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TigerGraph and GraphRAG can support fraud investigations by connecting structured events—such as transactions, accounts, and devices—to evidence extracted from case documents, then retrieving relevant links for an investigator to review. A useful design combines graph traversal with document and vector retrieval; an agent can choose among those methods when a question calls for it. This is an architectural approach, not a verified deployment or a demonstrated way to improve fraud outcomes.
What “agentic GraphRAG” means in a fraud investigation
Graph-based investigation treats evidence as a network rather than as unrelated rows. A transaction may connect an account to a device, the device to another account, and both accounts to people or case documents. Investigators can then examine paths across those records instead of relying only on one event at a time.
GraphRAG adds retrieval from graph structures to the broader retrieval-augmented generation pattern. Microsoft’s documented GraphRAG indexing pipeline extracts entities, relationships, and claims from text, detects communities, generates summaries at multiple levels, and creates embeddings. Its overview describes a structured, hierarchical approach that uses a knowledge graph and community hierarchy during retrieval tasks (Microsoft GraphRAG indexing overview; Microsoft GraphRAG getting started).
“Agentic” retrieval means an agent can choose which retrieval method or tool to use for a question—for example, a graph query, vector search, community search, or an external tool. TigerGraph’s GraphRAG project describes a system combining a graph database, vector store, and generative AI, with an agentic engine that can select retrieval methods. Microsoft GraphRAG and TigerGraph’s GraphRAG project are separate implementations, not one combined product. TigerGraph’s repository identifies its agentic engine and non-hybrid retrieval methods as self-service/as-is, so they should be treated as implementation options requiring appropriate review, not as standard supported capabilities (TigerGraph GraphRAG README).
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How the proposed investigation architecture fits together
The following is a design pattern, not a vendor-prescribed fraud schema or reference architecture. Adapt it to the records, identity controls, and review procedures of the organization using it.
- Ingest structured events and documents. Bring in relevant transaction records and other structured events, along with case notes or reports whose contents can contribute evidence. Preserve source identifiers and timestamps so that retrieved facts can be traced back to their origins.
- Normalize identities and define graph entities. Decide which records represent the same account, person, device, or other entity, and record how confident that resolution is. Create an explicit schema rather than assuming the vendor prescribes a fraud-specific one.
- Connect entities with evidence-backed relationships. Add edges such as “account initiated transaction” or “event used device” only when the source records support them. Preserve provenance and relevant time information on or alongside each relationship.
- Extract useful structure from text. Where documents contain relevant information, use an extraction pipeline to identify entities, relationships, and claims. Keep extracted assertions distinguishable from directly ingested records, and retain the document and passage from which each assertion came.
- Retrieve context using appropriate methods. Use graph traversal to find connected entities and multi-hop paths, vector search to find semantically relevant text, and document or community summaries to orient a search across larger collections.
- Plan retrieval when it adds value. An agent may select or combine tools based on the investigator’s question. For a narrow question, a fixed and reviewable workflow may be simpler; agent-selected retrieval introduces additional choices that should be observable and testable.
- Present evidence for investigator review. Return cited source records and document passages alongside a human-readable case narrative. The narrative should distinguish observed facts from extracted claims and generated interpretation, and should not stand in for an investigator’s decision.
Designing a graph model for fraud evidence
A compact starting model can make the investigation question explicit without treating every possible data source as a graph entity. The entities and relationships below are illustrative design choices; they are not a TigerGraph-mandated fraud schema.
| Graph element | Illustrative use | Evidence to retain |
|---|---|---|
| Person | Represent an individual referenced by a source record or case document. | Source identifiers, identity-resolution basis, and confidence where applicable. |
| Account | Connect transactions and account-related events to an account record. | Originating system, account key, and effective dates. |
| Device | Represent a device identifier associated with an event. | Source event, observed time, and identifier type. |
| Transaction | Represent an event with attributes such as amount, time, and direction when those fields are available. | Original event ID and source record. |
| Document or passage | Anchor a text-derived entity, relationship, or claim to its source. | Document ID, passage reference, and extraction provenance. |
| Relationship | Connect entities, such as an account initiating a transaction or an event involving a device. | Relationship type, supporting record, time, and any confidence or status needed to interpret it. |
In practice, identity resolution is a consequential modeling decision. A shared device or similar name can be a lead for review, not proof that two records refer to the same actor. Store the basis and uncertainty of a match rather than silently collapsing records into one identity. Likewise, distinguish a directly observed relationship from one inferred from text or from a model.
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TigerGraph’s GSQL language is designed for graph exploration and analysis. Its query documentation describes queries as sequences of retrieval and computation statements that can also update graph data and produce output (TigerGraph GSQL Query Language, version 4.2). For an investigation system, keep read-oriented investigation queries separate from graph updates where practical, and make any update path explicit and auditable. The GSQL 4.2 documentation identifies Syntax V2 as its current default for that documentation version; verify language behavior against the version actually deployed.
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Choosing graph, vector, hybrid, or agent-selected retrieval
These approaches answer different kinds of questions. The available documentation does not establish a head-to-head winner for fraud investigations, so choose based on the question, evidence requirements, and operational constraints.
| Approach | Useful for | Key consideration |
|---|---|---|
| Graph traversal | Questions about explicit connections and multi-hop paths among modeled entities. | Depends on the quality of entity resolution, edge definitions, and source provenance. |
| Vector retrieval | Finding text that is semantically relevant even when the wording differs from the query. | Similarity is not proof of a relationship; the returned passage still needs source review. |
| Hybrid retrieval | Combining graph connections with relevant text or document context. | Requires coordinating retrieval results and showing which source supports each statement. |
| Agent-selected retrieval | Questions that may benefit from choosing among graph, vector, community, or other available tools. | Tool selection adds complexity; log the selected tools and results, and assess when the agent should abstain or request review. |
For example, “Which other accounts used this device during the relevant period?” is naturally expressed as a graph question if account-device links and their times are modeled. “What did the case notes say about the dispute?” is a document retrieval question. A question connecting both—such as finding notes relevant to accounts along a device-linked path—may call for hybrid retrieval. These are query-design examples, not claims about measured investigative performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to check before running TigerGraph GraphRAG
The TigerGraph GraphRAG README lists Docker with Docker Compose or Kubernetes, TigerGraph DB 4.2 or newer, and an API key for an LLM provider among its prerequisites. Provider availability and setup details can change; confirm the current repository instructions and release notes for the version you intend to use before deployment. The README also describes the project’s agentic engine and non-hybrid retrieval methods as self-service/as-is (TigerGraph GraphRAG README).
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →- Confirm that the database version meets the current README’s requirement and that the deployment method is supported for your environment.
- Check which LLM providers and configuration options the current project version supports; do not assume an API key alone is sufficient.
- Review what data is sent to the model provider, how secrets are managed, and what logging or retention controls apply to your deployment.
- Validate that graph queries, vector retrieval, and document citations resolve to the intended source records before enabling investigator use.
- Check the project’s current release notes for changes that affect migrations, data integrity, or agentic chat behavior.
The repository’s current version and feature set are volatile; a version number is not necessary to understand the architecture and should be checked directly before following release-specific instructions.
How to evaluate the design without overstating its impact
The cited material establishes a vendor-described fraud and financial-crime use-case category, but it does not report quantified fraud performance for this proposed architecture. It does not establish improved detection, fewer false positives, reduced losses, faster case resolution, or compliance with a particular jurisdiction’s rules (TigerGraph GraphRAG; TigerGraph GraphRAG README). Those outcomes require relevant, named, dated evaluation rather than inference from product capabilities.
A defensible evaluation plan should compare retrieval methods on the same historical cases and keep the test design from leaking identities or related events across splits.
- Define a labeled historical holdout. Set aside cases with labels appropriate to the investigation task, and document how labels were assigned and what information was available at the time.
- Prevent entity leakage. Split data so that linked people, accounts, devices, or related events do not inadvertently appear in both development and test sets in a way that gives the system an unfair advantage.
- Compare against a baseline on identical cases. Evaluate graph-aware retrieval, vector retrieval, hybrid retrieval, and any agent-selected workflow against an appropriate baseline using the same case inputs and review conditions.
- Track both retrieval quality and investigative workload. Proposed measures include precision and recall for the defined task, along with investigator review time or workload measures. Treat these as evaluation measures to collect, not results already established by the cited sources.
- Inspect attribution and abstention. Sample outputs to verify that cited records support the narrative, that unsupported claims are not presented as facts, and that the system can decline to answer or seek review when evidence is inadequate.
Keep offline retrieval metrics separate from operational outcomes such as confirmed fraud, losses, or case-resolution time. A system can retrieve relevant evidence without proving that it changes those outcomes.
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