You can build a fraud investigation agent by using TigerGraph to retrieve connected case evidence, deterministic policy logic to score signals and route cases, and Gemini to explain the evidence or draft a report. Keep those responsibilities separate: a model-generated explanation is not proof of fraud, and consequential customer actions or regulatory filings should remain subject to human review. A public FraudSight AI hackathon project describes this kind of workflow, but it does not establish production accuracy or regulatory effectiveness.
What the agent should do—and what it should not decide
The FraudSight AI project describes a workflow that starts with a high-risk alert, a customer report, or an analyst referral. It gathers relationship and transaction evidence from TigerGraph, assesses the case, requests or simulates additional evidence when needed, reassesses against policy rules, drafts a suspicious activity report (SAR) when project-defined thresholds are met, and writes case findings and actions back to graph memory. Its stated routing modes are auto, L1, and L2. These are the project’s design choices, not universal financial-industry standards. The project repository describes the prototype.
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Use “autonomous” to describe how the workflow gathers and organizes evidence, not as permission for a language model to independently declare fraud, freeze an account, or submit a regulatory filing. Make the decision boundary explicit: the system can investigate and recommend; people approve actions that affect customers or create formal filings.
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| Component | Job | Output to preserve |
|---|---|---|
| TigerGraph | Retrieve connected transactions, entities, and relevant historical cases for the investigation. | The query results and the paths or records supporting each finding. |
| Deterministic policy logic | Apply explicit rules to evidence, produce scores or recommended actions, and decide whether a case meets configured routing or drafting thresholds. | Rule identifiers, input values, threshold version, and resulting score or route. |
| Gemini | Turn structured evidence and policy results into a readable explanation or draft. | The model-produced text, tied to the evidence and policy outputs it was given. |
| Human reviewer | Approve, reject, or request more evidence before consequential action or a formal filing. | Reviewer identity, decision, timestamp, and any reason or override. |
This separation makes it possible to inspect whether a recommendation came from a graph observation, a policy rule, or generated prose. The FraudSight project describes LangGraph as its state-machine framework, Gemini 2.5 Flash as its model, gemini-embedding-001 for embeddings, and tigergraph-mcp as the graph-access bridge. Treat these as the repository’s named implementation choices, not a guarantee that they are the best current versions for a new deployment. Verify current compatibility and availability before adopting them. The repository’s implementation description provides its project-specific setup.
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Model the relationships an investigator needs to inspect
The project describes a graph containing transactions, cards, device fingerprints, email clusters, billing regions, customers, and investigation records. Its listed query areas include customer baselines, card activity windows, small authorization sequences, new-device proxies, out-of-region behavior, recurring charges, similar closed cases, and device neighbors.
For a practical implementation, ensure that each finding can be followed back to the records that support it. For example, a reviewer investigating a card alert should be able to see the associated customer and transactions, whether the device appears in other relevant activity, and whether similar closed cases were retrieved. Treat these as investigation leads: a shared device or region can provide context, but a relationship alone does not establish wrongdoing.
The project calls its approach GraphRAG-style: it combines graph traversal, historical case similarity, and policy retrieval before providing context to Gemini. Keep the retrieved evidence structured and distinguish observed facts from inferred relationships, historical analogies, and policy conclusions. An embedding-based match to a prior case is a way to find potentially relevant material; it is not a substitute for checking whether the current case actually shares the material facts.
Build the workflow as reviewable stages
- Open a case. Accept an alert, customer report, or analyst referral. Assign a stable case identifier and preserve the supplied reason for referral so the investigation can be reconstructed.
- Retrieve evidence from the graph. Run the relevant query families for customer baselines, card windows, authorization sequences, device signals, regions, recurring charges, neighboring devices, and similar closed cases. Retain the returned records and their provenance rather than passing only a summary to the next stage.
- Evaluate explicit policies. Apply deterministic rules to the retrieved evidence. Record which rules matched and how any configured score, threshold, or routing outcome was calculated. Do not let Gemini silently change a policy result.
- Identify evidence gaps. If a required signal is absent or a query fails, mark it as unavailable or unresolved. The repository describes requesting or simulating additional evidence when needed; a deployment should distinguish a real source response from simulated data and must not present a simulation as verified evidence.
- Generate an explanation or draft. Give Gemini the structured findings, relevant policy results, and clear instructions to distinguish facts from uncertainty. The output should point back to evidence identifiers so a reviewer can verify each material statement.
- Route for approval. Apply the configured routing mode, such as the project’s
auto,L1, orL2labels, but define what each means in your own organization. Require human approval before customer-impacting action or submission of a SAR. - Persist the investigation. Write the case, findings, decisions, and actions to graph memory as the project describes. Keep the original evidence, policy version, generated draft, reviewer decision, and any later correction linked to the case.
These stages are a reference design, not a claim that the public prototype implements every production control. The repository is a 2026 hackathon submission and its descriptions of case counts, graph size, workflow completion, and benchmarks are project-reported rather than independently validated. It provides no independently established fraud-accuracy, false-positive-reduction, production-readiness, or SAR-compliance results. See the project description and code.
Make auditability a system requirement
A reviewer should be able to answer, for any recommendation: which records were retrieved, which graph relationships were traversed, which rules fired, what threshold was applied, what Gemini was asked to produce, and who approved or changed the outcome. Store these details as structured case evidence, not only as a conversational transcript.
- Version the graph queries, policy rules, prompts, and model configuration used for each case.
- Preserve evidence source and retrieval time, along with the distinction between observed, inferred, and simulated information.
- Log approval, rejection, escalation, override, and correction events with their actors and timestamps.
- Restrict access to sensitive case and customer data, and ensure that reviewers can see the evidence needed to assess a recommendation.
- Test how the workflow behaves when evidence is missing, contradictory, stale, or unavailable; define when it must stop or escalate rather than inventing an explanation.
A related September 2026 TigerGraph hackathon article frames the human-review principle as “the system recommends, it doesn’t unilaterally act,” and identifies policy-threshold tuning and audit logging as future work in that prototype. That is a useful design example, not a binding standard or proof that those controls are complete. Read the related hackathon article.
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TigerGraph’s March 4, 2025 announcement describes hybrid graph and vector search for GraphRAG and fraud or AML use cases. The announcement reports “5.2x faster vector searches with 23% higher recall than competitors to rapidly uncover the most similar items while using 22.4x fewer resources” and “6x faster indexing.” These are TigerGraph’s vendor claims; the announcement does not independently establish the comparison methodology, so they should not be treated as a benchmark for your workload. TigerGraph’s March 4, 2025 announcement also lists Community Edition specifications of 16 CPUs, 200 GB of graph storage, and 100 GB of vector storage. Those are dated vendor-published specifications; verify current availability and terms before planning capacity around them.
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The repository declares Python 3.11–3.14, Node.js 18 or 20, a TigerGraph cloud instance, and Gemini API credentials among its setup requirements, and names TigerGraph Savanna Cloud v4.2.5 as its graph environment. These are project-declared requirements and version details, not a current compatibility matrix. Check them against the present platform documentation and your organization’s deployment, access-control, data-residency, latency, and jurisdictional requirements before implementation. The available project sources do not provide a controlled platform comparison or current pricing, so they do not support ranking graph platforms or model options.
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