README.md

metadata

license: cc-by-4.0
task_categories:
  - tabular-classification
tags:
  - cybersecurity
  - fraud
  - mobile-banking
  - phishing
  - fintech
  - sub-saharan-africa
  - synthetic
  - scam
pretty_name: African Mobile Banking Phishing
size_categories:
  - 10K<n<100K
language:
  - en
configs:
  - config_name: baseline
    data_files: data/phishing_baseline.csv
  - config_name: ai_amplification
    data_files: data/phishing_ai_amplification.csv
  - config_name: enhanced_protection
    data_files: data/phishing_enhanced_protection.csv
data_type: synthetic

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

African Mobile Banking Phishing

Synthetic dataset on mobile banking phishing attacks across 15 African economies. Tracks attack vectors (SMS/USSD), success rates, financial losses, victim demographics, and detection patterns. Designed for fraud detection research, cybersecurity analysis, and fintech risk assessment.

Dataset Description

• 18,000 synthetic phishing campaign records
• 15 countries: Major mobile money markets in Africa
• 3 scenarios: baseline, ai_amplification, enhanced_protection
• 26 variables per record

Variables

Variable	Type	Description
record_id	string	Unique campaign identifier
year	int	Year of record
country	string	Target country
region	string	Regional grouping
mobile_maturity_index	float	Mobile money maturity 0-1
campaign_id	string	Unique campaign ID
attack_vector	string	sms_phishing, ussd_phishing, voice, social_media
target_platform	string	Target banking app
num_targets	int	Number of targets in campaign
num_successful_compromises	int	Successful infections
attack_success_rate_pct	float	Success rate percentage
total_financial_loss_usd	float	Total loss in USD
average_loss_per_victim_usd	float	Average victim loss
campaign_duration_days	float	Campaign duration
fraud_stage	string	Attack stage
message_theme	string	Phishing theme
sophistication_score	float	Attack sophistication 0-1
target_sophistication_needed	float	Target sophistication needed
time_to_detect_days	float	Detection time
detection_rate_pct	float	Detection rate
user_awareness_score	float	User awareness level
law_enforcement_response	string	Law enforcement action
victim_age_group	string	Victim age distribution
victim_education	string	Victim education level
reporting_rate_pct	float	Victim reporting rate
repeat_victim_pct	float	Repeat victim percentage
cross_border_attack	int	Cross-border attack flag
scenario	string	baseline, ai_amplification, enhanced_protection

Scenarios

• baseline (6K): Pre-AI phishing, traditional methods, 2018-2021. SMS/USSD vectors, moderate success rates.
• ai_amplification (6K): AI-powered attacks, deepfakes, 2022-2024. Higher sophistication, increased losses.
• enhanced_protection (6K): Better detection, awareness, 2025-2026. Reduced success rates, faster detection.

Generation Methodology

Parameters calibrated against:

• GASA State of Scams in Africa 2025
• SABRIC South Africa Crime Statistics 2024
• Kenya Banking Fraud Losses 2025 ($1.6B)
• Nature smishing attacks research
• ADF Magazine mobile money security analysis

Fraud rates based on:

• Kenya: 4.2% fraud rate, M-Pesa ecosystem
• Nigeria: 5.5% fraud rate
• South Africa: 3.8% fraud rate, 74% increase in 2025
• 68% of Africans report scam experience

Use Cases

• Phishing detection model training
• Fraud pattern analysis
• Financial loss forecasting
• User awareness campaign planning
• Law enforcement resource allocation
• Cross-border threat intelligence

Citation

@dataset{african_mobile_banking_phishing_2026,
  title={African Mobile Banking Phishing Dataset},
  author={ElectricSheepAfrica},
  year={2026},
  license={cc-by-4.0}
}

License

CC BY 4.0 - This is synthetic data generated for research and educational purposes.