Datasetpublic

Etherlabs/ios-risk-finetune-v1

IOS Risk Fine-Tune Dataset v1 Instruction-tuning dataset for financial risk and fraud detection. Built by the IOS Risk Data Foundry pipeline. Sources Source Records Description Credit Card Fraud (real) ~284k Kaggle creditcard.csv with engineered features Synthetic Transactions ~10k Statistically sampled from real distributions SEC EDGAR 10-K Filings varies Regulatory risk language from public filings Format Alpaca instruction… See the full description on the dataset page: https://huggingface.co/datasets/Etherlabs/ios-risk-finetune-v1.

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

0likes11downloads

Dataset Card

IOS Risk Fine-Tune Dataset v1

Instruction-tuning dataset for financial risk and fraud detection. Built by the IOS Risk Data Foundry pipeline.

Sources

Source	Records	Description
Credit Card Fraud (real)	~284k	Kaggle creditcard.csv with engineered features
Synthetic Transactions	~10k	Statistically sampled from real distributions
SEC EDGAR 10-K Filings	varies	Regulatory risk language from public filings

Format

Alpaca instruction format — three fields per record:

json

{
  "instruction": "Classify this financial transaction as FRAUD or LEGITIMATE based on the features provided.",
  "input": "Amount: $149.62 | Hour: 0 | OffHours: 1 | ...",
  "output": "LEGITIMATE"
}

Usage

python

from datasets import load_dataset
ds = load_dataset("Etherlabs/ios-risk-finetune-v1")

Features Engineered

—amount_zscore — how unusual the amount is vs dataset mean
—is_round_amount — round number flag (structuring signal)
—is_micro_txn — micro transaction flag (card testing signal)
—is_large_txn — above 95th percentile flag
—txn_count_1h / txn_count_24h — velocity features
—hour_of_day / is_off_hours — time-based fraud signals