CiferAI/Cifer-Fraud-Detection-Dataset-AF

1---2license: apache-2.03task_categories:4- tabular-classification5- feature-extraction6language:7- en8tags:9- fraud-detection10- finance11- federated-learning12- cifer13pretty_name: Cifer-Fraud-Detection-Dataset-AF14size_categories:15- 10M<n<100M16---17 18# 📊 Cifer Fraud Detection Dataset19 20## 🧠 Overview21The **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.22 23This 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.24 25> ### Accuracy Benchmark:26> 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.27 28---29 30## ⚙️ Generation Method31This 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.32- Based on the structure and simulation dynamics of PaySim33- Enhanced for multi-agent testing, federated partitioning, and async model training34- Includes realistic fraud flagging mechanisms and unbalanced label distributions35 36---37 38# 🧩 Data Structure39| Column Name      | Description                                                                 |40|------------------|-----------------------------------------------------------------------------|41| `step`           | Unit of time (1 step = 1 hour); simulation spans 30 days (744 steps total) |42| `type`           | Transaction type: CASH-IN, CASH-OUT, DEBIT, PAYMENT, TRANSFER               |43| `amount`         | Transaction value in simulated currency                                     |44| `nameOrig`       | Anonymized ID of sender                                                     |45| `oldbalanceOrg`  | Sender’s balance before transaction                                         |46| `newbalanceOrig` | Sender’s balance after transaction                                          |47| `nameDest`       | Anonymized ID of recipient                                                  |48| `oldbalanceDest` | Recipient’s balance before transaction (if applicable)                      |49| `newbalanceDest` | Recipient’s balance after transaction (if applicable)                       |50| `isFraud`        | Binary flag: 1 if transaction is fraudulent                                 |51| `isFlaggedFraud` | 1 if transaction exceeds a flagged threshold (e.g. >200,000)                |52 53---54 55# 📁 File Organization56Total Rows: **21,000,000**57Split into 14 files for large-scale and federated learning scenarios:58- `Cifer-Fraud-Detection-Dataset-AF-part-1-14.csv` → 1.5M rows59- `Cifer-Fraud-Detection-Dataset-AF-part-2-14.csv` → 1.5M rows60- `Cifer-Fraud-Detection-Dataset-AF-part-3-14.csv` → 1.5M rows61- `Cifer-Fraud-Detection-Dataset-AF-part-4-14.csv` → 1.5M rows62- `Cifer-Fraud-Detection-Dataset-AF-part-5-14.csv` → 1.5M rows63- `Cifer-Fraud-Detection-Dataset-AF-part-6-14.csv` → 1.5M rows64- `Cifer-Fraud-Detection-Dataset-AF-part-7-14.csv` → 1.5M rows65- `Cifer-Fraud-Detection-Dataset-AF-part-8-14.csv` → 1.5M rows66- `Cifer-Fraud-Detection-Dataset-AF-part-9-14.csv` → 1.5M rows67- `Cifer-Fraud-Detection-Dataset-AF-part-10-14.csv` → 1.5M rows68- `Cifer-Fraud-Detection-Dataset-AF-part-11-14.csv` → 1.5M rows69- `Cifer-Fraud-Detection-Dataset-AF-part-12-14.csv` → 1.5M rows70- `Cifer-Fraud-Detection-Dataset-AF-part-13-14.csv` → 1.5M rows71- `Cifer-Fraud-Detection-Dataset-AF-part-14-14.csv` → 1.5M rows72 73Format: `.csv` (optionally `.parquet` or `.json` upon request)74 75---76 77# ✅ Key Features78- Fully synthetic and safe for public release79- Compatible with federated learning (cross-silo, async, or multi-agent)80- Ideal for privacy-preserving machine learning and robustness testing81- Benchmarkable against real-world fraud datasets82- Supports fairness evaluation via distribution-aware modeling83 84---85 86# 🔬 Use Cases87- Fraud detection benchmarking in decentralized AI systems88- Federated learning simulation (training, evaluation, aggregation)89- Model bias mitigation and fairness testing90- Multi-agent coordination and adversarial fraud modeling91 92---93 94# 📜 License95**Apache 2.0** — freely usable with attribution96 97---98 99# 🧾 Attribution & Citation100This dataset was generated and extended by Cifer AI, building on structural principles introduced by:101 102**E. A. Lopez-Rojas, A. Elmir, and S. Axelsson** <br>103*PaySim: A financial mobile money simulator for fraud detection.* <br>10428th European Modeling and Simulation Symposium – EMSS 2016105 106---