Graph analysis helps investigators find fraud leads that are hard to see in isolated records: accounts linked by a shared device, transfers routed through intermediaries, or companies connected through ownership. It represents entities and their relationships so analysts can query and inspect multi-step connections. A graph is an investigative aid, not a verdict engine—and a prompt alone cannot supply reliable relationships that were never modeled or verified.
What a graph adds to a fraud investigation
A transaction table can show who paid whom, when, and how much. A graph can make it easier to ask what connects two parties across several steps: perhaps both used the same device, one received funds from an account that paid the other, or their companies share an owner.
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In a graph model, entities become nodes and the links between them become relationships. A payment investigation might represent people, accounts, devices, cards, and transactions as nodes, with relationships for transfers, shared identifiers, or account ownership. Analysts can then look for paths, repeated patterns, or clusters spanning multiple records.
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This relationship view is useful when the significance lies not in one record but in how records connect. Deloitte Switzerland describes financial-crime analysts working across siloed information systems and using graph queries and visualizations to expose connections among people, companies, transactions, and other data. Deloitte says it adopted Linkurious Enterprise for investigations, AML alert review, KYC, and related work; that is an account of its own practice, not an independent comparison of tools.
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When the relationship is the lead
Accounts connected by shared identifiers
Two accounts may look unrelated until investigators find that they used the same device, card, or contact detail. Google Cloud’s June 29, 2026 case article describes Curve using BigQuery Graph to investigate connections among users, devices, cards, and other shared identifiers within its existing data platform. A shared identifier can generate a lead, but it does not by itself establish that the account holders acted together.
Funds routed through intermediaries
A transfer chain can connect a suspicious originator to a beneficiary through accounts that are not immediately adjacent in a transaction list. AWS’s 2022 architecture article describes batch investigation of transaction chains using RDFox, EKS, and Neptune. Its example is a particular architecture, not proof that every payment network or workload will achieve the same results.
Potentially collusive claims or actors
Connections among claimants, providers, experts, and other actors can help an investigator examine possible collusion, duplicate claims, or staged losses. These are use cases listed by Neo4j, a software provider; they describe possible applications, not independently measured detection performance.
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Mapping ownership links can help analysts trace beneficial owners through layers of companies. A Neo4j-hosted webinar listing with GraphAware presents this as a demonstration topic. Treat it as an example of what a software-provider demonstration may cover, not independent evidence that a particular investigation was solved.
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How to run a graph investigation
- Define the question. State what relationship you need to test, such as whether two suspicious parties are connected by a chain of transfers or share a device.
- Choose the entities and relationships. For a payment inquiry, decide which records represent people, accounts, devices, cards, and transactions, and which links represent transfers, shared attributes, or ownership.
- Connect the relevant records. Load the data needed for the question and preserve provenance: investigators should be able to identify the source records behind each node and link.
- Search for paths or patterns. Query the graph for the defined relationship, or use relevant graph analytics or graph machine learning to help identify patterns or assign scores. A score or match is a lead for review, not a finding of intent.
- Inspect the connections. Give investigators a way to follow the result back through the graph and verify each relationship against its source record and investigative context.
- Record the decision. Document what was reviewed and carry confirmed findings into the existing case or risk workflow.
The National Institute of Justice Office of Justice Programs record describes PINGS—Procedures for Investigative Graph Search—as a graph database library for inexact graph-pattern matching with a scoring mechanism. Its 2019 paper reports demonstrations on a synthetic radicalization dataset and a publicly available crime dataset. Those demonstrations illustrate investigative graph search; they should not be mistaken for evidence of a contemporary production fraud deployment.
How the documented approaches differ
The examples below show different architectural choices and reported uses. They are not a head-to-head test or a ranking.
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| Approach | What the source describes | Attribution and scope |
|---|---|---|
| BigQuery Graph | Graph investigation within an existing BigQuery environment, including connections among users, devices, cards, and other identifiers. | Google Cloud’s June 29, 2026 case article about Curve. |
| Neptune Analytics and GraphStorm | A pipeline emphasizing multi-hop relationships and graph machine learning. | AWS Database Blog technical article, 2025; a vendor-authored example. |
| RDFox, EKS, and Neptune | A batch architecture for investigating transaction chains. | AWS Architecture Blog, 2022; a vendor-authored demonstration. |
| Linkurious Enterprise | Graph investigation tooling used for investigations, AML alert review, KYC, and related work. | Deloitte Switzerland’s account of its own practice. |
| Neo4j graph platform | Vendor-listed use cases include recursive relationship patterns, pathfinding, entity resolution, money laundering, claims collusion, quote fraud, and account takeover. | Neo4j’s own use-case material, not an independent evaluation. |
The right design depends on where the data already lives, what analysis is required, and whether analysts can trace results back to records. Explicit pattern matching, pathfinding, entity resolution, graph algorithms, and graph machine learning are different needs; an organization may use graph analysis alongside rules, relational queries, case tools, or other machine-learning systems rather than replacing them.
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AWS’s 2022 Architecture Blog reports that its described demonstration processed 500 million transactions and 50 million parties in under two hours. That is a vendor-reported result for that architecture and demonstration, not a general benchmark or a forecast for another organization’s data, infrastructure, or workload.
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A 2021 technical survey notes that implementing graph solutions in real-time financial transaction systems brings application and deployment challenges. The cited examples therefore do not establish that graph analysis is a plug-in replacement for existing fraud systems, nor do they provide an independent head-to-head performance comparison with relational analysis.
Validate the graph before acting on a lead
- Check identity resolution. A graph is only as reliable as the process that decides whether records refer to the same person, business, account, or device.
- Verify the underlying link. A visually persuasive path should be checked against the source records that created each relationship.
- Consider legitimate explanations. People may share devices, cards, addresses, or service providers without coordinating fraud.
- Account for missing or stale data. Incomplete records can hide links, while outdated or incorrect records can create misleading ones.
- Keep investigators in the decision loop. A graph match, pattern, or score can direct attention; it does not establish intent or prove that a person committed fraud.
The cited material does not establish a universal graph-fraud false-positive rate, accuracy figure, or independent product ranking. Those outcomes depend on the data, entity matching, relationships modeled, query or model, and review process in a particular deployment.
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