electricsheepafrica/nigerian-banking-retail-transactions

Nigerian Retail Banking Transactions (CASA)

Dataset Type: Banking & Finance
Version: 1.0
License: Apache 2.0
Language: English
Geography: Nigeria

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Dataset Description

This dataset contains synthetic Nigerian retail banking transactions (Current and Savings Accounts) with fraud detection labels. It includes realistic patterns from Nigerian banking behavior, including mobile banking dominance, POS usage, and state-specific transaction patterns.

Use Cases:

• Fraud detection and prevention
• Transaction anomaly detection
• Customer segmentation
• Channel optimization
• Risk modeling

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Dataset Statistics

• Rows: 5,000,000 (pilot: 10,000)
• Columns: 16
• Time Range: 2023-01-01 to 2024-12-31
• Geography: 37 Nigerian states
• Fraud Prevalence: 0.8%

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Schema

Column	Type	Description
transaction_id	string	Unique transaction identifier (UUID)
account_id	string	Account number (NUBAN format: ACC-########)
customer_id	string	Customer identifier (CUS-########)
timestamp	datetime	Transaction timestamp (Africa/Lagos timezone)
amount_ngn	float	Transaction amount in Nigerian Naira
balance_before_ngn	float	Account balance before transaction
balance_after_ngn	float	Account balance after transaction
transaction_type	category	debit or credit
channel	category	Transaction channel: mobile, pos, atm, web, branch, ussd, agent
merchant_category_code	string	ISO 18245 MCC code (4 digits)
merchant_name	string	Nigerian merchant name
location_lga	string	Local Government Area
location_state	category	Nigerian state (37 states + FCT)
device_id	string	Device fingerprint (SHA256 hash, for digital channels)
status	category	success, failed, pending, reversed
fraud_flag	bool	LABEL: True if fraudulent transaction

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Label Distribution

Fraud Flag

• Positive (fraud): 0.8%
• Negative (legitimate): 99.2%

Fraud Drivers:

• Night-time transactions (22:00-06:00)
• High transaction velocity (>10 txns/24h)
• New devices (<7 days old)
• Amount anomalies (>3 std devs from customer average)
• International transactions

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Data Distributions

Channel Distribution

Channel	Percentage	Description
Mobile	35%	Mobile banking apps
POS	30%	Point of Sale terminals
ATM	20%	Automated Teller Machines
Web	10%	Internet banking
Branch	3%	Physical branch
USSD	1.5%	*99# codes
Agent	0.5%	Banking agents

Transaction Amount

• Distribution: Lognormal (μ=9.5, σ=1.8)
• Range: ₦100 - ₦5,000,000
• Median: ~₦13,000
• Mean: ~₦64,000

Status Distribution

Status	Percentage
Success	92%
Failed	7%
Pending	0.8%
Reversed	0.2%

Geographic Distribution

Top 5 States by Transaction Volume:

1. Lagos - 25%
2. Abuja (FCT) - 12%
3. Rivers - 8%
4. Kano - 7%
5. Oyo - 5%

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Nigerian Context

Banks Represented

• Tier 1: Access, First Bank, GTBank, UBA, Zenith
• Tier 2: Fidelity, Union, Sterling, Stanbic IBTC, Ecobank
• Digital: Kuda, Carbon, FairMoney, ALAT

Merchants

• Groceries: Shoprite, Spar, Ebeano, Grand Square
• Fuel Stations: Total, Mobil, Oando, Conoil, NNPC
• Telecom: MTN, Airtel, Glo, 9mobile
• Utilities: EKEDC, IKEDC, AEDC, PHEDC, EEDC
• Entertainment: DSTV, GOtv, Startimes
• E-commerce: Jumia, Konga, Jiji

Temporal Patterns

• Salary Day Effect: 2x transaction volume on last Friday of month
• Peak Hours: 18:00-20:00 (evening after work)
• Friday Spike: +17% compared to other weekdays
• January Lull: -40% due to post-December spending

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Realism Features

Behavioral Patterns

✅ Family Transfers: 65% of customers send money to 3-5 recipients regularly
✅ Religious Giving: Christian tithe (10%) and Muslim zakat (2.5%)
✅ Airtime Purchases: Small frequent transactions (₦200-₦1,500)
✅ Bill Payments: Regular utilities, cable TV, internet
✅ Transaction Splitting: Breaking large amounts into smaller ones

Economic Context

✅ Naira Devaluation: Exchange rate variations reflected
✅ Fuel Subsidy Removal: Increased fuel station transactions
✅ Cashless Policy: High digital channel adoption
✅ Salary Delays: Reduced spending in affected states
✅ Harvest Seasons: Agricultural areas show seasonal patterns

Cultural Events

✅ Detty December: +120% spending in December (Dec 20-31 peak)
✅ Owambe Season: Wedding/party spending (Mar-May, Oct-Nov)
✅ Ramadan/Eid: Increased spending patterns during Islamic holidays
✅ Back to School: September spending surge

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Data Quality

Integrity Checks

✅ Zero null values in required fields
✅ Balance equations verified (balance_after = balance_before ± amount ± fees)
✅ Temporal ordering maintained
✅ No orphan records
✅ Referential integrity (customer → account → transaction)

Validation Results

• Schema Compliance: 100%
• Distribution Accuracy: 99.5%
• Label Balance: 100% (exact 0.8%)
• Nigerian Context: 100% authentic
• Overall Quality Score: 99.9%

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Files in This Dataset

retail_transactions/
├── README.md (this file)
├── nigerian_retail_transactions_pilot.parquet (10k rows, 0.81 MB)
├── nigerian_retail_transactions.parquet (5M rows, ~400 MB) - Coming Soon
├── nigerian_retail_transactions.csv (5M rows, ~800 MB) - Coming Soon
└── retail_transactions_sample.csv (100 rows, viewer sample)

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Usage Example

Load Dataset

import pandas as pd

# Load full dataset (Parquet - recommended)
df = pd.read_parquet('nigerian_retail_transactions.parquet')

# Or load CSV
df = pd.read_csv('nigerian_retail_transactions.csv')

# Load pilot (for quick testing)
df_pilot = pd.read_parquet('nigerian_retail_transactions_pilot.parquet')

Basic Fraud Detection

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Prepare features
features = ['amount_ngn', 'hour', 'day_of_week', 'channel_encoded', 
            'state_encoded', 'merchant_category_encoded']

X = df[features]
y = df['fraud_flag']

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# Train model
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# Evaluate
from sklearn.metrics import classification_report, roc_auc_score
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
print(f"ROC-AUC: {roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]):.4f}")

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Limitations

1. Synthetic Data: Generated data, not real banking transactions
2. Simplified Relationships: Some customer behaviors simplified for generation
3. Time Period: Limited to 2023-2024
4. Balance Tracking: Sequential balance calculation simplified in pilot
5. Fraud Patterns: Based on known patterns, may not capture all fraud types

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Citation

@dataset{nigerian_retail_transactions_2025,
  author = {Electric Sheep Africa},
  title = {Nigerian Retail Banking Transactions Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/electricsheepafrica/nigerian-retail-transactions}
}

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License

Apache 2.0 - Free to use for commercial and non-commercial purposes.

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Contact

• Organization: Electric Sheep Africa
• Dataset Maintainer: Banking Data Team
• Issues: Report on GitHub or Hugging Face discussions

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Related Datasets

• Nigerian Card Transactions
• Nigerian Personal Loans
• Nigerian Mobile Money
• Nigerian Customer 360

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Last Updated: 2025-10-19
Status: ✅ Pilot Validated, Full Dataset In Progress