Case Study: Mission Lane achieves unified real-time and batch credit decisioning with Chalk

Mission Lane’s Source of Truth for Credit Decisioning with Chalk

Mission Lane, a fintech company, faced significant challenges with its fragmented feature infrastructure. With feature logic spread across Python, SQL, and various production systems, the company struggled with inconsistencies, duplication, and potential drift between its model training and production environments. This was a critical bottleneck for their core operations in credit underwriting and fraud detection, hampering their ability to scale and innovate quickly.

By implementing the Chalk feature platform, Mission Lane unified its feature definitions and computation across all environments. The solution provided native support for their existing Python and SQL tools, enabling consistent execution for both batch processing and real-time decisioning. This resulted in the time to deploy new features being reduced from weeks to days, eliminated training-serving drift, and empowered data scientists to work self-serve. Chalk’s platform also extended beyond machine learning to power customer-facing features and operational tooling.

Mission Lane

Mike Kuhlen

ML Solutions & Strategy