Les cahiers du CREAD
Volume 39, Numéro 3, Pages 217-234
2024-01-08
Authors : Oucherif Walid . Touche Nassim .
The Algerian automobile insurance market faces significant challenges in pricing insurance policies due to the lack of reliable predictions for insurance losses. In this paper, we introduce a new ratemaking system that leverages advanced data analysis techniques, including Generalized Linear Models and machine learning algorithms like Neural Networks, boosting, and stacking algorithms, to model claims frequency. By analyzing data and statistics of drivers in the Algerian market, this system offers a data-driven solution that helps insurers to better understand their risk exposure and make informed pricing decisions. The proposed system has implications for both insurers and policyholders in terms of fairer and more accurate pricing, which will ultimately benefit the Algerian economy.
Auto insurance ; statistical learning ; Neural Nets ; GBM ; XGBoost
بوسالم أحلام
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عابد يوسف
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ص 117-132.
Yahia Zeghoudi
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pages 74-88.
Aitouche Moh-amokrane
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Djeddi Mounir
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Djeddi Mabrouk
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Mihoubi Abdelhafid
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pages 119-138.
Said Houari Amel
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pages 257-268.
Debbouzine Mohamed
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Khoukhi Abderrahmane
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Sellah Rabiâa
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pages 299-314.