Telecom — Fraud Detection Model Retraining Pipeline

Overview

The primary purpose of this DAG is to automate the retraining of fraud detection models within the telecom industry, ensuring that these models remain effective in identifying fraudulent activities. The pipeline begins with the ingestion of recent transaction data, including call detail records and customer behavior logs, which serve as the foundation for model retraining. Following data ingestion, the pipeline processes this data through several transformation steps, including data cleaning, fe

The primary purpose of this DAG is to automate the retraining of fraud detection models within the telecom industry, ensuring that these models remain effective in identifying fraudulent activities. The pipeline begins with the ingestion of recent transaction data, including call detail records and customer behavior logs, which serve as the foundation for model retraining. Following data ingestion, the pipeline processes this data through several transformation steps, including data cleaning, feature extraction, and model training. Each retrained model is rigorously evaluated against predefined performance metrics, such as precision and recall, to assess improvements over previous iterations. The outputs of this process include retrained models, performance reports, and deployment readiness assessments. Key performance indicators (KPIs) for monitoring include the percentage improvement in fraud detection accuracy and the average retraining time. By continuously updating the fraud detection models, this DAG provides significant business value by reducing financial losses due to fraud and enhancing customer trust in telecom services.

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

Use cases

• Reduces financial losses from undetected fraudulent activities
• Enhances customer trust through improved service reliability
• Increases operational efficiency with automated processes
• Provides timely updates to adapt to evolving fraud tactics
• Supports data-driven decision-making in fraud prevention strategies