The rapid advancement of machine learning (ML) has significantly impacted financial analytics. This paper addresses the importance of feedback collection within financial analytics pipelines, highlighting challenges and proposing a framework for effective feedback integration. By focusing on real-time feedback mechanisms, user-friendly interfaces, and diverse data coordination, the proposed solution aims to enhance the reliability and adaptability of ML models over time. Through a comprehensive case study, the framework’s efficacy in improving the performance and usability of financial analytics pipelines is demonstrated.