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.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: Systems, methods, and apparatuses for detecting and identifying anomalous data in an input data set are provided.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same.