| --- |
| 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 |
| |
| ```bibtex |
| @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. |
