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Synthetic fraud graph generator for benchmarking graph-based fraud detection models in financial services.
| Date | Stars |
|---|---|
| 2026-07-24 | 275 |
| 2026-07-25 | 275 |
| 2026-07-28 | 275 |
| 2026-07-30 | 275 |
| 2026-08-06 | 275 |
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# gen_fraud_graph > Synthetic fraud graph generator for training and benchmarking graph-based fraud detection models in financial services. [](LICENSE) [](https://www.python.org/downloads/) [](https://pypi.org/project/gen-fraud-graph/) [](https://github.com/SantanderAI/gen-fraud-graph/actions/workflows/ci.yml) [](https://codecov.io/gh/SantanderAI/gen-fraud-graph) [](https://github.com/SantanderAI/gen-fraud-graph/actions/workflows/codeql.yml) [](https://scorecard.dev/viewer/?uri=github.com/SantanderAI/gen-fraud-graph) [](https://github.com/psf/black) [](https://github.com/astral-sh/ruff) [](https://conventionalcommits.org) [](https://github.com/SantanderAI/gen-fraud-graph/commits/main) --- ## Overview **gen_fraud_graph** is an open-source Python tool that generates massive synthetic financial transaction graphs with injected fraud patterns and optional vector embeddings. It produces CSV datasets ready for ingestion into graph databases (TigerGraph, Neptune, Neo4j, JanusGraph) or for training graph neural networks (GNN). The generator creates three types of data: - **Account nodes** — synthetic customer accounts with balance, risk score, and optional embedding vectors - **Transaction edges** — normal financial transactions between accounts - **Fraud rings** — cyclic money-laundering patterns with suspicious transaction descriptions ### Key Features - **Massive scale** — Generate from 1K to 100M+ accounts with configurable scale factor - **Fraud pattern injection** — Cyclic money-laundering rings with configurable depth (4–7 hops) - **Parallel generation** — Multi-process workers for fast generation on high-core machines - **Vector embeddings** — Three providers: `fake` (random, fast), `local` (SentenceTransformers), `openai` (API) - **Multiple formats** — Generic CSV or AWS Neptune bulk-load format - **Resume support** — Interrupted generation can resume from where it left off - **Privacy by design** — All data is 100% synthetic; no real financial data is used ### Use Cases - Training and evaluating **graph neural networks (GNN)** for fraud detection - Benchmarking **anti-money laundering (AML)** detection algorithms - Load-testing graph databases (TigerGraph, Neptune, JanusGraph, NebulaGraph, FalkorDB) - Research in **financial crime detection** and **anomaly detection** on graphs - Generating labeled datasets for **deep learning** on graph-structured data --- ## Quick Start ### Installation > **Note:** `gen-fraud-graph` is not yet published to PyPI, so `pip install gen-fraud-graph` will fail with `No matching distribution found`. Until the first PyPI release, install from source as shown below. The PyPI badge above is pre-provisioned for the planned release. Install from source using [uv](https://github.com/astral-sh/uv): ```bash git clone https://github.com/SantanderAI/gen-fraud-graph.git cd gen-fraud-graph uv venv && source .venv/bin/activate uv pip install -e '.[dev]' ``` With optional embedding providers (from the cloned source directory): ```
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matched fp:adabe54ba69efcfb, topic:synthetic-data
matched fp:adabe54ba69efcfb, topic:benchmark