| | --- |
| | 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: |
| | - 10M<n<100M |
| | --- |
| | |
| | # 📊 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 **21 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: **21,000,000** |
| | Split into 14 files for large-scale and federated learning scenarios: |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-1-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-2-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-3-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-4-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-5-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-6-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-7-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-8-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-9-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-10-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-11-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-12-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-13-14.csv` → 1.5M rows |
| | - `Cifer-Fraud-Detection-Dataset-AF-part-14-14.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 |
| |
|
| | --- |
