Telecom — Telecom Fraud Detection Feature Engineering Pipeline

Overview

The primary purpose of this DAG is to enhance fraud detection capabilities within the telecom industry by creating relevant features from existing data sources. It ingests data from multiple sources, including call detail records, customer transaction logs, and network usage statistics. The ingestion pipeline begins with data extraction, followed by data cleaning and normalization to ensure consistency and quality. Feature engineering techniques, such as feature selection and transformation, are

The primary purpose of this DAG is to enhance fraud detection capabilities within the telecom industry by creating relevant features from existing data sources. It ingests data from multiple sources, including call detail records, customer transaction logs, and network usage statistics. The ingestion pipeline begins with data extraction, followed by data cleaning and normalization to ensure consistency and quality. Feature engineering techniques, such as feature selection and transformation, are applied to derive new features that are more predictive of fraudulent behavior. These features are then stored in a centralized data warehouse, allowing for easy access and integration with machine learning models. Key performance indicators (KPIs) monitored throughout this process include the time taken to create features, the accuracy of the models post-implementation, and the overall impact on fraud detection rates. The business value of this DAG lies in its ability to reduce losses from fraudulent activities, improve customer trust, and streamline operational efficiencies in fraud management.

Part of the Supply/Demand Forecast solution for the Telecom industry.

Use cases

• Reduces financial losses due to fraudulent activities
• Enhances accuracy of fraud detection models
• Improves operational efficiency in fraud management
• Increases customer trust through better security measures
• Enables data-driven decision-making in telecom operations