Abstract: There is a need for more effective and efficient data prioritization with respect to predictive input entities across predictive input channels. This need can be addressed by, for example, techniques for prospective prioritization that utilize supervised machine learning models. In one example, a method includes determining a prospective priority score for each predictive input entity of a group of predictive input entities based on a predictive input channel for the predictive input entity and performing prospective prioritization of the group of predictive input entities based on each prospective priority score for a predictive input entity.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for verifying the coordination of benefits information with an end-to-end automated process. First, one or more machine-learning models generate predictions for members who are likely to have insurance with another insurer. The members identified are processed through another one or more machine learning models that generate predictions for who the likely other insurers are. Each insurer is associated with an insurer record/profile that identifies one or more application programming interface templates. The API-based eligibility request templates can be automatically populated to generate eligibility API-based eligibility requests. And in turn, eligibility responses are received and used to update corresponding member profiles and process claims accordingly.