AttyAbson/Nigerian-Financial-Transactions-and-Fraud-Detection-Dataset

Nigerian Financial Transactions and Fraud Detection Dataset | Africa (Electric Sheep Africa metadata inventory)

Size category: 1M<n<10M - Formats: csv - Sector: economics_finance - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

Dataset context from the existing Hugging Face card: Nigerian Financial Fraud Detection Dataset (Enhanced) Overview This is a comprehensive synthetic financial fraud detection dataset specifically engineered for the Nigerian fintech ecosystem. The dataset contains 5,000,000 transactions with 45 advanced features including sophisticated user behaviour analytics, device intelligence, risk scoring, and temporal patterns tailored for Nigerian financial fraud detection. We have found that a lot of people are unable to use the full… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Nigerian-Financial-Transactions-and-Fraud-Detection-Dataset.

Dataset Profile

How To Read This Dataset

• —Start from the repository files and the dataset viewer when available.
• —Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
• —Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
• —Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/Nigerian-Financial-Transactions-and-Fraud-Detection-Dataset")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

• —This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
• —Exact schema, row counts, and source files should be inspected in the repository data files.
• —Metadata gaps from the inventory: country, upstream_publisher.
• —Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

• —Source context: Electric Sheep Africa metadata inventory
• —Publisher/source attribution: Public dataset metadata
• —License: gpl
• —Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/Nigerian-Financial-Transactions-and-Fraud-Detection-Dataset
• —Inventory retrieved at: 2026-07-16T16:00:34Z

Suggested Analyses

• —Inspect schema and missingness before modeling.
• —Profile variables by geography, time, and subgroup columns where present.
• —Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
• —Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_nigerian_financial_transactions_and_fraud_detection_dataset_2026,
  title        = {Nigerian Financial Transactions and Fraud Detection Dataset | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/Nigerian-Financial-Transactions-and-Fraud-Detection-Dataset},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/Nigerian-Financial-Transactions-and-Fraud-Detection-Dataset}}
}

License

Released under gpl.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.