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FraudLens is a hackathon-built fraud investigation system, not a proven bank deployment or independently validated fraud detector. Created for Goa Hackerhouse 2026, it uses TigerGraph to trace relationships around suspicious transactions, assemble evidence, and support a policy-controlled decision about what to do next. Its central idea is that a risk score should open an investigation—not settle it.
What FraudLens investigates
According to Team TrustMeBro’s project article, FraudLens was built around a bank transaction dataset, closed investigation history, and 20 open alerts. For each alert, it follows the transaction’s connections to a card and customer, then gathers transaction history and surrounding graph context.
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The system is designed to investigate practical questions such as whether a purchase fits the customer’s usual spending, whether a device appears on other cards, how similar past cases ended, and whether there is an innocent explanation. It also considers whether requesting more evidence—such as contacting the customer—could change the recommended action.
How the architecture divides responsibility
The project assigns different jobs to separate components. TigerGraph and GSQL store and traverse relationships; a deterministic policy engine applies action rules and approval routes; a LangGraph agent manages investigation state and evidence-gathering choices; and an LLM helps with planning and written explanations.
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That division is a stated design choice, not an independently audited security guarantee. The team says the LLM cannot approve an action and that the API checks policy requirements again. The project describes investigation tools as read-only and says the model cannot directly call the write path.
Graph data and temporal boundaries
The graph includes Customer, Card, and Transaction vertices, along with shared-origin entities such as DeviceProfile, EmailDomain, and BillingRegion. It also represents investigation memory using FraudCase, Evidence, EvidenceRequest, and ActionDecision entities. A NEXT edge links each card’s transactions in time order.
The project reports approximately 26 installed GSQL queries spanning context gathering, pattern detection, relationship discovery, graph algorithms, case memory, and write-back. Each read query requires a cutoff time, so an investigation is intended to exclude transactions occurring after that cutoff.
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Evidence, conclusions, and write-back
FraudLens keeps evidence distinct from agent conclusions. The team says each evidence item records its source and the query that produced it, while conclusions are stored separately. After a case bundle is written to TigerGraph, the system reads it back and compares counts and a hash as a verification step.
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What the reported benchmark shows—and does not show
Team TrustMeBro reports that all 20 benchmark cases passed its answer validator and were written to the graph and verified by read-back. The project reports 7 fraud verdicts, 12 legitimate verdicts, and 1 uncertain verdict; it also says 6 suspicious activity reports were generated. Each case used 21–34 graph queries. These are results reported by the project team for its benchmark, not independent performance measurements.
The article describes a dataset of 590,742 transactions, 13,553 customers, 14,317 cards, 9,702 device profiles, 576,425 NEXT edges, and 5,565 closed historical cases. These figures, like the benchmark results, are project-reported. The cited article establishes neither independent validation nor production deployment at a bank.
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A key lesson from the team’s account is that a graph can make a weak relationship look compelling simply because many records share it. The project found one generic “unknown | unknown | unknown” device profile associated with 1,011 customers, and a common Windows + Chrome profile associated with 842 customers. Treating those broad matches as meaningful connections risked generating false leads.
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The team says requiring a more specific device profile reduced the report count to 6 of 20 cases. That is a project-reported change in this benchmark, not evidence that the revised rule has a known false-positive rate in other datasets or live operations.
Data-loading and implementation lessons
The team also reports encountering reserved GSQL words, Boolean defaults, file-loading behavior, and prefixed query output fields on its live instance. An initial bulk load omitted about 14,000 transactions. Comparing graph vertex counts with source-file counts exposed the missing records—a straightforward integrity check that can catch a serious ingestion problem before downstream investigation depends on incomplete data.
How to interpret FraudLens
FraudLens is most useful as an architecture demonstration of graph-based investigation: connect an alert to relevant entities, preserve evidence and its provenance, account for counter-evidence, and route actions through explicit policy controls. Its reported case counts and successful validator checks show what the team says it achieved in a small, defined benchmark. They do not establish accuracy, operational safety, or suitability for deployment in a financial institution.
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