Risk Assessment for Personalized Health Insurance Based on Real-World Data

Aristodemos Pnevmatikakis, Stathis Kanavos, George Matikas, Konstantina Kostopoulou, Alfredo Cesario and Sofoklis Kyriazakos
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Aristodemos Pnevmatikakis: Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium
Stathis Kanavos: Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium
George Matikas: Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium
Konstantina Kostopoulou: Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium
Alfredo Cesario: Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium
Sofoklis Kyriazakos: Innovation Sprint Sprl, Clos Chapelle-aux-Champs 30, 1200 Brussels, Belgium

Risks, 2021, vol. 9, issue 3, 1-15

Abstract: The way one leads their life is considered an important factor in health. In this paper we propose a system to provide risk assessment based on behavior for the health insurance sector. To do so we built a platform to collect real-world data that enumerate different aspects of behavior, and a simulator to augment actual data with synthetic. Using the data, we built classifiers to predict variations in important quantities for the lifestyle of a person. We offer a risk assessment service to the health insurance professionals by manipulating the classifier predictions in the long-term. We also address virtual coaching by using explainable Artificial Intelligence (AI) techniques on the classifier itself to gain insights on the advice to be offered to insurance customers.

Keywords: machine learning; classification; explainable AI; risk assessment (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:9:y:2021:i:3:p:46-:d:508517

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