On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study

Qazvini, Marjan

doi:10.3390/risks7030071

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…Since then, this model has been applied in different settings including insurance pricing. The variation had been applied to insurance data by [7] who studied the classical Poisson and logistic regression and compare the findings with a Zero Inflated Poisson (ZIP) model using insurance data from the French motor third party liability. The result shows that the ZIP outperforms the classical Poisson regression.…”

Section: Original Research Articlementioning

confidence: 99%

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Modelling Claim Frequency in Insurance Using Count Models

Adetunji

Sabri

2021

AJPAS

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Background: In modelling claim frequency in actuary science, a major challenge is the number of zero claims associated with datasets. Aim: This study compares six count regression models on motorcycle insurance data. Methodology: The Akaike Information Criteria (AIC) and the Bayesian Information Criterion (BIC) were used for selecting best models. Results: Result of analysis showed that the Zero-Inflated Poisson (ZIP) with no regressors for the zero component gives the best predictive ability for the data with the least BIC while the classical Negative Binomial model gives the best result for explanatory purpose with the least AIC.

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Section: Original Research Articlementioning

confidence: 99%

“…A very pertinent concept in general insurance pricing involves classification of risks and identification of risk characteristics (like age, duration of policy, gender, type of policy, etc.) of the insured to estimate premium [7]. Since most insurance data have confidentiality issue, it is always difficult to obtain more recent data.…”

Section: Datamentioning

confidence: 99%

Modelling Claim Frequency in Insurance Using Count Models

Adetunji

Sabri

2021

AJPAS

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“…Wang et al (2017) implemented driver identification based on the telematics data and Random forest classification method. Qazvini (2019) compared the performance of a Poisson regression model and a zero-inflated Poisson (ZIP) model for the prediction of the claim frequency based on the telematics data that were collected from insured drivers.…”

Section: Introduction and Relevant Workmentioning

confidence: 99%

Address Identification Using Telematics: An Algorithm to Identify Dwell Locations

Grumiau¹,

Mostoufi²,

Pavlioglou³

et al. 2020

Risks

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In this work, a method is proposed for exploiting the predictive power of a geo-tagged dataset as a means of identification of user-relevant points of interest (POI). The proposed methodology is subsequently applied in an insurance context for the automatic identification of a driver’s residence address, solely based on his pattern of movements on the map. The analysis is performed on a real-life telematics dataset. We have anonymized the considered dataset for the purpose of this study to respect privacy regulations. The model performance is evaluated based on an independent batch of the dataset for which the address is known to be correct. The model is capable of predicting the residence postal code of the user with a high level of accuracy, with an f1 score of 0.83. A reliable result of the proposed method could generate benefits beyond the area of fraud, such as general data quality inspections, one-click quotations, and better-targeted marketing.

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“…ML has been used in the insurance industry to analyze insurance claim data [11,12]. Vehicle insurance coverage affects driving behavior and hence insurance claims [13]. These previous works employed ML to analyze data at the end of the insurance pathway, after the event.…”

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confidence: 99%

Risk Assessment for Personalized Health Insurance Products

Pnevmatikakis¹,

Kanavos²,

Perikleous³

et al. 2022

Big Data and Artificial Intelligence in Digital Finance

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The way people lead their lives is considered an important factor in health. In this chapter, we describe a system to provide risk assessment based on behavior for the health insurance sector. The system processes real-world data (RWD) of individuals from their daily life that enumerate different aspects of behavior collection. The data have been captured using the Healthentia platform and a simulator that augments the actual dataset with synthetic data. Classifiers are built to predict variations of peoples’ well-being short-term outlook. Risk assessment services are provided to health insurance professionals by processing the classifier predictions in the long term while explaining the classifiers themselves provide insights on the coaching of the users of the service.

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On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study

Cited by 7 publications

References 25 publications

Modelling Claim Frequency in Insurance Using Count Models

Modelling Claim Frequency in Insurance Using Count Models

Address Identification Using Telematics: An Algorithm to Identify Dwell Locations

Risk Assessment for Personalized Health Insurance Products

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