Use of open data to improve automobile insurance premium rating

Blesa, Angel; Íñiguez, D.; Moreno, Rubén Martínez; Ruiz, Gonzalo

doi:10.1177/1470785319862734

Cited by 4 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other sources of data could be structured but of large size. See Blier-Wong et al 2020and Blesa et al (2020) for use of open data and Pechon et al (2019) to select potential features.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Blier-Wong

Cossette

Lamontagne

et al. 2020

Risks

View full text Add to dashboard Cite

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry.

show abstract

“…Other sources of data could be structured but of large size. See Blier-Wong et al 2020and Blesa et al (2020) for use of open data and Pechon et al (2019) to select potential features.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Blier-Wong

Cossette

Lamontagne

et al. 2020

Risks

View full text Add to dashboard Cite

show abstract

“…The studies on this aspect by Subudhi and Panigrahi [12,13] provide different ways of detecting frauds in the Indian context. There is a body of literature that focusses on improving insurance rates via different approaches [14,15]. Devlin [16] examines different automobile insurance regimes in the Canadian context.…”

Section: Literature Reviewmentioning

confidence: 99%

A Study on Customer’s Preference for Personal Vehicle Insurance Provided by Different Insurance Companies in Bengaluru

Ghose

Akanchha²

2020

IJMTS

View full text Add to dashboard Cite

The primary purpose of vehicle insurance is to cover the vehicle against damage, personal injury, and third-party liability. In addition to this, some insurance companies also provide value-added services such as roadside assistance and other services in return of the amount called as premium which attracts a large number of customers. However, our study shows that vehicle owners give maximum importance to the cost of insurance in terms of the annual premium. Primary data has been collected through questionnaire and analysed to ascertain about the factors responsible for taking out vehicle insurance, choice between private and public sector insurance companies, preferred insurance companies among the major players in the field, factors that play a role in the customers’ choice of a particular insurance company, customers’ opinion about the affordability of the premium to be paid, customers’ satisfaction with their chosen company, whether customers consider fast and efficient service as a deciding factor, and whether the brand value of the company plays a role in the customers’ choice.

show abstract

Machine learning based methods for ratemaking health care insurance

Ben Hamida,

Kacem,

de Peretti

et al. 2024

International Journal of Market Research

View full text Add to dashboard Cite

In insurance, proposing an accurate premium that is adjusted to the insured risk profile allows companies to better manage their portfolios and to be more competitive. Machine learning methods have recently been adopted for various improvements in insurance ratemaking, especially in the automobile industry. These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using explanatory variables. In this paper, we aim to provide a pricing method for ratemaking individual healthcare insurance contracts using machine learning algorithms that are applied to a Tunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced methods that are Random Forest, Extreme gradient boosting, Support Vector Regression, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear model. Our results showed the applicability of machine learning in the healthcare insurance market and that the XGBoost algorithm outperforms the predictive capacity of the classical generalized linear model.

show abstract

Use of open data to improve automobile insurance premium rating

Cited by 4 publications

References 25 publications

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

A Study on Customer’s Preference for Personal Vehicle Insurance Provided by Different Insurance Companies in Bengaluru

Machine learning based methods for ratemaking health care insurance

Contact Info

Product

Resources

About