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license: apache-2.0 |
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task_categories: |
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- tabular-classification |
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- feature-extraction |
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language: |
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- en |
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tags: |
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- fraud-detection |
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- finance |
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- federated-learning |
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- cifer |
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pretty_name: Cifer-Fraud-Detection-Dataset-AF |
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size_categories: |
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- 10M<n<100M |
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--- |
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# 📊 Cifer Fraud Detection Dataset |
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## 🧠 Overview |
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The **Cifer-Fraud-Detection-Dataset-AF** is a high-fidelity, fully synthetic dataset created to support the development and benchmarking of privacy-preserving, federated, and decentralized machine learning systems in financial fraud detection. |
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This dataset draws structural inspiration from the **PaySim simulator,** which was built using aggregated mobile money transaction data from a real financial provider operating in 14+ countries. Cifer extends this format by scaling it to **21 million samples,** optimizing for **federated learning environments,** and validating performance against real-world datasets. |
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> ### Accuracy Benchmark: |
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> Cifer-trained models on this dataset reach **99.93% accuracy,** benchmarked against real-world fraud datasets with **99.98% baseline accuracy**—providing high-fidelity behavior for secure, distributed ML research. |
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## ⚙️ Generation Method |
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This dataset is **entirely synthetic** and was generated using **Cifer’s internal simulation engine,** trained to mimic patterns of financial behavior, agent dynamics, and fraud strategies typically observed in mobile money ecosystems. |
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- Based on the structure and simulation dynamics of PaySim |
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- Enhanced for multi-agent testing, federated partitioning, and async model training |
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- Includes realistic fraud flagging mechanisms and unbalanced label distributions |
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# 🧩 Data Structure |
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| Column Name | Description | |
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|------------------|-----------------------------------------------------------------------------| |
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| `step` | Unit of time (1 step = 1 hour); simulation spans 30 days (744 steps total) | |
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| `type` | Transaction type: CASH-IN, CASH-OUT, DEBIT, PAYMENT, TRANSFER | |
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| `amount` | Transaction value in simulated currency | |
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| `nameOrig` | Anonymized ID of sender | |
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| `oldbalanceOrg` | Sender’s balance before transaction | |
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| `newbalanceOrig` | Sender’s balance after transaction | |
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| `nameDest` | Anonymized ID of recipient | |
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| `oldbalanceDest` | Recipient’s balance before transaction (if applicable) | |
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| `newbalanceDest` | Recipient’s balance after transaction (if applicable) | |
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| `isFraud` | Binary flag: 1 if transaction is fraudulent | |
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| `isFlaggedFraud` | 1 if transaction exceeds a flagged threshold (e.g. >200,000) | |
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--- |
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# 📁 File Organization |
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Total Rows: **12,000,000** |
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Split into 8 folders/files for large-scale and federated learning scenarios: |
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- `Cifer-Fraud-Detection-Dataset-AF-part-1-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-2-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-3-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-4-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-5-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-6-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-7-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-8-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-9-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-10-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-11-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-12-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-13-14.csv` → 1.5M rows |
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- `Cifer-Fraud-Detection-Dataset-AF-part-14-14.csv` → 1.5M rows |
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Format: `.csv` (optionally `.parquet` or `.json` upon request) |
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# ✅ Key Features |
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- Fully synthetic and safe for public release |
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- Compatible with federated learning (cross-silo, async, or multi-agent) |
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- Ideal for privacy-preserving machine learning and robustness testing |
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- Benchmarkable against real-world fraud datasets |
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- Supports fairness evaluation via distribution-aware modeling |
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# 🔬 Use Cases |
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- Fraud detection benchmarking in decentralized AI systems |
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- Federated learning simulation (training, evaluation, aggregation) |
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- Model bias mitigation and fairness testing |
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- Multi-agent coordination and adversarial fraud modeling |
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# 📜 License |
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**Apache 2.0** — freely usable with attribution |
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# 🧾 Attribution & Citation |
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This dataset was generated and extended by Cifer AI, building on structural principles introduced by: |
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**E. A. Lopez-Rojas, A. Elmir, and S. Axelsson** <br> |
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*PaySim: A financial mobile money simulator for fraud detection.* <br> |
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28th European Modeling and Simulation Symposium – EMSS 2016 |
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