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SENTINEL is a Team GOA-T project built for the TigerGraph Agentic Fraud Investigation challenge. It combines graph relationships, past cases, deterministic fraud checks and policy rules to help an analyst investigate an alert and produce a governed recommendation. The team’s design principle is: “Let the graph gather evidence, let deterministic code enforce policy, and let the LLM explain the result.”
That describes a project architecture, not a generally available TigerGraph product or independently validated commercial fraud system.
What SENTINEL is designed to do
A conventional risk score can flag a transaction, but it does not by itself explain how the transaction relates to other accounts, devices or earlier investigations. SENTINEL’s described purpose is to assemble that connected context and present it as an investigation workflow for a human analyst.
The project article describes eight components: live TigerGraph Savanna Cloud with an MCP client; hybrid GraphRAG memory; eight deterministic fraud detectors; a legitimacy checklist; a deterministic policy engine; a dynamic next-best-action engine; an LLM synthesis layer; and an interactive analyst web cockpit. The inputs can include risk-score alerts, disputes and analyst escalations. The graph context includes customer cards, card history, shared devices and prior cases.
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How an investigation moves through the system
- A case begins. A risk-score alert, dispute or analyst escalation starts the investigation.
- The system gathers connected evidence. TigerGraph queries and MCP tools retrieve relevant account, card, device, transaction and prior-case context.
- Deterministic checks assess patterns and counter-signals. The described workflow uses eight fraud detectors and a legitimacy checklist, so an investigator can consider both suspicious links and plausible benign explanations.
- Historical context is retrieved. Hybrid GraphRAG memory brings in relevant closed cases and policy or typology context.
- Policy code recommends an action and approval route. A deterministic policy engine governs the recommendation rather than leaving the decision to generated text.
- The LLM explains the findings. It turns structured findings into a human-readable explanation or regulatory narrative. The system can also write investigation memory back to the graph.
What questions the graph and workflow address
Are there suspicious links to other accounts?
The graph can help an analyst check whether a card has interacted with accounts already associated with suspicious activity, and whether a device is shared across customers or appeared in prior fraud cases. These relationships add context beyond the transaction’s individual risk score.
Is an unusual transaction actually legitimate?
The project’s legitimacy checklist is intended to help distinguish suspicious behavior from explanations such as legitimate travel or recurring subscriptions. The point is to evaluate counter-signals alongside risk indicators, rather than treating an alert as proof of fraud.
What action is appropriate, and who must approve it?
The team says the deterministic policy engine determines the recommended action and approval route. The LLM is not supposed to choose whether a card is blocked or which approval route applies. In the project article’s words, “The LLM is deliberately not allowed to decide whether a card should be blocked or which approval route should be assigned.” This is the team’s description of its implementation, not an independent security audit.
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Team GOA-T’s September 25, 2026 project article reports an evaluation using 590,742 IEEE-CIS/Vesta transactions, 5,565 historical closed cases and 20 benchmark cases, with a live TigerGraph graph containing approximately 1.45 million transaction vertices. The team reports that all 20 benchmark cases passed and that policy validation had a 100% pass rate. These are the project authors’ reported results; the available material does not establish independent replication, an external audit or production performance.
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How SENTINEL relates to TigerGraph’s fraud-investigation positioning
SENTINEL is a specific Team GOA-T implementation, not a generic name for TigerGraph’s product. TigerGraph separately promotes fraud-investigation agents that analyze “connected transactions, entities, and behavioral patterns.” That vendor positioning describes a related use case, but it does not validate SENTINEL’s reported results.
For anyone assessing a fraud-investigation design, useful questions include how many relationship types and graph hops it examines, how it retrieves and represents historical cases, which decisions are fixed by policy code, how it handles uncertainty and benign explanations, what actions require human approval, and whether evaluation controls for data leakage and has been independently replicated. The project description provides architectural detail, but does not supply a head-to-head product comparison.
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