Internship Trainee – Sonatrach
AI-Based Electricity Fraud Detection System
View Project Repository
Internship Overview
During my internship at Sonelgaz, I gained practical knowledge of electricity production, transmission, and distribution. I learned how power is delivered to homes through substations and transformers, while also understanding voltage management and safety procedures in high-voltage environments.
Project Overview
During my internship at Sonelgaz, I developed an AI-based system to analyze electricity consumption data and detect abnormal patterns, transforming raw data into actionable insights for fraud detection.
The challenge
Electricity fraud is difficult to detect due to its hidden nature within large datasets, and traditional methods are inefficient. The goal was to build a system capable of automatically identifying subtle anomalies without labeled data.
The solution
I built an AI-powered anomaly detection system using unsupervised machine learning to identify suspicious behavior, supported by an interactive dashboard for visualizing trends, risks, and high-risk regions.
Key Technical Features
Customer risk scoring system
Interactive dashboard (Plotly)
Consumption deviation & behavioral analysis
Regional anomaly detection
View Project Repository
https://github.com/belkacemabdelkadermarouf/sonelgaz-fraud-detection