Banking — Automated Model Retraining for Scoring Systems

Overview

The primary purpose of this DAG is to manage the automated retraining of scoring models based on the latest available data. In the banking industry, maintaining the accuracy of scoring models is critical for risk assessment and compliance. This DAG ingests data from multiple sources, including transaction logs and customer behavior analytics, to ensure that the models reflect the most current trends and patterns. The ingestion pipeline begins with the collection of relevant data, followed by a t

The primary purpose of this DAG is to manage the automated retraining of scoring models based on the latest available data. In the banking industry, maintaining the accuracy of scoring models is critical for risk assessment and compliance. This DAG ingests data from multiple sources, including transaction logs and customer behavior analytics, to ensure that the models reflect the most current trends and patterns. The ingestion pipeline begins with the collection of relevant data, followed by a thorough evaluation of existing model performance metrics. If the performance falls below a predefined threshold, the DAG triggers the retraining process. This involves utilizing advanced machine learning techniques to refine the models with the new data. Once retrained, the updated models are automatically deployed into the production environment, ensuring that decision-making processes are based on the latest insights. Monitoring is a key aspect of this DAG; it tracks KPIs such as retraining success rates and the time taken to update models. In case of any failure during retraining, alerts are sent to the MLOps teams for immediate action. The value of this automated process lies in its ability to enhance model accuracy, reduce manual intervention, and ensure compliance with regulatory standards, ultimately leading to improved risk management and operational efficiency.

Part of the Governance & Compliance solution for the Banking industry.

Use cases

• Improves model accuracy for better risk assessment
• Reduces manual effort in model management
• Ensures compliance with regulatory requirements
• Enhances operational efficiency through automation
• Facilitates timely updates to scoring models