Abstract: An automated, real-time mortality classification and signaling system for real-time risk assessment, and adjustment based on an automated selective multi-level triage process, where risks associated with a plurality of risk-exposed individuals are at least partially transferable from a risk-exposed individual to a first insurance system and from the first insurance system to an associated second insurance system, the real-time mortality classification and signaling system accesses a database stored in a memory to retrieve risk classes, identifies and selects a specific risk class associated with the risk of the exposed individual, processes specific parameters of the exposed individual using a machine learning-based pattern recognition to automatically assign risk-exposed individuals with detected non-smoking patterns to a second triage channel, and automatically assigning risk-exposed individuals with detected smoking patterns to a third triage channel as predicted smokers.