An Analytical Model for Evaluating Social Security Schemes-A Focus on “Ayushman Bharat” Universal Health Scheme in India
P. Sunil Kumar1, Raghunatha Sarma2, Satya Sai Mudigonda3
1P.Sunil Kumar, “Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Muddennahalli, Chickballapur, India.
2Dr Raghunatha Sarma,”Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India.
3Sathya Sai Mudigonda, “Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India.
Manuscript received on 12 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 8929-8936 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6631098319/2019©BEIESP | DOI: 10.35940/ijrte.C6631.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Classification, Clustering, Anomaly Detection, Social Security Scheme, Actuarial Techniques, Ayushman Bharat, AB-NHPM.
Scope of the Article: Classification