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alert_id
stringlengths
36
36
alert_date
timestamp[ns]date
2023-01-01 00:00:00
2024-12-30 00:00:00
customer_id
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alert_type
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4 values
alert_amount_ngn
float64
100k
100M
risk_score
float64
0.5
1
investigation_days
int64
1
38
true_positive
bool
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End of preview. Expand in Data Studio

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}}
}

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