Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

Torra, J.; Guillén, Montserrat; Pérez-Marín, Ana M.; Gámez, Lorena Rey; Aguer, Giselle

doi:10.3390/risks11030057

Cited by 5 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As can be seen by the aforementioned literature review, our research is closer to the most recent articles of Reig Torra et al (2023) and Masello et al (2023), whose work has most likely been done in parallel with ours, as these papers were published in 2023. Still, our work maintains its novelty since (i) we use ML approaches compared to the work of Reig Torra et al (2023), who employ the Poisson model (even though they also include weather data in their model); and (ii) we use the weather conditions/data in order to forecast the (mean) motor claims, compared to the analysis of Masello et al (2023) who asses their impact on driving risks/safety.…”

Section: Díazsupporting

confidence: 57%

“…More specifically, Duval et al (2022) used ML models to come up with a method that indicates the amount of information-collected via telematics with regards to the policyholders' driving behavior-that needs to be (optimally) retained by insurers to (successfully) perform motor insurance claim classification. Reig Torra et al (2023) also capitalized on the data provided by telematics and used the Poisson model, along with some weather data, to forecast the expected motor insurance claim frequency over time. They found that weather conditions do affect the risk of an accident.…”

Section: Claims/risksmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Forecasting Motor Insurance Claims

Poufinas,

Gogas,

Papadimitriou

et al. 2023

Risks

View full text Add to dashboard Cite

Accurate forecasting of insurance claims is of the utmost importance for insurance activity as the evolution of claims determines cash outflows and the pricing, and thus the profitability, of the underlying insurance coverage. These are used as inputs when the insurance company drafts its business plan and determines its risk appetite, and the respective solvency capital required (by the regulators) to absorb the assumed risks. The conventional claim forecasting methods attempt to fit (each of) the claims frequency and severity with a known probability distribution function and use it to project future claims. This study offers a fresh approach in insurance claims forecasting. First, we introduce two novel sets of variables, i.e., weather conditions and car sales, and second, we employ a battery of Machine Learning (ML) algorithms (Support Vector Machines—SVM, Decision Trees, Random Forests, and Boosting) to forecast the average (mean) insurance claim per insured car per quarter. Finally, we identify the variables that are the most influential in forecasting insurance claims. Our dataset comes from the motor portfolio of an insurance company operating in Athens, Greece and spans a period from 2008 to 2020. We found evidence that the three most informative variables pertain to the new car sales with a 3-quarter and 1-quarter lag and the minimum temperature of Elefsina (one of the weather stations in Athens) with a 3-quarter lag. Among the models tested, Random Forest with limited depth and XGboost run on the 15 most informative variables, and these exhibited the best performance. These findings can be useful in the hands of insurers as they can consider the weather conditions and the new car sales among the parameters that are considered to perform claims forecasting.

show abstract