Hungarian Technique and Selection of Optimized Plan of Insurance

Vipin Saxena *

Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India.

Versha Verma

Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India.

Vishal Verma

Department of Computer Applications and Science, School of Management and Science, Lucknow, 226027, India.

Karm Veer Singh

Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India.

*Author to whom correspondence should be addressed.


Abstract

Objectives: Customers are generally confused while taking a plan of Life Insurance of India (LIC) which is having huge amount of database stored over the cloud server. The customer has to select the optimum plan for getting insurance either for own or for family members.

Method: The present paper represents use of a mathematical technique for retrieving the optimum policy plan which shall be suitable as per need of the customer. In this regard, a well-known Hungarian technique is applied for selection of best policy plan by the customer. From the literature, it is revealed that the said technique is used by the researchers for optimizing the big data related to business organizations.

Tools Used: A sample of the database is stored into a matrix form and computations have been performed to
suggest accurate policy plans based on age, amount of premium, etc. A well know python programming is used to execute the proposed system model.

Testing: The model is tested over the collected data from the LIC of India and computed results are given in the form of tables. The proposed methodlogy can be used for the business organizations related to the insurance sectors and others.

Keywords: Customer, database, LIC, policy plan, Hungarian method, optimization


How to Cite

Saxena , Vipin, Versha Verma, Vishal Verma, and Karm Veer Singh. 2024. “Hungarian Technique and Selection of Optimized Plan of Insurance”. Journal of Advances in Mathematics and Computer Science 39 (3):11-19. https://doi.org/10.9734/jamcs/2024/v39i31871.

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