TigerAttack is a fraud-investigation workflow that assembles connected graph evidence and relevant policy or case context before an AI reasoning layer explains findings or recommends an action. Its design separates evidence gathering, calculations and policy checks from language-model synthesis—and can request more evidence when the case is not yet sufficiently supported. The project’s reported tests concern pipeline execution and grounding, not the accuracy of fraud decisions.
What TigerAttack is designed to do
Built by its author for the TigerGraph × Hacker House Goa challenge, TigerAttack is described as an agent-assisted workflow for investigating a suspicious transaction, a customer dispute, a fraud-risk signal, or an analyst-initiated case. Rather than asking a model to judge a case from a prompt alone, the system gathers connected records and contextual material, evaluates them, and records the investigation path. The project description is the source for the architecture and figures below; it is not an independent security or reliability assessment. Project description
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The application is described as having a Next.js investigator interface and a FastAPI orchestration backend. TigerGraph provides the central investigation graph, which represents entities such as customers, cards or accounts, transactions, merchants, devices, identities, and prior cases. Reusable GSQL queries are described as supporting transaction context and customer history, shared-device or identity discovery, velocity analysis, historical-case retrieval, exposure calculations, and evidence-pack assembly.
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How graph evidence, GraphRAG, and MCP fit together
TigerGraph: relationships and transaction context
Graph queries let an investigation look beyond an isolated transaction. The system can retrieve related entities and historical activity—for example, shared devices or identities—alongside transaction and customer context. That makes relationship-centered evidence available to the investigation, rather than relying only on text supplied to the model.
#1 Best Overall
GraphRAG: policies and investigative context
GraphRAG is described as bringing structured graph evidence together with relevant contextual material, including fraud policies, typologies, investigation procedures, regulatory requirements, and historical case information. The combined evidence pack is then supplied to the reasoning layer. Graph evidence answers questions about connected records; contextual retrieval helps frame how those records should be interpreted and what procedures may apply.
MCP: a controlled tool boundary
The backend uses MCP to expose graph operations to the agent as tools. In the project’s description, this boundary lets the agent request structured results from graph operations, with those results contributing to evidence provenance. MCP is therefore part of the mechanism for obtaining and returning evidence; it is not itself the fraud decision-maker.
What happens during an investigation
- Start from a trigger. An investigation may begin with a suspicious transaction, customer dispute, fraud-risk signal, or analyst request.
- Gather connected records. The backend coordinates graph queries and other tools to collect transaction context, related entities, customer history, and potentially relevant prior cases.
- Retrieve context and assemble evidence. GraphRAG brings applicable policies, typologies, procedures, regulatory requirements, and case material into the evidence pack.
- Analyze patterns, exposure, and evidence sufficiency. The workflow evaluates the findings and checks whether the available evidence is enough to proceed.
- Request more evidence if needed. If the case is not sufficiently supported, the workflow can pause and request information such as customer validation or step-up authentication, then reassess.
- Evaluate policy and recommend an action. Once evidence and policy checks have been considered, the system can produce a next-best-action recommendation and an explanation.
- Update and retain the case record. The described lifecycle includes case updates, audit logging, and case memory so findings, evidence, decisions, actions, outcomes, and investigation history can be retrieved later.
The project also describes support for suspicious-activity-report (SAR) generation. That is a stated workflow capability, not evidence that any report is legally sufficient or ready to file without appropriate review.
Which parts are deterministic and which use a reasoning model?
The design assigns calculations and constraints to deterministic components, while using a reasoning model to synthesize and explain the evidence. This division is intended to keep arithmetic and rule-based decisions from being left to free-form model output.
Rank #3
| Responsibility | How the project describes it |
|---|---|
| Risk and transaction-pattern calculations | Deterministic components calculate risk and transaction patterns. |
| Exposure calculations | Deterministic components calculate exposure. |
| Policy and action constraints | Policy rules, evidence sufficiency, and action constraints are handled deterministically. |
| Evidence synthesis and interpretation | The reasoning model synthesizes evidence, interprets patterns, plans investigations, reasons about uncertainty, and produces explanations or natural-language summaries. |
| Case memory and audit trail | The described system retains investigation findings and history and generates audit events; the project article does not establish an independent audit or security certification. |
As author Rohit Sharma puts the design principle: “Every important conclusion should be traceable back to evidence.” Traceability is an architectural aim here; the statement is not an independent standard or proof that every production conclusion is fully auditable.
What the reported benchmark figures show—and what they do not
The project author reports evaluation across 20 benchmark cases and lists the following pipeline results. The article result is dated September 23, but does not explicitly establish its year; several figures below were presented in the project summary as 2026 figures based on recent search-result context rather than an explicit year in the article itself. They should therefore be read as author-reported project results, not independently verified measurements.
Rank #4
| Author-reported result | What it counts |
|---|---|
| 20/20 benchmark investigations completed | Investigation pipeline completions, not correct fraud classifications. |
| 140/140 MCP calls completed | Successful tool-call completion in the reported evaluation. |
| 300 graph evidence items retrieved | Retrieved evidence items, not a measure of their correctness or usefulness. |
| 100 historical case records retrieved | Historical records retrieved by the evaluated workflow. |
| 12 evidence requests and reassessments | Instances in which the workflow requested more evidence and reassessed. |
| 20/20 case-memory write/readbacks | Successful reported write/readback cycles for case memory. |
| 412 audit events generated | Events generated by the evaluated pipeline. |
| 0 grounding failures | Grounding behavior reported for this evaluation, not a guarantee against unsupported output in other conditions. |
| 94 automated tests passed | Automated tests reported by the project author. |
The author explicitly cautions that these numbers describe pipeline execution and grounding behavior, not 100% fraud-detection accuracy. They do not establish real-world effectiveness, performance on larger or adversarial datasets, or production readiness.
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The project article identifies further work rather than presenting the system as fully production-hardened. It proposes broader evaluation with larger datasets and adversarial cases, along with operational work on authentication, metrics, tracing, monitoring, and alerting. Other stated directions include real-time investigation streaming, more advanced temporal fraud detection, structured analyst feedback, and richer graph visualization.
Best Value
For an organization assessing a similar design, the useful questions are whether evidence can be traced to its source, whether calculations and policy checks are deterministic, what happens when evidence is insufficient, how recommendations are reviewed, and whether evaluation and operational controls match the intended deployment. TigerAttack’s description gives an architectural example, but does not provide comparative tests against commercial platforms or independently validated deployment results.
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