Open AccessMulti Criteria Decision Making (MCDM) based preference elicitation framework for life insurance recommendation system
Author(s) -
Asha Rani
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.1523
Subject(s) - topsis , computer science , multiple criteria decision analysis , recommender system , life insurance , closeness , selection (genetic algorithm) , plan (archaeology) , preference , operations research , actuarial science , business , artificial intelligence , information retrieval , economics , history , mathematical analysis , mathematics , archaeology , microeconomics , engineering
The global life insurance industry has shown a phenomenal growth in number of companies, insurance products and their users. The digital revolution has played a pivotal role in the field of insurance too. Increased numbers of companies and insurance plans have increased the complexities and time involved in selection of appropriate policies. At present, major share of policy selling goes to the agents which may be biased and time consuming. The web aggregators too have failed to provide customized and personalized suggestions. Major portion of population still finds the selection of best insurance plan unfriendly and tedious. This huge volume of data requires intelligent system to facilitate efficient and effective retrieval, processing and management of the data from multiple dimensions. This research paper proposes a framework to provide a personalized life insurance recommender system using TOPSIS method of multi-criteria decision making. Point allocation method along with TOPSIS provides preference elicitation and list of recommended policies ranked according to closeness coefficients. Sensitivity analysis in the paper shows the effect of changing the policy features’ preferences (criteria weights) on the final recommended products. The proposed framework helps in achieving computational excellence for efficient decision making with reduced complexity