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Designing and deploying insurance recommender systems using machine learning
Author(s) -
Qazi Maleeha,
Tollas Kaya,
Kanchinadam Teja,
Bockhorst Joseph,
Fung Glenn
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1363
Subject(s) - recommender system , computer science , portfolio , product (mathematics) , order (exchange) , bayesian network , machine learning , artificial intelligence , collaborative filtering , personalized marketing , deep learning , face (sociological concept) , new product development , bayesian probability , data science , marketing , world wide web , business , finance , digital marketing , social science , geometry , mathematics , sociology , business to government , return on marketing investment
Recommender systems have become extremely important to various types of industries where customer interaction and feedback is paramount to the success of the business. For companies that face changes that arise with ever‐growing markets, providing product recommendations to new and existing customers is a challenge. Our goal is to give our customers personalized recommendations based on what other similar people with similar portfolios have, in order to make sure they are adequately covered for their needs. Our system uses customer characteristics in addition to customer portfolio data. Since the number of possible recommendable products is relatively small, compared to other recommender domains, and missing data is relatively frequent, we chose to use Bayesian Networks for modeling our systems. We also present a deep‐learning‐based approach to provide recommendations to prospects (potential customers) where only external marketing data is available at the time of prediction. This article is categorized under: Application Areas > Industry Specific Applications Algorithmic Development > Structure Discovery Algorithmic Development > Bayesian Models Technologies > Machine Learning