Banking — Automated Model Retraining for Performance Optimization

Overview

The purpose of this DAG is to facilitate the automated retraining of machine learning models in response to observed performance metrics, thereby ensuring optimal model effectiveness in banking applications. The data sources for this workflow include monitoring results, which provide insights into model performance, and new transaction data that reflects current market conditions. The ingestion pipeline begins with the collection of these data inputs, followed by a series of processing steps tha

The purpose of this DAG is to facilitate the automated retraining of machine learning models in response to observed performance metrics, thereby ensuring optimal model effectiveness in banking applications. The data sources for this workflow include monitoring results, which provide insights into model performance, and new transaction data that reflects current market conditions. The ingestion pipeline begins with the collection of these data inputs, followed by a series of processing steps that include performance evaluation, model selection for retraining, and deployment of the updated models. During the performance evaluation step, models are assessed against predefined KPIs to determine if they meet the required standards. Selected models undergo a retraining process using the latest data, ensuring they adapt to recent trends. Quality controls are implemented to validate that the newly trained models comply with regulatory standards, safeguarding against potential risks. In the event of a failure during any step, a notification process is triggered to alert responsible parties for immediate action. The outputs of this DAG include updated model versions, performance reports, and compliance documentation. Monitoring KPIs such as model accuracy, retraining frequency, and compliance status are crucial for ongoing evaluation. The business value of this DAG lies in its ability to maintain high-performing models that drive informed decision-making, reduce operational risks, and enhance customer satisfaction in the banking sector.

Part of the Enterprise Search solution for the Banking industry.

Use cases

• Improved model accuracy leads to better financial predictions
• Reduced operational risk through compliance assurance
• Increased efficiency in model management processes
• Enhanced responsiveness to market changes
• Boosted customer trust through reliable banking services