SUMMARY As the insurance industry continues to improve operating capabilities supporting its core functions through digital transformation, the need to adopt prescriptive data analytics capabilities in order to improve business productivity and automation is becoming table stakes. One such application of prescriptive analytics is in the improvement of underwriting effectiveness by leveraging data to prioritize submissions for underwriting action. This is especially true in the underwriting of Small, Medium Enterprise insurance risks where underwriters spend significant time in assessing and pricing risks, including desirable ones that may end up not getting bound. This solution would establish a continuously updated underwriting score that incorporates new data as it is obtained through the underwriting value chain. This enables the underwriting teams to focus on selling the right risks for the business. By focusing on the right submissions and improving submission processing, insurance carriers can incrementally improve their underwriting cycle times, bind ratios, and profitable growth metrics. In this paper, we outline how TAZI’s Submission Scoring solution works. This solution is based on TAZI’s Continuous and Explainable, No-Code, Machine Learning platform. We describe how continuous learning and dynamic policy submission segmentation help score incoming policy submissions in real-time and improve the automation of submission triage by helping underwriters prioritize their daily workload. We also describe how underwriters and agents can monitor their submission processes to take the next best actions in order to improve profitable growth through New Business transactions.