A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer. If any of the fluidless predictive models determines a high risk target, the applicant is required to submit clinical data.