A Claimsman's Rejoinder to the Scheduled Compensation Proposals for Automobile Insurance Claims

1970 ◽  
Vol 37 (3) ◽  
pp. 474
Author(s):  
William S. Jeffries
Keyword(s):  
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

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.

scholarly journals A Predictive Modeling for Detecting Fraudulent Automobile Insurance Claims

2019 ◽  
Vol 09 (06) ◽  
pp. 1886-1900
Author(s):  
Hojin Moon ◽  
Yuan Pu ◽  
Cesarina Ceglia
Keyword(s):  
Predictive Modeling ◽  
Automobile Insurance ◽  
Insurance Claims

TERRITORIAL RISK CLASSIFICATION USING SPATIALLY DEPENDENT FREQUENCY-SEVERITY MODELS

2017 ◽  
Vol 47 (2) ◽  
pp. 437-465 ◽  
Author(s):  
Peng Shi ◽  
Kun Shi
Keyword(s):  
Risk Classification ◽  
Risk Scores ◽  
Occurrence Rate ◽  
Automobile Insurance ◽  
Insurance Claims ◽  
Modeling Framework ◽  
Cross Sectional ◽  
Dependent Frequency ◽  
Average Size ◽  
Conditional Autoregressive Models

AbstractIn non-life insurance, territory-based risk classification is useful for various insurance operations including marketing, underwriting, ratemaking, etc. This paper proposes a spatially dependent frequency-severity modeling framework to produce territorial risk scores. The framework applies to the aggregated insurance claims where the frequency and severity components examine the occurrence rate and average size of insurance claims in each geographic unit, respectively. We employ the bivariate conditional autoregressive models to accommodate the spatial dependency in the frequency and severity components, as well as the cross-sectional association between the two components. Using a town-level claims data of automobile insurance in Massachusetts, we demonstrate applications of the model output–territorial risk scores–in ratemaking and market segmentation.

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