traced to a graph result, source document, calculation, or policy version?
- Unsupported claims: How often does the agent state a claim that the cited evidence does not support?
- Investigator correction: What evidence or interpretation do reviewers add, reject, or correct?
- Operations: What are the latency and cost per case, and how many cases reach a useful disposition?
- Decision controls: Are policy outcomes reproducible, and are approvals recorded before consequential actions?
The cited project materials do not provide independent comparative performance measurements. A sample corpus or small benchmark is not evidence of institution-wide accuracy, regulatory compliance, or production readiness.
Quick Recap
Security, privacy, and operational limits
- False positives and customer impact: Treat scores and graph patterns as triage signals, not verdicts. Separate investigation from blocking, account closure, reporting, or other consequential outcomes.
- Misattributed evidence: Preserve identifiers, provenance, time windows, and uncertainty, and make missing or conflicting evidence visible.
- Tool access: Use least privilege, authentication, allowlists, and distinct read and write capabilities. Log calls and validate tool outputs.
- Sensitive data: Apply access controls, data minimization, secrets management, and retention rules to financial, identity, and device records. Legal and compliance requirements depend on the institution and jurisdiction; these architecture sources do not settle them.
- Changing dependencies: Confirm the GraphRAG version, license, supported providers, setup requirements, and deployment instructions against the current repository documentation. Its setup materials describe Docker Compose or Kubernetes deployment, a TigerGraph version prerequisite, and an LLM-provider API key; they also warn that rebuilding a graph can incur embedding and data-structure generation costs.
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