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