{"id":100000805,"date":"2017-09-22T15:25:46","date_gmt":"2017-09-22T13:25:46","guid":{"rendered":"http:\/\/know-how-to-risk.com\/?p=100000805"},"modified":"2019-01-12T22:43:59","modified_gmt":"2019-01-12T21:43:59","slug":"white-paper-machine-learning-applications-to-non-life-pricing-machine-learning-applications-to-non-life-pricing","status":"publish","type":"post","link":"https:\/\/www.reacfin.com\/index.php\/2017\/09\/22\/white-paper-machine-learning-applications-to-non-life-pricing-machine-learning-applications-to-non-life-pricing\/","title":{"rendered":"Machine Learning applications"},"content":{"rendered":"

{“main-title”:{“component”:”hc_title”,”id”:”main-title”,”subtitle”:”Frequency modelling: An educational case study”,”title_content”:{“component”:”hc_title_image”,”id”:”title-image”,”image”:””,”full_screen”:false,”full_screen_height”:””,”parallax”:false,”bleed”:””,”ken_burn”:””,”overlay”:””,”breadcrumbs”:false,”white”:true},”title”:”Machine Learning applications”},”section_5ZtkF”:{“component”:”hc_section”,”id”:”section_5ZtkF”,”section_width”:””,”animation”:””,”animation_time”:””,”timeline_animation”:””,”timeline_delay”:””,”timeline_order”:””,”vertical_row”:””,”box_middle”:””,”css_classes”:””,”custom_css_classes”:””,”custom_css_styles”:””,”section_content”:[{“component”:”hc_column”,”id”:”column_vtfQF”,”column_width”:”col-md-12″,”animation”:””,”animation_time”:””,”timeline_animation”:””,”timeline_delay”:””,”timeline_order”:””,”css_classes”:””,”custom_css_classes”:””,”custom_css_styles”:” color: #9C9C9C;”,”main_content”:[{“component”:”hc_icon_list”,”id”:”jQ3jk”,”css_classes”:””,”custom_css_classes”:””,”custom_css_styles”:””,”rows”:[{“component”:”repeater_item”,”id”:”5uxoL”,”icon”:”fa fa-info-circle”,”icon_style”:””,”icon_image”:””,”text”:”Title : <\/b>Machine Learning applications to non-life pricing”},{“component”:”repeater_item”,”id”:”UUJ09″,”icon”:”fa fa-pencil”,”icon_style”:””,”icon_image”:””,”text”:”Authors : <\/b>Julien Antunes Mendes, S\u00e9bastien de Valeriola, Samuel Mahy and Xavier Mar\u00e9chal”},{“component”:”repeater_item”,”id”:”E3jlE”,”icon”:”fa fa-calendar”,”icon_style”:””,”icon_image”:””,”text”:”Date : <\/b>September 2017″},{“component”:”repeater_item”,”id”:”sGhZ0″,”icon”:”fa fa-bookmark”,”icon_style”:””,”icon_image”:””,”text”:” Reference : <\/b>Reacfin White Paper 2017″},{“component”:”repeater_item”,”id”:”SMfVu”,”icon”:”fa fa-tags”,”icon_style”:””,”icon_image”:””,”text”:”Key words : <\/b>Modeling – Machine learning – Non-life – Pricing”}],”direction”:”vertical-icon-list”,”alignment”:”text-left”,”icon_position”:””,”text_size”:”text-xs”,”icon_size”:””},{“component”:”hc_space”,”id”:”fGuyu”,”css_classes”:””,”custom_css_classes”:””,”custom_css_styles”:””,”size”:”m”,”height”:””},{“component”:”hc_quote”,”id”:”ki7eH”,”css_classes”:”text-justify text-s “,”custom_css_classes”:””,”custom_css_styles”:””,”text”:”Machine Learning applications to non-life pricing\\n\\nFrequency modeling: An educational case study\\n\\nMachine Learning techniques operate data to perform classification and prediction. They are becoming more and more popular, due (among other things) to the still increasing computational power of machines. These algorithms use a very limited set of assumptions, compared to \u201cstandard\u201d statistical methods. However, on the contrary of such methods, inference is not the main goal of Machine Learning techniques: they focus on classification and prediction. This is the reason why these \u201cinnovative\u201d techniques are considered as more \u201cblack box\u201d solutions.\\n\\nThis paper presents a comparison on a simulated database between traditional statistical predictive modeling techniques (GLM and GAM), machine learning techniques (regression trees, bagging, random forests, boosting and neural networks) and penalized regression techniques (Lasso, Ridge and Elastic Net).\\n\\n”,”style”:”double_quote”,”author”:”Julien Antunes Mendes, S\u00e9bastien de Valeriola, Samuel Mahy and Xavier Mar\u00e9chal”},{“component”:”hc_space”,”id”:”c9fa5″,”css_classes”:””,”custom_css_classes”:””,”custom_css_styles”:””,”size”:”m”,”height”:””},{“component”:”hc_button”,”id”:”5ZtkF”,”css_classes”:””,”custom_css_classes”:””,”custom_css_styles”:””,”icon”:”fa fa-file-pdf-o”,”style”:”square”,”size”:”btn-sm”,”position”:”left”,”animation”:false,”text”:”Download”,”link_type”:”classic”,”lightbox_animation”:””,”caption”:””,”inner_caption”:false,”new_window”:true,”link”:”https:\/\/www.reacfin.com\/wp-content\/uploads\/2016\/12\/20170914-Machine-Learning-applications-for-non-life-pricing.pdf”,”link_content”:[],”lightbox_size”:””,”scrollbox”:false}]}],”section_settings”:””},”scripts”:{},”css”:{},”css_page”:””,”template_setting”:{“settings”:{“id”:”settings”}},”template_setting_top”:{},”page_setting”:{“settings”:[“lock-mode-off”]},”post_type_setting”:{“settings”:{“image”:”http:\/\/know-how-to-risk.com\/wp-content\/uploads\/2018\/07\/machine-learning-applications-to-non-life-pricing.png|354|777|100000965″,”excerpt”:”Frequency modelling: An educational case study, Machine Learning applications to non-life pricing.\\n
\\n <\/i>Download<\/a>“,”extra_1″:””,”extra_2″:””,”icon”:{“icon”:””,”icon_style”:””,”icon_image”:””}}}}<\/p>\n","protected":false},"excerpt":{"rendered":"

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