FraudNet is best treated as a proposed architecture, not a verified product or deployed system. It combines graph traversal for connected transaction context, GraphRAG for retrieving graph and document evidence, and an agent that coordinates bounded investigation steps through tools exposed over MCP. The design can help investigators assemble evidence; it should not let a language model decide on its own that fraud occurred or authorize consequential action.
What a graph-native investigation agent does
Fraud cases often involve relationships that are difficult to see by examining one transaction at a time: accounts share devices, several accounts send funds to the same counterparty, or a sequence of transfers connects otherwise separate alerts. A graph represents entities as vertices and their relationships as edges, so a query can follow those links across multiple steps.
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GraphRAG adds retrieval to that structural view. It can bring together graph-query results and relevant text such as case narratives, typologies, rules, or policy documents. An agent can select or sequence retrieval tools and organize the results into an investigation summary. These are complementary jobs: graph queries expose relationships; document retrieval supplies context; the agent coordinates and explains. None of them, by itself, establishes intent or proves wrongdoing.
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In this article, “FraudNet” means the reference architecture below. The reviewed material does not establish a production product or deployed system with that exact name.
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A controlled FraudNet workflow
- Accept an alert and create case state. Validate required fields, assign a case identifier, and retain the original alert and relevant timestamps. The case identifier should follow each subsequent query, tool call, and decision.
- Gather connected evidence. Starting from the alert’s transaction or entity, query relationships to relevant accounts, cards, devices, merchants, IP addresses, or counterparties. Use parameterized, access-controlled tools, and return entity identifiers and source references with results.
- Analyze candidate patterns. Run approved traversals or analytics for patterns such as shared devices, transaction velocity, or repeated counterparties. Label the output as an investigative signal, including its time window and relevant data, rather than as a finding of fraud.
- Retrieve graph and documentary context. Search for relevant graph structures and documents such as prior case narratives, typologies, rules, and policy. The retrieval choice should depend on the question: a relationship question may call for a structural query, while a policy question may need document retrieval.
- Synthesize with provenance. Ask the agent to separate observed graph facts, retrieved document statements, calculated metrics, and hypotheses. Attach entity IDs, tool or query references, document identifiers, and time windows so a reviewer can reproduce the basis of each material claim.
- Evaluate policy and route action. Send candidate actions to explicit, deterministic policy logic. Keep the model’s recommendation separate from the policy engine’s result, and apply the institution’s approval requirements before any consequential action.
- Persist the case record. Store evidence, tool calls, model output, policy results and versions, reviewer decisions, and final disposition under appropriate access and retention controls.
This is a recommended architecture, not a claim that TigerGraph ships a complete regulated fraud workflow. A public FraudGraph AI project describes its own sequence of trigger handling, evidence gathering, pattern matching, GraphRAG retrieval, risk assessment, sufficiency evaluation, policy evaluation, and case update; that is a project-specific implementation example.
What TigerGraph GraphRAG documents
The TigerGraph GraphRAG repository describes an agentic engine that can select among structural graph queries, vector search, and community search, and can call configured external MCP tools. Its documentation describes retrieval results that can cite retrieved chunks and queries, as well as traces that record plans or steps and selected chunks.
Planned and reactive retrieval
- Planned: The agent outlines a retrieval plan, executes its steps, and then synthesizes an answer. This makes the intended sequence easier to inspect before execution.
- Reactive: The agent selects each next retrieval step in response to earlier results. This can adapt to what it finds, but complex investigations may take more steps and tokens.
These are documented modes in that repository, not guarantees of a particular investigation’s quality or speed. Compare them using representative cases and measure traceability, completeness, latency, and cost.
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Repository support boundary
The TigerGraph GraphRAG repository states: “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 are statements about the repository’s support boundary, not a universal statement about all TigerGraph products or commercial services. Its releases, dependencies, provider support, and license can change, so check the documentation for the exact version being deployed.
How MCP should fit into the design
MCP is a connection boundary through which an agent can invoke tools; it is not a safety certification or evidence that a tool’s result is correct. TigerGraph GraphRAG’s repository documents MCP server configuration using HTTP or stdio transport, HTTP authentication headers, allowed-tool globs, and enabled or disabled server controls.
Expose narrow investigation capabilities rather than unrestricted database access. For example, tools can accept a case ID, a seed entity, an approved query type, and a bounded time range, then return structured evidence with source IDs. Keep investigation tools read-only where possible. If case updates or other write operations are needed, make them separate tools with explicit authorization and policy checks.
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- Enable only the tools required for the investigation task, and restrict allowed tools explicitly.
- Authenticate connections and limit network exposure; choose HTTP or stdio based on deployment ownership and security boundaries.
- Validate tool inputs and outputs, including identifiers, time ranges, result sizes, and source references.
- Record which tool ran, with what parameters, under which case and identity, and what it returned.
- Do not treat retrieved content or a tool’s narrative response as authoritative without checking its provenance.
Choose retrieval and platform based on the investigation
TigerGraph is one possible graph platform, not the only way to build a graph-based fraud workflow. Google’s official BigQuery AML/fraud GraphRAG codelab demonstrates a separate approach using BigQuery’s property graph and GQL traversal, vector search, LangChain, and Gemini. It describes vector search for finding seed entities followed by graph traversal to follow multi-hop money trails.
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| Decision | Option | What to weigh |
|---|---|---|
| Graph and data platform | TigerGraph, or graph capabilities in an existing cloud warehouse such as the BigQuery approach shown in Google’s codelab | Data location, connectivity to source systems, administration, and fit with existing infrastructure. The reviewed sources do not provide an independent performance comparison. |
| Retrieval control | Fixed pipeline, planned agentic retrieval, or reactive agentic retrieval | A fixed pipeline is easier to bound; agentic selection can adapt retrieval to a question. Evaluate predictability, traceability, latency, and token use on representative cases rather than assuming one mode is best. |
| MCP transport | HTTP server or locally launched stdio process | Deployment ownership, authentication, network boundaries, tool allowlisting, and operational support. |
| Decision authority | Model recommendation, deterministic policy result, and human approval | Keep these outputs distinct. Define which role may authorize customer-impacting or irreversible actions. |
Google’s codelab estimates a 35-minute lab duration and less than $2.00 USD in pay-as-you-go service and query costs for that tutorial; its publication date is not shown on the inspected page. It requires a Google Cloud project with billing enabled. Those figures are tutorial estimates, not a forecast for a production deployment; check the codelab and current pricing before using them for planning.
Keep evidence, policy, and model output distinct
A reliable case summary should make clear what was observed and what was inferred. A shared device edge is a graph fact only to the extent that the source data supports the identity match; the conclusion that the shared device indicates coordinated fraud is an interpretation. A retrieved policy excerpt is documentary evidence of what the document says, not proof that the policy applies correctly to the case. A model-generated risk assessment is a recommendation, not a policy decision.
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Use structured evidence objects that preserve source IDs, query or tool references, document identifiers, time windows, and relevant policy versions. Require the agent to surface missing, stale, or contradictory evidence rather than smoothing over it. Investigators should be able to reproduce the evidence trail without relying on the generated narrative alone.
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False positives and customer impact
A risk score or graph pattern is a triage signal, not a verdict. Blocking a transaction, closing an account, filing a report, or taking another consequential step should follow defined institutional policy and approval controls, not a model’s prose alone.
Entity resolution and data quality
Shared identifiers can be stale, reused, or incorrectly joined. Preserve the source and confidence of important relationships, test edges against originating systems, and make uncertainty visible to reviewers. A plausible graph path can still be built from a bad match.
Tool access, privacy, and security
Financial, identity, and device data require access controls, data minimization, retention rules, and secrets management. Separate read-only investigation from write actions, and review applicable legal and compliance requirements for the institution and jurisdiction. The architecture alone does not determine those requirements.
Evaluation and operational readiness
Evaluate the system on representative historical cases with documented labels and controls against data leakage. Track evidence completeness, unsupported claims, investigator correction rates, latency, cost, and task outcomes. Establish baselines and review ground truth before interpreting results; project sample counts alone do not establish effectiveness or production readiness.
What public project counts do—and do not—show
The independent FraudGraph AI repository describes a TigerGraph, LangGraph, and hybrid GraphRAG project built for the Hacker House Goa 2026 challenge. It reports ten MCP investigation tools, deterministic policy rules labelled R1–R10, an eight-node bounded state machine, and a twenty-case benchmark. These are claims about that project’s implementation and evaluation design, not features established for TigerGraph GraphRAG or a production bank system.
The repository reports 5,565 closed cases, five fraud typologies, ten regulatory guidelines, and ten policy rules, and separately reports 5,590 indexed documents. These are project-reported corpus counts, not measures of representativeness, label quality, precision, recall, loss reduction, or regulatory compliance. A twenty-case benchmark is not industry evidence; any performance claim would require reviewing its methodology, ground truth, data split, baselines, and limitations.
Deployment considerations
TigerGraph GraphRAG’s repository setup documentation describes Docker Compose or Kubernetes deployment, a TigerGraph database version prerequisite, and an LLM-provider API key. It warns that rebuilding a graph can incur costs for embedding and data-structure generation. These details are version-sensitive; use the current setup documentation for prerequisites, commands, supported providers, and licensing rather than relying on a copied setup recipe.
Before production, also decide who owns the graph schema and entity resolution, how source data is refreshed, how tool credentials are managed, what is retained in case records, and how changes to prompts, retrieval configuration, graph queries, and policy versions are reviewed. Treat those as system controls that need operational ownership, not incidental implementation details.
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