SUMMARY
Complex machine learning problems often involve dynamic processes and unobserved variables, making them challenging to solve. This paper presents a framework combining human expertise with TAZI’s Continuous Learning ML platform to tackle such problems effectively.
Using auto insurance as an example, we explore how human insights enhance machine learning predictions, enabling better decisions for pricing, loss ratio management, and adapting to unpredictable factors like economic shifts.