In the dynamic financial sector, ensuring the continuous accuracy and reliability of machine learning models is essential for effective risk management and operational efficiency. With the introduction of regulatory frameworks like the EU Artificial Intelligence Act, the need to monitor and uphold model performance to meet strict compliance standards has become even more pressing. As models undergo development, it is essential to observe how their outcomes shift to ensure they stay within acceptable limits. This presentation outlines a method for continuous monitoring of model drift, thereby closing the Model Lifecycle and guaranteeing ongoing model performance and regulatory adherence.

Our method takes inspiration from statistical techniques and data analysis to detect and respond to model drift promptly. By tracking model performance and comparing it against baseline metrics, our approach ensures that ongoing model development does not compromise accuracy and reliability. This proactive monitoring is vital for financial institutions to mitigate risks associated with outdated or misaligned models and to sustain trust in automated decision-making systems.

The presentation will explain the technical aspects of our method, including its integration into the development pipeline and automation capabilities. For this we leverage the capabilities of data scientists and data engineers to close the Model Lifecycle effectively. Additionally, we will present a case study demonstrating the successful implementation of our method. By framing our method within the Model Lifecycle context, we emphasize its relevance and the benefits it brings to the financial industry. This approach not only enhances model accuracy but also supports the scalability and compliancy of machine learning systems in continuously evolving environments.