Credit risk is a crucial aspect in the financial industry, with organizations continuously seeking ways to improve their credit risk models and minimize potential losses. Scoring credit risk accurately in real-time has been, until now, an unreachable goal. The legacy approach uses stale data and too many cutoffs, resulting in less than ideal results.

Hopsworks will demonstrate a machine learning driven approach and an end-to-end solution that provides accurate real-time results at extreme scale and that incorporates graph features, pipeline management, alerting & workflow, and visualization.

With Hopsworks' feature store and vector database, organizations can store and manage credit risk features that are used to train their models, leading to more accurate and efficient predictions by:

• Offering a comprehensive solution for managing data and building machine learning models for credit risk.
• Using its feature store and vector database allow for efficient storage and management of credit risk features used in model training.
• Supporting scalable model training on GPUs, Hopsworks enables high-performance even with large and complex datasets often involved in credit risk.
• Its open-source foundation models provide a solid starting point for building accurate credit risk models.

Join us as we delve into the entire life cycle of building and deploying credit risk models using Hopsworks and open-source foundation models. With its powerful capabilities, Hopsworks offers a comprehensive platform for organizations to effectively manage credit risk and leverage cross-risk synergies. Don't miss this opportunity to learn how Hopsworks can help your organization optimize its credit risk assessment and management processes.