Exaggerating to Break-Even: Reference-Dependent Moral Hazard in Automobile Insurance Claims

2021 ◽  
Author(s):  
Feng Gao ◽  
Jaimie W. Lien ◽  
Jie Zheng
Keyword(s):  
Moral Hazard ◽  
Automobile Insurance ◽  
Insurance Claims

An Evaluation of the New Japanese Bonus–Malus System with No-claim and Claimed Subclasses

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mahito Okura ◽  
Takuya Yoshizawa ◽  
Motohiro Sakaki
Keyword(s):  
Simulation Analysis ◽  
Insurance Premium ◽  
Automobile Insurance ◽  
Insurance Claims ◽  
Present Value ◽  
Time Discount

AbstractThe purpose of this research is to evaluate the new Japanese Bonus–Malus System (BMS 2012) in automobile insurance, which is an unusual system wherein both no-claim and claimed subclasses exist. To evaluate BMS 2012, we conduct a simulation analysis and compare BMS 2012 with the former Japanese BMS (BMS 2009) in terms of the present value of the total insurance premium that is closely related to the frequency of insurance claims. Based on the comparison, our main conclusion is that BMS 2012 offers more effects to lower the frequency of insurance claims than BMS 2009 does when the policyholders’ classes in BMS are high classes that evaluate as safety drivers, time discount and/or renewal rates are relatively low, and the policyholders’ risk averseness is large.

Automobile Insurance Claims Auditing: A Comprehensive Survey on Handling Awry Datasets

2021 ◽  
pp. 135-144
Author(s):  
Ezzaim Soufiane ◽  
Salah-Eddine EL Baghdadi ◽  
Aissam Berrahou ◽  
Abderrahim Mesbah ◽  
Hassan Berbia
Keyword(s):  
Automobile Insurance ◽  
Insurance Claims ◽  
Comprehensive Survey

Managing moral hazard in motor vehicle accident insurance claims

2013 ◽  
Vol 34 (2) ◽  
pp. 320-329 ◽  
Author(s):  
Shanil Ebrahim ◽  
Jason W Busse ◽  
Gordon H Guyatt ◽  
Stephen Birch
Keyword(s):  
Moral Hazard ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Accident Insurance ◽  
Insurance Claims ◽  
Vehicle Accident

IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA

2021 ◽  
pp. 1-29
Author(s):  
Shengwang Meng ◽  
He Wang ◽  
Yanlin Shi ◽  
Guangyuan Gao
Keyword(s):  
Neural Network ◽  
Risk Score ◽  
Domain Knowledge ◽  
Automobile Insurance ◽  
Average Risk ◽  
Insurance Claims ◽  
Safe Driving ◽  
Insurance Claims Data ◽  
Claims Frequency ◽  
Car Driving

Abstract Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

Sign in / Sign up

Export Citation Format

Share Document