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You can build a graph-backed fraud investigation agent by having TigerGraph retrieve connected evidence, deterministic policy rules assess that evidence, and Gemini explain the resulting case for a human reviewer. A public project called FraudSight AI describes this architecture using TigerGraph Savanna Cloud, LangGraph, Gemini, and tigergraph-mcp. It is a 2026 hackathon prototype, not proof of production accuracy or regulatory compliance. Treat “autonomous” as automating evidence gathering and case preparation—not authorizing an AI model to make consequential decisions on its own.
What the agent should do—and what it should not
A fraud investigation agent should turn an alert or referral into a traceable, reviewable case. In the FraudSight AI project’s stated design, an investigation can begin with a high-risk alert, a customer report, or an analyst referral. The agent gathers graph evidence, assesses the case, requests or simulates more evidence if needed, applies policy rules again, and may draft a suspicious activity report (SAR) when the project’s configured thresholds are met. It also writes case findings and actions to graph memory. These are descriptions of the prototype’s intended workflow, not independently validated operating results. The project repository describes the implementation.
Keep three responsibilities distinct:
- Evidence retrieval: TigerGraph finds connected transactions, entities, devices, and prior cases relevant to the alert.
- Policy assessment: deterministic rules map retrieved signals to scores, thresholds, and recommended next steps.
- Explanation: Gemini turns structured evidence and policy results into a readable case narrative. It should not be treated as an independent verifier of fraud.
This separation limits the risk that a fluent explanation will be mistaken for evidence or policy. The prototype describes auto, L1, and L2 approval routing, but the repository description does not establish those labels as universal standards or define a production-ready approval policy. Specify what each route means in your own institution, and require human approval before customer-impacting action or regulatory filing.
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Fraud investigations often depend on relationships that are difficult to see in a single transaction or account record. A graph can connect customers to transactions, cards, device fingerprints, email clusters, billing regions, and investigation records. Traversing those links can surface shared devices, unusual transaction sequences, or connections to previously closed cases. The graph does not prove intent; it supplies relationships for investigators and policy logic to evaluate.
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| Layer | Role in the investigation | FraudSight AI implementation described |
|---|---|---|
| Case orchestration | Tracks investigation state, routes work, and coordinates retrieval, assessment, review, and persistence. | LangGraph state-machine framework. |
| Graph evidence | Retrieves linked entities, transactions, patterns, and historical cases. | TigerGraph Savanna Cloud v4.2.5, accessed through tigergraph-mcp. |
| Policy assessment | Applies explicit, repeatable rules to evidence and determines scores or recommended actions. | Policy rules are part of the described workflow; the repository should be consulted for its particular implementation. |
| Language model | Produces readable explanations from retrieved context and assessment results. | Gemini 2.5 Flash. |
| Embeddings | Support similarity-oriented retrieval, such as finding semantically related case material when configured. | gemini-embedding-001. |
| Case memory | Records investigation cases, findings, and actions for later review and retrieval. | Writes results back into graph memory. |
The project describes a GraphRAG-style workflow: graph traversal supplies structural relationships, historical case similarity supplies precedents, and policy retrieval supplies relevant rules before context is passed to Gemini. Keep the evidence sources identifiable in the resulting case. A summary should let a reviewer trace each important claim to the records, graph relationships, and policy version that support it.
How to build the investigation workflow
Use the repository as an implementation reference, then define the controls and policies for your own environment. The steps below describe the architecture without assuming undocumented commands or query syntax.
- Define the case inputs and scope. Specify which alerts, customer reports, or analyst referrals can start an investigation, what identifiers they contain, and which data sources the agent is authorized to access. Record the originating alert and its timestamp as case context.
- Model the relationships you need to investigate. Represent relevant entities and links—for example, customers, transactions, cards, device fingerprints, email clusters, billing regions, and investigation records. Define what each relationship means, how it is sourced, and when it was observed; do not infer that a shared attribute alone establishes fraud.
- Implement bounded evidence retrieval. The FraudSight project lists GSQL queries for customer baselines, card windows, small-authorization sequences, new-device proxies, out-of-region behavior, recurring charges, similar closed cases, and device neighbors. Use queries like these to collect specific evidence for a case, preserving the underlying records and relationship paths rather than passing only an unexplained score to the model. The repository describes these query categories; adapt them to your schema and policies.
- Orchestrate state and evidence collection. Use the agent state machine to represent stages such as intake, graph retrieval, policy assessment, additional-evidence request, reassessment, human review, and case persistence. Set explicit conditions for retrying, stopping, or escalating. Do not let an unclear or missing result silently become a negative finding.
- Apply deterministic policy rules. Define the signals, scoring or thresholds, rule versions, and permitted recommendations outside the language model. Keep policy outcomes reproducible: store the inputs and applicable rule version alongside each result. A threshold recommendation is not itself approval to restrict an account or file a SAR.
- Generate a grounded explanation. Give Gemini structured findings, their evidence paths, and policy results. Ask for a concise explanation that distinguishes observed facts from inferences, identifies missing evidence, and avoids presenting a similarity or anomaly as proof. Validate the output against the source records before using it in a decision.
- Route actions to a reviewer. Establish who can approve each level of action, what information they need, and how disagreement or insufficient evidence is handled. Keep human review in the path for customer-impacting decisions and SAR submission. A draft can support an investigator; it does not establish that a filing is complete or compliant.
- Persist the case and audit trail. Store the case, findings, evidence references, policy version, model-generated text, reviewer decisions, and actions taken. Record enough context to reconstruct why a recommendation was made, who approved it, and what changed during review.
What to verify before running the prototype
The repository declares Python 3.11–3.14, Node.js 18 or 20, a TigerGraph cloud instance, and Gemini API credentials as setup requirements. It names TigerGraph Savanna Cloud v4.2.5. These are the project’s stated requirements, not a guarantee that each version remains supported or compatible today. Check current platform and library compatibility before installing or deploying. The repository’s setup instructions are the source for these project-specific details.
- Confirm that graph schema, query access, and credentials expose only the data the investigation needs.
- Check how missing, stale, conflicting, or inaccessible records are represented; they should not be silently treated as evidence against or in favor of a customer.
- Test policy behavior against representative cases, including borderline and incomplete evidence, and have accountable policy owners approve thresholds.
- Make model inputs, outputs, and retained case data conform to your organization’s security, retention, and jurisdiction-specific requirements.
- Review human routing, auditability, and recovery behavior before connecting the workflow to actions that affect customers or create filings.
What the platform claims do—and do not—establish
In a March 4, 2025 announcement, TigerGraph described hybrid graph/vector search for GraphRAG and fraud or AML use cases. The announcement reported “5.2x faster vector searches with 23% higher recall than competitors” while using “22.4x fewer resources,” as well as “6x faster indexing.” These are TigerGraph’s vendor-reported comparisons; the announcement does not independently establish the comparison methodology, and the figures should not be treated as a forecast for a particular fraud workload. TigerGraph’s announcement also listed Community Edition specifications of 16 CPUs, 200 GB of graph storage, and 100 GB of vector storage. Those are dated 2025 specifications, so verify current availability and terms rather than assuming they still apply.
Platform performance claims do not demonstrate investigation quality. The sources describing FraudSight AI do not independently establish fraud-detection accuracy, fewer false positives, production readiness, regulatory effectiveness, or performance on live financial data. Treat any prototype case counts, graph size, workflow-completion figures, or benchmark results as project-reported rather than independent evidence.
Graph technology is not automatically the right choice for every workload. TigerGraph’s page for a September 2020 Graph + AI World session quotes Dan McCreary of Optum: “You gotta use the right tool for the right job. If you only have one user in the system, you are just doing single analytics and you have time to load terabytes of data into your RAM, Spark might be an option. But it is only going to fit about 2-3% of the use cases that we use TigerGraph for.” This is a speaker’s dated perspective, not a current benchmark or general rule for architecture selection. The session page provides the event context.
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Human review is an architectural control, not a final checkbox. A related TigerGraph hackathon article describes the principle as “the system recommends, it doesn’t unilaterally act,” and lists policy-threshold tuning and audit logging as future work for that project. That is a useful design example, not a binding standard or evidence that the FraudSight prototype has those controls fully implemented. The related hackathon article describes its approach.
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For each recommendation, preserve the chain from source data to action: the records retrieved, graph paths and query context, applicable policy version, model-generated explanation, reviewer decision, and any resulting action. Give reviewers a way to see uncertainty and missing evidence rather than forcing a confident-sounding conclusion. Keep the model from bypassing policy gates or converting its own prose into authorization.
Before considering operational use, evaluate the system with representative data under controlled conditions and define success, error, escalation, and rollback criteria. The available project descriptions do not establish those results. Deployment decisions also depend on access controls, latency, model quality and cost, human approval design, and jurisdiction-specific obligations; the cited sources provide no controlled vendor comparison or current pricing from which to rank alternatives.
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