Model Risk Management in Banking

In today's strict regulatory environment, financial institutions face immense pressure to adopt rigorous model governance, both during development and after deployment. In this landscape, ensuring AI delivers value and accountability requires a comprehensive approach to model risk management.

This paper discusses key considerations for developing a successful model risk management strategy, including the need for model validation focusing on input data quality and model output stability. Learn how Teradata VantageCloud supports a secure, ethical, and governed framework for Trusted AI. Explore key features of ClearScape Analytics™ that support effective, efficient model risk management, including data preparation utilities, model performance diagnostics, attribute curation via a Feature Store, and model lifecycle management.