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The government initiated social security schemes in countries such as India, target a large proportion of the population to provide various types of benefits that involve a number of stakeholders. Such schemes are executed by a large number of transactions between the Government agencies and the other stakeholders on a real time basis, thus resulting in large data sets. Current research advancements in the domain of social security schemes include analysis of sequential activities and debt occurrences for such transactions at the national level only. It has been a challenge in recent times to monitor and evaluate the performance of such gigantic schemes which also involves financial decision making at different levels. This paper proposes an innovative frame-work that combines data mining strategies with actuarial techniques to evaluate one of the popular schemes in India, namely AB-PMJAY (“Ayushman Bharat–Pradhan Mantri Jan Arogya Yojana”) launched by the Government in 2018 at family level. In the proposed framework, the scheme has been divided into a number of sub-processes for which various data mining techniques such as, clustering, classification, anomaly detection and actuarial techniques for pricing are proposed to evaluate the scheme effective at micro level.