{"id":4029,"date":"2024-09-05T10:17:05","date_gmt":"2024-09-05T10:17:05","guid":{"rendered":"https:\/\/detralytics.com\/?p=4029"},"modified":"2026-06-22T14:29:52","modified_gmt":"2026-06-22T14:29:52","slug":"insurance-analytics-with-clustering-techniques","status":"publish","type":"post","link":"https:\/\/detralytics.com\/fr\/publications\/insurance-analytics-with-clustering-techniques\/","title":{"rendered":"Insurance Analytics with Clustering Techniques"},"content":{"rendered":"
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Insurance Analytics with Clustering Techniques<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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DetraNote 2020-3<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Introduction<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The k-means algorithm and its variants are popular techniques of clustering. Their purpose is to uncover group structures in a dataset. In actuarial applications, these methods detect clusters of policies with similar features and allows to draw a map of dominant risks. This working note starts with a review of the k-means algorithm and develops next two extensions to manage categorical features. We develop a mini-batch version that keeps computation time under control when analysing a high-dimensional dataset. We next introduce the fuzzy k-means in which policies can belong to multiple clusters. Finally, we conclude by a detailed introduction to spectral clustering.<\/p>\n

Mots-cl\u00e9s : <\/span><\/strong>Clustering analysis, unsupervised learning, k-means, spectral clustering.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Secteur : <\/b>Assurance<\/p>\n

<\/b>Expertise: <\/b>Machine learning<\/p>\n

Auteurs :<\/b>\u00a0Charlotte Jamotton,<\/p>\n

Donatien Hainaut, and Thomas Hames<\/p>\n

\u00a0<\/p>\n

\u00a0<\/b><\/p>\n

Publication:<\/b> Risks<\/p>\n

Date:<\/b> September 2024<\/p>\n

Langue :<\/b> Anglais<\/p>\n

Pages:<\/b> 28<\/p>\n

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\u00c0 propos des auteurs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Donatien\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Donatien Hainaut<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Donatien Hainaut est Conseiller Scientifique chez Detralytics et Professeur \u00e0 l\u2019UCLouvain (Belgique), o\u00f9 il dirige le Master en Data Science \u00e0 orientation statistique. Auparavant, il a occup\u00e9 plusieurs postes acad\u00e9miques, notamment en tant que Professeur Associ\u00e9 \u00e0 la Rennes School of Business et \u00e0 l\u2019ENSAE \u00e0 Paris. Il poss\u00e8de \u00e9galement une solide exp\u00e9rience en entreprise, ayant travaill\u00e9 comme Risk Officer, Quantitative Analyst et ALM Officer.<\/p>

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Actuaire qualifi\u00e9 et titulaire d\u2019un doctorat en Asset and Liability Management, ses recherches actuelles portent sur les m\u00e9canismes de contagion dans les processus stochastiques ainsi que sur les applications des r\u00e9seaux de neurones en assurance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Thomas Hames<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Depuis son arriv\u00e9e chez Detralytics en 2019, Thomas a principalement travaill\u00e9 sur des sujets de tarification et de provisionnement en assurance Non-Vie et Automobile de d\u00e9tail, en combinant des m\u00e9thodes classiques avec des approches bas\u00e9es sur le machine learning.<\/p>\n

Par ailleurs, en interne, Thomas participe au d\u00e9veloppement technique et commercial de notre package de micro-provisionnement et assure la supervision des projets de notre Innovation Lab.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Charlotte Jamotton<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Charlotte is a PhD Student in Actuarial Science at UCLouvain.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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This working note starts with a review of the k-means algorithm and develops next two extensions to manage categorical features. <\/p>","protected":false},"author":1,"featured_media":5338,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[136,133],"tags":[131],"class_list":["post-4029","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-136","category-publications","tag-detranote"],"yoast_head":"\nDetra Note : Insurance Analytics with Clustering Techniques<\/title>\n<meta name=\"description\" content=\"This working note starts with a review of the k-means algorithm and develops next two extensions to manage categorical features.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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