Dynamic Pricing Models for Automobile Insurance
The accurate pricing of automobile insurance remains a critical challenge, particularly in markets where traditional models fail to capture risk heterogeneity. This study addresses the limitations of conventional Poisson models, which assume uniform accident probabilities among insured individuals,...
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| Format: | Article |
| Language: | English |
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Academic Research and Publishing UG
2025-03-01
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| Series: | Financial Markets, Institutions and Risks |
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| Online Access: | https://armgpublishing.com/wp-content/uploads/2025/04/FMIR_1_2025_11.pdf |
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| _version_ | 1849698062263386112 |
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| author | Lynda Ait Bachir Oumelkheir Elbaroud Fatma Bouderra |
| author_facet | Lynda Ait Bachir Oumelkheir Elbaroud Fatma Bouderra |
| author_sort | Lynda Ait Bachir |
| collection | DOAJ |
| description | The accurate pricing of automobile insurance remains a critical challenge, particularly in markets where traditional models fail to capture risk heterogeneity. This study addresses the limitations of conventional Poisson models, which assume uniform accident probabilities among insured individuals, by incorporating advanced probabilistic models that account for overdispersion and individual risk variability. The primary objective is to develop a more precise and equitable pricing model for automobile insurance premiums, integrating statistical and Bayesian inference techniques. The research focuses on Algeria, a market characterized by distinct driving conditions, regulatory frameworks, and demographic patterns that influence accident frequency. Using a dataset of 680 insured individuals from the Société Algérienne d'Assurances, collected over the period 2021–2024, this study applies the Poisson-Gamma and Poisson-Inverse Gaussian models to refine accident risk assessment. The findings reveal that accident frequency is significantly influenced by factors such as gender, age, driving experience, vehicle power, and usage. The Poisson-Gamma model outperforms the standard Poisson model by better capturing individual differences, while the Poisson-Inverse Gaussian model further refines risk estimation by addressing heavy-tailed distributions. Additionally, a Bayesian posterior pricing framework within a Bonus-Malus system is introduced, enabling gains to dynamically adjust premiums based on an individual’s accident history. The study confirms the superiority of these models through statistical validation and goodness-of-fit tests, demonstrating their potential for improving risk assessment and pricing accuracy. The results offer practical applications for insurance companies, policymakers, and regulators seeking to enhance premium fairness and financial sustainability. |
| format | Article |
| id | doaj-art-9a2b6ebf292b4827baff705fdb80bacc |
| institution | DOAJ |
| issn | 2521-1250 2521-1242 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Academic Research and Publishing UG |
| record_format | Article |
| series | Financial Markets, Institutions and Risks |
| spelling | doaj-art-9a2b6ebf292b4827baff705fdb80bacc2025-08-20T03:19:02ZengAcademic Research and Publishing UGFinancial Markets, Institutions and Risks2521-12502521-12422025-03-019116219410.61093/fmir.9(1).162-194.2025Dynamic Pricing Models for Automobile InsuranceLynda Ait Bachir0https://orcid.org/0000-0002-8141-1551Oumelkheir Elbaroud1https://orcid.org/0009-0002-9195-4514Fatma Bouderra2https://orcid.org/0009-0005-1733-4183PhD, Professor Lecturer (A), Department of Economics, Finance and Accounting, Institute of Economics, University Center Aflou, AlgeriaPhD, Professor Lecturer (A), Department of Economics, Finance and Accounting, Institute of Economics, University Center Aflou, AlgeriaPhD, Professor Lecturer (A), Department of Economics, Finance and Accounting, Institute of Economics, University Center Aflou, AlgeriaThe accurate pricing of automobile insurance remains a critical challenge, particularly in markets where traditional models fail to capture risk heterogeneity. This study addresses the limitations of conventional Poisson models, which assume uniform accident probabilities among insured individuals, by incorporating advanced probabilistic models that account for overdispersion and individual risk variability. The primary objective is to develop a more precise and equitable pricing model for automobile insurance premiums, integrating statistical and Bayesian inference techniques. The research focuses on Algeria, a market characterized by distinct driving conditions, regulatory frameworks, and demographic patterns that influence accident frequency. Using a dataset of 680 insured individuals from the Société Algérienne d'Assurances, collected over the period 2021–2024, this study applies the Poisson-Gamma and Poisson-Inverse Gaussian models to refine accident risk assessment. The findings reveal that accident frequency is significantly influenced by factors such as gender, age, driving experience, vehicle power, and usage. The Poisson-Gamma model outperforms the standard Poisson model by better capturing individual differences, while the Poisson-Inverse Gaussian model further refines risk estimation by addressing heavy-tailed distributions. Additionally, a Bayesian posterior pricing framework within a Bonus-Malus system is introduced, enabling gains to dynamically adjust premiums based on an individual’s accident history. The study confirms the superiority of these models through statistical validation and goodness-of-fit tests, demonstrating their potential for improving risk assessment and pricing accuracy. The results offer practical applications for insurance companies, policymakers, and regulators seeking to enhance premium fairness and financial sustainability.https://armgpublishing.com/wp-content/uploads/2025/04/FMIR_1_2025_11.pdfaccident frequencyactuarial modelingautomobile insurancebayesian inferencebonus-malus systeminsurance pricingpoisson-gamma modelpoisson-inverse gaussian modelrisk assessmentstatistical modeling |
| spellingShingle | Lynda Ait Bachir Oumelkheir Elbaroud Fatma Bouderra Dynamic Pricing Models for Automobile Insurance Financial Markets, Institutions and Risks accident frequency actuarial modeling automobile insurance bayesian inference bonus-malus system insurance pricing poisson-gamma model poisson-inverse gaussian model risk assessment statistical modeling |
| title | Dynamic Pricing Models for Automobile Insurance |
| title_full | Dynamic Pricing Models for Automobile Insurance |
| title_fullStr | Dynamic Pricing Models for Automobile Insurance |
| title_full_unstemmed | Dynamic Pricing Models for Automobile Insurance |
| title_short | Dynamic Pricing Models for Automobile Insurance |
| title_sort | dynamic pricing models for automobile insurance |
| topic | accident frequency actuarial modeling automobile insurance bayesian inference bonus-malus system insurance pricing poisson-gamma model poisson-inverse gaussian model risk assessment statistical modeling |
| url | https://armgpublishing.com/wp-content/uploads/2025/04/FMIR_1_2025_11.pdf |
| work_keys_str_mv | AT lyndaaitbachir dynamicpricingmodelsforautomobileinsurance AT oumelkheirelbaroud dynamicpricingmodelsforautomobileinsurance AT fatmabouderra dynamicpricingmodelsforautomobileinsurance |