| --- |
| language: |
| - en |
| tags: |
| - telecom |
| - fraud |
| - nigeria |
| - cyber |
| size_categories: |
| - 10K<n<100K |
| --- |
| <video controls style="width: 100%; max-width: 720px;"> |
| <source src="https://huggingface.co/datasets/kagikush/Nigerian_telecom_fraud/resolve/main/nigerian%20fraud%20-%20video%20exp.mp4" type="video/mp4"> |
| Your browser does not support the video tag. |
| </video> |
|
|
| # 🇳🇬 Nigerian Telecom Fraud Analysis |
|
|
| ## Overview |
|
|
| This project presents an end-to-end Exploratory Data Analysis (EDA) of telecom-related fraud incidents in Nigeria. The goal is to uncover patterns, quantify financial impact, and provide actionable recommendations to mitigate fraud. |
|
|
| The analysis is based on a dataset containing ~50,000 fraud records, including fraud type, timestamps, operators, and financial impact. |
|
|
| --- |
|
|
| ## Objectives |
|
|
| * Understand the distribution of fraud types |
| * Identify the most financially damaging fraud categories |
| * Compare fraud activity across telecom operators |
| * Provide business-oriented recommendations |
|
|
| --- |
|
|
| ## Dataset Description |
|
|
| Key columns in the dataset: |
|
|
| * `fraud_id` – Unique identifier for each fraud case |
| * `detected_at` – Timestamp when fraud was detected |
| * `fraud_type` – Category of fraud (e.g., phishing, account takeover) |
| * `operator` – Telecom operator involved |
| * `affected_phone` – Victim phone number |
| * `customer_id` – Customer identifier |
| * `financial_loss_ngn` – Estimated financial loss in Nigerian Naira |
|
|
| --- |
|
|
| ## Data Cleaning |
|
|
| Steps performed: |
|
|
| * Converted `detected_at` to datetime format |
| * Handled missing values in timestamps |
| * Standardized categorical values (fraud types, operators) |
| * Removed inconsistencies and duplicates where necessary |
|
|
| --- |
|
|
| ## Exploratory Data Analysis |
|
|
| ### 1. Fraud Type Distribution |
|
|
| * The most common fraud types include: |
|
|
| * call forwarding fraud |
| * identity theft |
| * other frauds occur in Relatively similar frequency. |
| |
|
|
|  |
|
|
| ### 2. Financial Impact Analysis |
|
|
| * Highest average losses: |
|
|
| * **Account Takeover** |
| * **Subscription Fraud** |
| * Moderate impact: |
|
|
| * Phishing |
| * Call forwarding fraud |
| * Low impact: |
|
|
| * IMEI spoofing and similar technical frauds |
|
|
| Insight: Frequency does not necessarily equal severity. Some rare frauds are far more damaging. |
|  |
|
|
|
|
| ### 3. Operator Comparison |
|
|
| * Fraud cases are distributed across multiple operators |
| * Some operators(MTN and Airtel) show higher exposure to specific fraud types |
|
|
| Insight: Targeted prevention strategies may be needed per operator. |
|
|
|  |
|
|
| --- |
| ### 3. Fraud location Comparison |
|
|
| * Fraud cases are distributed across multiple locations |
| * It is evident that most fraud cases occur in Lagos/ |
|
|
| Insight: Lagos is seriously suspected of fraud. |
|
|
|
|
|  |
|
|
| ## Key Insights |
|
|
| * A small number of fraud types account for the majority of financial loss |
| * High-frequency fraud is not always high-impact |
| * Fraud patterns suggest both opportunistic and organized activity |
|
|
| --- |
|
|
| ## Recommendations |
|
|
| ### Focus on High-Impact Fraud |
|
|
| * Prioritize detection systems for: |
|
|
| * Account takeover |
| * Subscription fraud |
| * Allocate more resources to prevent high-loss incidents |
|
|
| ### Improve Detection Systems |
|
|
| * Implement anomaly detection for unusual account behavior |
| * Use machine learning models for early fraud identification |
|
|
| ### Strengthen Customer Protection |
|
|
| * Introduce multi-factor authentication (MFA) |
| * Send real-time alerts for suspicious activities |
|
|
| ### Operator-Specific Strategies |
|
|
| * Customize fraud prevention measures per telecom operator |
| * Monitor operator-specific fraud trends |
|
|
| ### Continuous Monitoring |
|
|
| * Build dashboards for real-time fraud tracking |
| * Regularly update models and rules |
|
|
| --- |
|
|
| ## Tools & Technologies |
|
|
| * Python (Pandas, NumPy) |
| * Data Visualization (Matplotlib, Seaborn) |
| * Jupyter Notebook |
|
|
| --- |
|
|
| ## How to Use |
|
|
| 1. Clone the repository |
| 2. Open the notebook in Jupyter |
| 3. Run all cells sequentially |
| 4. Explore visualizations and insights |
|
|
| --- |
|
|
| ## Future Work |
|
|
| * Build predictive models for fraud detection |
| * Incorporate real-time streaming data |
| * Add geolocation-based analysis |
| * Deploy as an interactive dashboard |
|
|
| --- |
|
|
| ## Contributing |
|
|
| Contributions are welcome! Feel free to open issues or submit pull requests. |
|
|
| --- |
|
|
| ## License |
|
|
| This project is for educational and analytical purposes. |
|
|
| --- |
| ## Data Source |
|
|
| The dataset used in this project was sourced from [nigerian-telecom-fraudulent-activity-datasets / huggingface]. |
|
|
| ## Acknowledgments |
|
|
| This analysis was conducted as part of a Data Science assignment at Reichman University. The dataset is used for educational purposes only. |
|
|
