European Journal of Business Science and Technology 2022, 8(1):5-18 | DOI: 10.11118/ejobsat.2022.002

Multivariate Modelling of Motor Third Party Liability Insurance Claims

Aivars Spilbergs¹, Andris Fomins¹, Māris Krastiņš¹

¹ BA School of Business and Finance, Riga, Latvia

The aim of the study is to identity the main factors that affect claims amount paid by insurers in case of road accidents and to predict losses from valid third-party liability insurance (MTPLI) policies until their expiration. Such an assessment is essential to adequately cover MTPLI policies and ensure the sustainable development of insurance companies. The geography of the study covers the MTPLI market of Europe in the main areas, but a deeper analysis of the impact of various factors, interactions, and interrelationships in MTPLI product is focused on Latvian market data due to availability of high-quality primary data. The research is based on the analysis of primary Latvian MTPLI policies data of more than 128,000 road traffic accidents that have occurred during the time period from 2014 till 2020. Risk driver selection was performed based on the existing scientific studies and correlation analysis of the sample set. Both linear and nonlinear forms of relationships were used for modelling. A multivariate modeling was used to identify significant risk factors and to quantify their impact on loss of incidents. Statistical stability of the models was tested using chi-squared, t-tests and p-values. Validation of models calibrated where done using prediction errors measurements: mean square error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) assessment both within sample and out of sample technics. The results indicated that the driver’s behavior (penalties and Bonus-Malus) as well as vehicle parameters (weight and age), had significant impacts on crash losses.

Keywords: road traffic accidents, risk drivers, non-life insurance, MTPL insurance, private insurance, passenger cars, Bonus-Malus system, MTPL insurance claims paid, multivariate modelling

JEL classification: C38, G22

Received: October 7, 2021; Revised: March 31, 2022; Accepted: April 11, 2022; Published: July 31, 2022  Show citation