metadata

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
PaySim: A financial mobile money simulator for fraud detection.
28th European Modeling and Simulation Symposium – EMSS 2016