Create README.md

# 📊 Cifer Fraud Detection Dataset

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.

README.md

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+---
+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
+
+---