nithi060488/Cifer-Fraud-Detection-Dataset-AF

---
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

---

---
许可证：apache-2.0
任务类别：
- 表格分类（tabular-classification）
- 特征提取（feature-extraction）
语言：
- 英语（en）
标签：
- 欺诈检测（fraud-detection）
- 金融（finance）
- 联邦学习（federated-learning）
- Cifer
展示名称：Cifer-Fraud-Detection-Dataset-AF
规模类别：
- 1000万<样本量<1亿
---

# 📊 Cifer欺诈检测数据集

## 🧠 数据集概览
**Cifer-Fraud-Detection-Dataset-AF** 是一套高保真全合成数据集，旨在支持金融欺诈检测领域中隐私保护型、联邦式及去中心化机器学习系统的开发与基准测试。

本数据集的结构灵感源自**PaySim模拟器**，该模拟器基于一家在14个以上国家开展业务的真实金融机构的聚合移动货币交易数据构建。Cifer在此基础上将样本量拓展至**2100万条**，并针对**联邦学习（federated-learning）环境**进行了优化，同时通过与真实世界数据集的对比验证了模型性能。

> ### 精度基准测试结果：
> 在该数据集上训练的Cifer模型精度可达**99.93%**，对比真实欺诈数据集的**99.98%基准精度**，可为安全分布式机器学习研究提供高保真的行为模拟数据。

---

## ⚙️ 生成方式
本数据集**完全由合成生成**，通过**Cifer内部仿真引擎**生成，该引擎经过训练可复现移动货币生态系统中常见的金融行为、智能体动态及欺诈策略模式。
- 基于PaySim的结构与仿真动力学
- 针对多智能体测试、联邦分区及异步模型训练进行了增强
- 包含逼真的欺诈标记机制与非均衡标签分布

---

# 🧩 数据结构
| 列名 | 描述 |
|------------------|----------------------------------------------------------------------|
| `step` | 时间单位（1步=1小时）；仿真时长覆盖30天（总计744步） |
| `type` | 交易类型：现金存入（CASH-IN）、现金取出（CASH-OUT）、借记（DEBIT）、支付（PAYMENT）、转账（TRANSFER） |
| `amount` | 交易金额，单位为仿真货币 |
| `nameOrig` | 发送方的匿名ID |
| `oldbalanceOrg` | 交易前发送方账户余额 |
| `newbalanceOrig` | 交易后发送方账户余额 |
| `nameDest` | 接收方的匿名ID |
| `oldbalanceDest` | 交易前接收方账户余额（如适用） |
| `newbalanceDest` | 交易后接收方账户余额（如适用） |
| `isFraud` | 二元标记：若交易为欺诈则为1 |
| `isFlaggedFraud` | 若交易超出标记阈值（例如超过200,000）则为1 |

---

# 📁 文件组织
总记录数：**21,000,000**
为适配大规模学习与联邦学习场景，数据集被拆分为14个文件：
- `Cifer-Fraud-Detection-Dataset-AF-part-1-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-2-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-3-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-4-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-5-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-6-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-7-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-8-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-9-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-10-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-11-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-12-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-13-14.csv` → 150万条记录
- `Cifer-Fraud-Detection-Dataset-AF-part-14-14.csv` → 150万条记录

格式：`.csv`（可根据需求提供`.parquet`或`.json`格式）

---

# ✅ 核心特性
- 全合成生成，可安全公开发布
- 兼容联邦学习（跨域、异步或多智能体场景）
- 适用于隐私保护型机器学习与鲁棒性测试
- 可与真实世界欺诈数据集进行基准对比
- 支持通过分布感知建模开展公平性评估

---

# 🔬 应用场景
- 去中心化AI系统中的欺诈检测基准测试
- 联邦学习仿真（训练、评估、聚合）
- 模型偏差缓解与公平性测试
- 多智能体协同与对抗性欺诈建模

---

# 📜 许可证
**Apache 2.0** — 可自由使用，需注明出处

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# 🧾 署名与引用
本数据集由Cifer AI生成并扩展，其结构原理源自：

**E. A. Lopez-Rojas、A. Elmir与S. Axelsson** <br>
*PaySim：用于欺诈检测的金融移动货币模拟器* <br>
第28届欧洲建模与仿真研讨会——EMSS 2016