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That division is the project’s central design idea. As Team GOA-T put it in its September 25, 2026, DEV Community write-up, “Let the graph gather evidence, let deterministic code enforce policy, and let the LLM explain the result.” The team’s reported benchmark is a small project evaluation, not proof of production fraud-detection performance.
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What problem is Sentinel designed to solve?
A transaction risk score can flag activity without showing an investigator why it is concerning, what other accounts or devices are connected to it, or whether a similar pattern has appeared in earlier cases. Sentinel is designed to assemble that context into an investigation: retrieve relevant relationships and historical evidence, check for fraud patterns, apply policy, and present an explanation for an analyst.
It is therefore more than a score or a chatbot over a database. The project combines graph retrieval, detector logic, historical case search, policy controls and language-model synthesis in a bounded investigation workflow.
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How does the investigation move through the system?
Team GOA-T describes eight parts: 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, LLM synthesis and an interactive analyst web cockpit. An alert or analyst escalation starts the process; evidence is gathered, policy determines permitted actions and approvals, and the system can seek more evidence before producing an explanation and writing case and audit artifacts.
- Start with a trigger. An alert or analyst escalation opens an investigation.
- Retrieve connected evidence. Purpose-specific investigation tools query graph relationships and case history.
- Check signals and legitimacy context. Deterministic detectors and the legitimacy checklist assess evidence, including whether an apparent anomaly has a plausible explanation.
- Apply policy. Rules determine which actions and approval routes are permitted; the workflow may gather more evidence if it could affect that decision.
- Explain and record. The LLM synthesizes the evidence into case explanations or narratives, while the workflow writes case and audit artifacts.
The project describes stopping conditions based on configured fraud-probability thresholds when sufficient independent evidence exists, customer verification, authentication, or reaching a point where further graph traversal cannot change the permitted policy action. Those thresholds are project configuration examples, not general operating standards.
What does the graph reveal that a single transaction may not?
TigerGraph represents relationships among customers, cards, transactions, devices, billing regions, email domains and historical cases. For example, an investigation can traverse from a suspicious transaction to its device, find other cards associated with that device, and then locate closed cases connected to those cards. That can expose a pattern spanning several entities rather than treating each transaction as an isolated record.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteA shared device is a lead, not a verdict. The project authors explicitly caution that “A shared device alone does not prove fraud.” Investigators need to assess the connection alongside other signals and the customer’s history; shared devices can have legitimate explanations.
For controlled access, the team exposes purpose-specific tools through MCP, including queries for a customer’s cards, card history, shared devices and prior cases, plus a case-write operation. The stated design lets the agent request defined investigation tasks without constructing arbitrary graph queries or needing to understand the full graph schema.
How does Sentinel use GraphRAG and past cases?
Sentinel combines two kinds of retrieval: semantic similarity search over historical cases and graph search over related entities. The first can surface cases with similar descriptions or patterns; the second can surface cases connected through cards, devices or other graph relationships. Together, they aim to give investigators both similarity context and relationship context.
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In its implementation description, Team GOA-T reports using sentence-transformers/all-MiniLM-L6-v2 to create a 384-dimensional vector for each case. It also reports giving graph-adjacent cases an additional +0.15 priority boost during relevance fusion. These are implementation choices reported for this project, not established best-practice settings.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →The team says its historical index includes 5,565 closed cases, approximately 900 of them cleared cases. Cleared cases can supply negative evidence: the article’s example is a recurring merchant dispute that prior cleared cases helped contextualize as potentially legitimate. These counts and the example are project-reported; the article does not independently audit the underlying case data.
Which fraud signals does it check?
The project lists eight deterministic detector families. A detector identifies a pattern to investigate; it does not, by itself, establish that a transaction is fraudulent.
- Recurring merchant behavior
- Card testing
- Shared devices
- New devices
- Out-of-region activity
- Card-not-present transaction bursts
- Account-takeover signals
- Shared-region clusters
The team’s examples emphasize checking signals against customer history and considering benign explanations. A regional change may reflect travel, recurring merchant activity may reflect a subscription, and a shared device may have a legitimate connection to multiple customers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who decides whether to block a card or escalate a case?
In Sentinel’s stated design, deterministic policy code—not the LLM—has authority over permitted actions and approval routing. The LLM can summarize evidence, explain detected patterns and triggered rules, and draft case or regulatory narratives. Team GOA-T says, “The LLM is deliberately not allowed to decide whether a card should be blocked or which approval route should be assigned.”
The article describes policy examples such as requiring verification when a signal is weak and isolated, escalating uncertain cases with high exposure, and restricting broad card blocking. This separation is a design claim, not independent evidence that the implementation is correct or suitable for a regulated production environment. Policy quality, data controls, auditability and operational safeguards would still need to be assessed for a real deployment.
What did the project benchmark establish—and what did it not?
In its September 2026 project article, Team GOA-T reports using 590,742 IEEE-CIS/Vesta transactions and 5,565 closed historical cases. It also reports approximately 1.45 million transaction vertices in the live TigerGraph graph. Those figures describe the project’s data and graph as reported by its authors; they do not establish results on a bank’s live traffic.
The reported benchmark covered 20 cases. Team GOA-T says all 20 passed benchmark validation and reports a 100% policy-validation pass rate. It also reports an approximately 1.2-second fast-path investigation in its benchmark environment. That runtime is a project measurement, not a production service-level agreement.
The team explicitly warns that these project benchmark results “should not be interpreted as production fraud-detection performance.” Twenty validation cases do not establish general detection accuracy, effectiveness across populations or operating conditions, or performance at a financial institution’s production scale. The reported policy pass rate is likewise limited to the project’s stated validation set.
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What should a reader take away from Sentinel’s design?
- Use the graph to connect evidence. Linked cards, devices and cases can reveal context that an isolated transaction view misses, but a relationship is not proof of wrongdoing.
- Retrieve both similar and connected cases. Semantic search and graph traversal answer different questions; including cleared cases can help represent legitimate explanations.
- Keep action authority bounded. Sentinel assigns policy and approval decisions to deterministic rules while using an LLM for explanation and narrative drafting.
- Interpret the metrics narrowly. The published figures describe Team GOA-T’s project data and 20-case benchmark, not independently validated production performance.
The project article is a detailed account of one team’s architecture, not an independent evaluation, an official TigerGraph product specification or a regulatory standard. It does not establish current software pricing, geographic availability, data-protection controls or whether the demo remains available.
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