---
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 **12 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: **12,000,000**
Split into 8 folders/files for large-scale and federated learning scenarios:
- `Cifer-Fraud-Detection-Dataset-AF-part-1-8.csv` → 1.5M rows
- `Cifer-Fraud-Detection-Dataset-AF-part-2-8.csv` → 1.5M rows
- `Cifer-Fraud-Detection-Dataset-AF-part-3-8.csv` → 1.5M rows
- `Cifer-Fraud-Detection-Dataset-AF-part-4-8.csv` → 1.5M rows
- `Cifer-Fraud-Detection-Dataset-AF-part-5-8.csv` → 1.5M rows
- `Cifer-Fraud-Detection-Dataset-AF-part-6-8.csv` → 1.5M rows
- `Cifer-Fraud-Detection-Dataset-AF-part-7-8.csv` → 1.5M rows
- `Cifer-Fraud-Detection-Dataset-AF-part-8-8.csv` → 1.5M rows

Format: `.csv` (optionally `.parquet` or `.json` upon request)

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# ✅ 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

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# 🔬 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

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# 📜 License
**Apache 2.0** — freely usable with attribution

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# 🧾 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

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