arun-gharami/lead-ai-fraud-detection-dataset

📊 Lead.AI Fraud Detection Dataset

5,000-Row Synthetic Tabular Benchmark — Ready to Train, Ready to Publish

Published by Lead.AI Labs · Author: Arun Kumar Gharami

---

What This Dataset Is For

A clean, Parquet-formatted, immediately loadable synthetic fraud detection dataset built for researchers, ML engineers, and course instructors who need realistic tabular financial data without the legal complexity of real transaction data.

Use it to:

• Build and benchmark fraud classifiers in hours, not weeks
• Run XAI / SHAP experiments on financial features
• Teach imbalanced classification in courses or workshops
• Prototype a fraud detection proof-of-concept for a client demo

⚠️ Synthetic data. No real customers, no real transactions, no PII. Safe to use, share, and publish.

---

Dataset at a Glance

Property	Value
Rows	5,000
Features	14
Target	risk_label (0 = normal, 1 = fraud)
Format	Parquet
License	Apache 2.0
Split	train (5,000 rows)

---

Data Fields

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

---

Load in 3 Lines

from datasets import load_dataset

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

With pandas directly

import pandas as pd

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

With DuckDB (fast SQL on Parquet)

import duckdb

duckdb.query("""
    SELECT risk_label, COUNT(*) as n, ROUND(AVG(transaction_amount), 2) 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()

---

Ready-to-Use Model Trained on This Data

Don't want to train your own? The Lead.AI Fraud Shield is already trained and ready to load:

import joblib, pandas as pd
model = joblib.load("model/model.joblib")   # from lead-ai-fraud-shield repo

---

Need More Data?

This dataset has 5K rows and 14 features — good for fast prototyping and course projects.

For production model training or research publication, use Dataset v2 → 100K rows, 21 features which adds transaction type, day-of-week, geographic region, customer risk score, and more.

---

Bias & Fairness Note

Synthetically generated. Country and device features may encode assumptions that don't reflect real fraud distributions. Audit for proxy discrimination before using a model trained here in any context involving real individuals.

---

Privacy

No PII. All IDs, amounts, and behavioral features are synthetically generated.

---

Citation

@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}}
}

---

Lead.AI Labs — Trustworthy AI Systems for Practical Business Intelligence
lead-ai.us · LinkedIn · GitHub