electricsheepafrica/africa-synth-banking-aml-alerts-nigeria

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

AML (Anti-Money Laundering) Alerts

Dataset Description

Nigerian AML (Anti-Money Laundering) transaction alerts with true positive labels

This is a production-grade synthetic dataset with authentic Nigerian banking context, designed for AML investigation optimization, false positive reduction, and compliance automation.

Key Features

• 🇳🇬 Nigerian Context: Real bank names, states, demographics
• 📊 ML-Ready Labels: True positive flag (5.0% prevalence)
• ⚡ Large Scale: 2,000,000 rows
• 🎯 Realistic Distributions: Large lognormal amounts, alert type distributions, risk scores
• 📅 Temporal Coverage: 2023-01-01 to 2024-12-31

Dataset Structure

Splits

• Train: 1,400,000 rows (70%)
• Validation: 300,000 rows (15%)
• Test: 300,000 rows (15%)

Schema

Key columns: alert_id, alert_date, customer_id, alert_type, alert_amount_ngn, risk_score, investigation_days, true_positive

Usage

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("electricsheepafrica/nigerian_aml_alerts")

# Load specific split
train_data = load_dataset("electricsheepafrica/nigerian_aml_alerts", split="train")

Labels

• Type: True positive flag (5.0% prevalence)
• Use Case: AML investigation optimization, false positive reduction, and compliance automation

Nigerian Context

Nigerian AML patterns, alert types (structuring, rapid movement, high-risk countries, cash intensive), CBN compliance requirements, investigation workflows, and factors distinguishing true positives from false positives.

Citation

@dataset{nigerian_banking_aml_alerts,
  title = {Nigerian AML (Anti-Money Laundering) Alerts},
  author = {Electric Sheep Africa},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/nigerian_aml_alerts}}
}

License

Apache 2.0 - Free for commercial and research use.

Contact

• Organization: Electric Sheep Africa
• Collection: Nigeria Banking & Finance Datasets