---
license: apache-2.0
language:
- en
task_categories:
- tabular-classification
tags:
- fraud-detection
- tabular
- financial
- trustworthy-ai
- responsible-ai
- explainable-ai
- xai
- synthetic-data
- lead-ai-labs
size_categories:
- 1K<n<10K
---

# Lead.AI Fraud Detection Dataset

**5,000-row synthetic tabular dataset for fraud detection research, XAI benchmarking, and predictive model training**

> Published by [Lead.AI Labs](https://www.lead-ai.us)  
> Author: [Arun Kumar Gharami](https://huggingface.co/arun-gharami) | [Google Scholar](https://scholar.google.com/citations?user=uy4i5soAAAAJ&hl=en)

---

## Dataset Summary

This dataset contains 5,000 synthetic financial transactions labeled as fraud (1) or normal (0).
It was designed to support research and prototyping in explainable fraud detection, featuring
a balanced mix of behavioral, temporal, and merchant-level risk signals.

> ⚠️ **Synthetic Data Disclaimer:** This dataset is **entirely synthetic**. It was generated
> algorithmically to simulate plausible transaction fraud patterns for research and educational
> use. It contains **no real customer data, no real financial transactions, and no personally
> identifiable information (PII)**. It must not be treated as representative of any real-world
> fraud distribution.

---

## Supported Tasks

- Binary fraud classification (`risk_label`: 0 = normal, 1 = fraud)
- Explainable AI (XAI) feature attribution research
- Imbalanced classification benchmarking
- Financial risk signal analysis

---

## Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `transaction_id` | string | Unique transaction identifier |
| `customer_id` | string | Unique customer identifier |
| `transaction_amount` | float | Transaction value in currency units |
| `transaction_hour` | int | Hour of transaction (0–23) |
| `account_age_days` | int | Days since account creation |
| `previous_chargebacks` | int | Historical chargeback count |
| `merchant_category` | string | Merchant type (online_services, grocery, restaurant, electronics, travel, fuel, fashion) |
| `transaction_country` | string | Country code (US, UK, CA, AU, IN) |
| `device_type` | string | Device used (mobile, desktop, tablet) |
| `is_international` | int | 1 = international transaction |
| `is_high_risk_merchant` | int | 1 = high-risk merchant category |
| `transaction_velocity_1h` | int | Transaction count in last 1 hour |
| `transaction_velocity_24h` | int | Transaction count in last 24 hours |
| `avg_transaction_amount_30d` | float | 30-day average transaction amount |
| `risk_label` | int | **Target variable** — 0 = normal, 1 = fraud |

---

## Example Rows

| transaction_id | transaction_amount | transaction_hour | account_age_days | is_international | risk_label |
|----------------|-------------------|------------------|------------------|-----------------|------------|
| TXN0000047 | 922.01 | 1 | 12 | 1 | 1 (fraud) |
| TXN0000094 | 149.22 | 14 | 340 | 0 | 1 (fraud) |
| TXN0000099 | 70.30 | 9 | 892 | 0 | 1 (fraud) |
| TXN0001234 | 45.80 | 11 | 720 | 0 | 0 (normal) |

---

## How to Load

### With `datasets` (Hugging Face)
```python
from datasets import load_dataset

ds = load_dataset("arun-gharami/lead-ai-fraud-detection-dataset")
df = ds["train"].to_pandas()
print(df.head())
```

### With `pandas`
```python
import pandas as pd

url = "hf://datasets/arun-gharami/lead-ai-fraud-detection-dataset/data/train-00000-of-00001.parquet"
df = pd.read_parquet(url)
print(df.shape)
print(df["risk_label"].value_counts())
```

### With DuckDB (Parquet)
```python
import duckdb

result = duckdb.query("""
    SELECT risk_label, COUNT(*) as count, AVG(transaction_amount) as avg_amount
    FROM read_parquet('hf://datasets/arun-gharami/lead-ai-fraud-detection-dataset/data/train-00000-of-00001.parquet')
    GROUP BY risk_label
""").df()
print(result)
```

---

## Dataset Splits

| Split | Rows |
|-------|------|
| train | 5,000 |

---

## Intended Use

- Training and evaluating fraud detection classifiers
- XAI research: SHAP, LIME, feature attribution on tabular financial data
- Academic benchmarking and reproducibility studies
- Prototyping fraud detection dashboards and APIs

## Non-Intended Use

- Training production fraud systems deployed on real customer data
- Academic claims about real-world fraud rates or distributions
- Any use involving actual customer financial data

---

## Bias and Fairness Note

This dataset was synthetically generated and may encode assumptions about the relationship
between demographic signals (country, device type) and fraud likelihood that do not reflect
real-world distributions. Users should audit for proxy discrimination before using any
model trained on this data in a context involving real individuals.

---

## Privacy Note

This dataset contains **no real PII**. All transaction IDs, customer IDs, amounts, and
behavioral features are synthetically generated.

---

## Citation

```bibtex
@misc{gharami2024frauddataset,
  author       = {Arun Kumar Gharami},
  title        = {Lead.AI Fraud Detection Dataset: Synthetic Tabular Benchmark for XAI Research},
  year         = {2024},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/arun-gharami/lead-ai-fraud-detection-dataset}}
}
```

---

## License

Apache 2.0

---

*Lead.AI Labs — Trustworthy AI Systems for Practical Business Intelligence*  
[lead-ai.us](https://www.lead-ai.us) · [GitHub](https://github.com/Arungharami) · [LinkedIn](https://www.linkedin.com/in/arunkgharami)