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A fraud investigation agent built with TigerGraph and GraphRAG should connect an alert to related entities, retrieve relevant documents and prior cases, and return a traceable evidence bundle for review. It can automate evidence gathering and explanation; it should not silently turn a model output or a remembered case into a customer-impacting decision or regulatory filing.
What the agent should—and should not—do
“Autonomous” is best treated as a bounded workflow, not permission for an LLM to decide guilt or take unrestricted action. The agent can accept an alert, select approved retrieval methods, assemble evidence, and draft an investigation record. Its output should distinguish what the systems observed from what the model inferred, identify missing information, and leave consequential decisions to the organization’s approved policy and qualified reviewers.
The result is more useful than a bare risk score: an analyst can see which accounts, transactions, devices, or other entities connect to the alert, which documents informed the analysis, and how the agent reached its summary.
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Architecture: connect graph evidence, documents, and case history
1. Ingest an alert and resolve its entities
Start with an alert from a risk model, customer report, or analyst referral. Normalize identifiers before linking the alert to relevant entities—such as transactions, accounts, cards, devices, email addresses, and locations—in the graph. Keep the alert’s original values and source alongside normalized values so an analyst can trace a match and investigate a mistaken or ambiguous identity resolution.
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Graph structure matters because a suspicious relationship may be several links away from the alert. A transaction can connect accounts, a device can be shared across accounts, and a location can be associated with multiple events. A graph query can return those paths and their context; text similarity alone does not establish that two records are connected.
2. Retrieve bounded graph evidence
Use scoped, deterministic graph queries to retrieve relevant neighbors, paths, repeated entities, and transaction context. Define limits for the entities and relationship types the investigation may traverse, the depth or number of results it may return, and the time window that applies. This keeps the evidence gathering relevant and makes the query trace reviewable.
TigerGraph’s GraphRAG project documentation describes structural graph queries alongside vector and community retrieval. Its agentic engine can choose among retrieval methods; the documentation describes a planned style that constructs a bounded retrieval plan as well as reactive execution. Treat those as retrieval controls, not proof that the agent’s chosen path is complete or correct.
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3. Retrieve policies, narratives, and prior case documents
Ingest the materials that investigators are permitted to use: policy documents, fraud typologies, transaction narratives, and prior case records. TigerGraph GraphRAG documents local uploads and cloud downloads, preprocessing, and a knowledge-graph build and refresh workflow. The knowledge graph must be initialized before ingestion and refreshed after ingestion for its content to be updated. Plan those operations into document onboarding; uploading a file is not by itself evidence that the retrieval graph reflects it.
Hybrid retrieval combines semantic vector search with graph traversal. A useful pattern is to find document passages or entities relevant to an alert, then follow graph links to see whether the named accounts or transactions connect to the alert’s entities. Google’s BigQuery GraphRAG codelab demonstrates this general seed-entity-then-traverse pattern. It is a BigQuery example, not documentation of TigerGraph behavior.
4. Synthesize an evidence-based case record
Ask the language model to separate three categories in its response:
- Observed facts: records and graph relationships actually retrieved, with identifiers and source references.
- Prior-case context: relevant similarities to earlier cases, including their dates, dispositions, and policy context.
- Hypotheses: possible explanations that remain unverified, along with the evidence needed to test them.
This distinction prevents a plausible narrative from being mistaken for a verified fact. Require the output to state uncertainty and missing evidence, not just a recommended interpretation.
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Case memory is a retrieval source, not a ground-truth label. A previously closed case may have been closed under a different policy, later corrected, or incorrectly resolved. Store enough context to decide whether it is relevant before using it as an analogy for a new alert.
A practical implementation is to create a versioned record for each investigation and retain source references, graph query or retrieval trace, model and prompt version, policy version, analyst disposition, and later corrections. These are design recommendations; they are not guarantees provided by TigerGraph. The public FraudSight AI repository describes case memory in a hackathon prototype, not an independently validated production system.
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| Memory design | Strength | Risk and control |
|---|---|---|
| Append-only case history | Preserves prior versions and dispositions, making changes and corrections auditable. | Old or incorrect outcomes remain retrievable; mark superseded records and make disposition and policy context available to retrieval and review. |
| Mutable summary | Can make a compact, current synopsis easier to retrieve. | Edits can erase provenance or hide corrections; retain the source record and version history, and restrict who may change the summary. |
For either design, apply access controls to sensitive case material and test retrieval relevance. Do not use a prior case as proof that a new alert is fraudulent, and do not let an incorrectly closed case become a reinforcing label through repeated retrieval.
Choose a retrieval mode based on predictability and operational needs
The TigerGraph GraphRAG repository describes a Classic fixed pipeline and an Agentic engine that can select retrieval methods. The choice is a trade-off, not a claim that one mode is universally more accurate.
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|---|---|---|
| Retrieval behavior | Fixed and predictable pipeline. | Self-directed choice among documented retrieval methods, including structural graph queries, vector search, and community search. |
| Traceability | A fixed sequence is easier to anticipate and audit. | Inspect and retain the selected retrieval steps and results so reviewers can understand the agent’s path. |
| Execution budget | Plan resource use around the fixed pipeline. | Bound iterations or steps and set an acceptable latency and resource budget. |
| Coverage | Coverage follows the retrieval stages configured in the pipeline. | Can choose among multiple retrieval methods, but dynamic choice does not guarantee that all relevant evidence will be found. |
| Support status | The repository identifies hybrid search as the officially supported retrieval method. | The repository describes the agentic engine as self-service and provided as-is. |
The repository README states: “Hybrid Search is the officially supported retrieval method; other retrieval methods, and the agentic chat engine that orchestrates them, are provided as-is for self-service use.” Read this as a support qualification, not an independent evaluation of retrieval quality. Confirm suitability and support expectations for the deployment you intend to operate.
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Build and validate the workflow in stages
- Define the investigation boundary. Specify alert types, permitted data sources, entity types, traversal limits, retention rules, and which actions require human approval. Decide what the agent may draft or recommend and what it may not execute.
- Prepare the graph and documents. Model the entities and relationships investigators need to follow. Prepare policy, typology, narrative, and case documents for ingestion, then initialize and refresh the knowledge graph as required by the GraphRAG workflow.
- Configure retrieval. Begin with scoped graph queries and hybrid search for a predictable baseline. If using agentic retrieval, constrain its available methods and execution budget and retain the retrieval trace.
- Define the case record. Store the alert, retrieved evidence, source references, retrieval trace, model and prompt version, policy version, analyst disposition, and corrections as versioned information. Make relevant dates and prior dispositions visible when cases are retrieved.
- Test evidence quality, not just fluent summaries. Use representative alerts and reviewer assessment to check whether links are supported by records, relevant documents are retrieved, uncertainty is expressed, and incorrect or outdated case analogies are not treated as proof. Record failures and adjust query scope, ingestion, or controls.
- Route outcomes through review. Return the evidence bundle, unresolved questions, and proposed next steps to the approved workflow. Require the appropriate qualified review before customer-impacting action or regulatory reporting, and retain the reviewer and outcome in the audit trail.
Platform requirements and evidence limits
The TigerGraph GraphRAG README lists TigerGraph DB 4.2 or later and an LLM provider API key among its prerequisites, and describes Docker Compose or Kubernetes deployment options. Its supported-provider list and configuration can change; check the README for the version and deployment you plan to use rather than treating those details as permanent. The repository also describes TigerGraph as the graph and vector database for this project.
There is a fraud-specific TigerGraph example, but it is a hackathon project. Its reported scale or capabilities should not be treated as independently audited production performance. Likewise, TigerGraph’s Enterprise GraphRAG material describes vendor positioning. Its fraud-investigation page advertises “$100M+” annual fraud savings across top global banks, “229% ROI” with less than six-month payback, “40% Faster” AML case resolution with 30% earlier intervention, and “$50M+” annual savings at a global bank with 25% higher accuracy. Those are TigerGraph-published claims on a page reviewed in 2026; the reviewed landing page does not supply study methods sufficient to validate them independently. They are not expected outcomes or benchmarks for an agent built from this architecture.
No independent measured performance result for the titled agent is established by these examples. Evaluate a real deployment against its own documented cases, policies, reviewers, and operational requirements rather than inferring effectiveness from a vendor claim or prototype.
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