Banking — Automated Model Retraining for Scoring Efficiency

Overview

The primary purpose of this DAG is to automate the retraining process of scoring models in the banking sector whenever performance drifts are detected. By continuously monitoring model performance against historical data, this workflow ensures that scoring models remain effective and relevant. The architecture consists of a series of interconnected nodes that handle data ingestion, processing, validation, and output generation. Initially, the DAG ingests historical transaction data and model per

The primary purpose of this DAG is to automate the retraining process of scoring models in the banking sector whenever performance drifts are detected. By continuously monitoring model performance against historical data, this workflow ensures that scoring models remain effective and relevant. The architecture consists of a series of interconnected nodes that handle data ingestion, processing, validation, and output generation. Initially, the DAG ingests historical transaction data and model performance metrics from various sources, such as ERP transaction logs and customer behavior analytics. Once the data is collected, it undergoes a series of processing steps that include anomaly detection, model retraining, and validation. The retraining process utilizes machine learning algorithms to update the models based on the latest data trends. After retraining, the models are validated to ensure they meet predefined performance criteria. In the event of a validation failure, alerts are triggered to notify the data science team for immediate intervention. The outputs of this DAG include updated scoring models and performance reports, which are essential for maintaining operational efficiency. Monitoring key performance indicators (KPIs) such as model accuracy and retraining frequency allows for ongoing assessment of model effectiveness. This automated retraining process provides significant business value by reducing manual intervention, improving model accuracy, and ensuring compliance with regulatory standards.

Part of the Predictive Maintenance solution for the Banking industry.

Use cases

• Enhances model accuracy and reliability in scoring
• Reduces operational risks associated with outdated models
• Improves compliance with regulatory requirements
• Facilitates rapid response to performance issues
• Increases overall efficiency of data science operations