[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121720-en":3,"doc-seo-121720-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121720,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","IBNR TECHNIQUES IN HEALTH INSURANCE - A MACHINE LEARNING APPROACH","Loss reserves form a major liability for insurers, influencing both profitability and solvency. The project uses the Chain Ladder actuarial reserving technique to estimate Incurred But Not Reported claims, then benchmarks it against machine learning methods. The approach compares Chain Ladder with Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Neural Networks. The objective is to produce the most accurate reserve claim amount estimates and predictions for corporate health insurance contexts.","Mestrado em Estatística e Gestão da Informação  \nMaster Program in Statistics and Information Management  \nIBNR TECHNIQUES IN HEALTH INSURANCE: A MACHINE LEARNING APPROACH  \nCatarina Ferreira de Jesus de Sousa  \nProject work presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management  \nNOVA Information Management School  \nInstituto Superior de Estatística e Gestão da Informação  \nUniversidade Nova de Lisboa  \nMEGI  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade NOVA de Lisboa  \nIBNR TECHNIQUES IN HEALTH INSURANCE: A MACHINE LEARNING APPROACH  \nby  \nCatarina Ferreira de Jesus de Sousa  \nProject work presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management  \nAdviser: Professor Doutor Roberto André Pereira Henriques  \nCo-adviser: Professora Doutora Maria de Lourdes Belchior Afonso  \nFebruary, 2023  \nIBNR techniques in Health Insurance: A Machine Learning Approach  \nCopyright © Catarina Ferreira de Jesus de Sousa, NOVA Information Management School, NOVA University Lisbon.  \nThe NOVA Information Management School and the NOVA University Lisbon have the right, perpetual and without geographical boundaries, to file and publish this dissertation through printed copies reproduced on paper or on digital form, or by anyother means known or that may be invented, and to disseminate through scientific repositories and admit its copying and distribution for non-commercial, educational or research purposes, as long as credit is given to the author and editor.  \nThis document was created with the (pdf/Xe/Lua)LATEX processor and the NOVAthesis template (v6.9.2) Lourenço, 2021.  \nAcknowledgements  \nI was entrusted with the opportunity to be an intern for a year to develop this project. It would not have been possible without the vote of confidence that Multicare gave me in the person of Maria do Carmo Ornelas. I would especially like to thank the SAC team for receiving me so well during this time. I take with me a lot of learning from this multidisciplinary team, focused on applying these recent data science methodologies to the business. To Filipa Marques, who was the heart of this project, thank you foryour patience, kindness, and vision throughout this development. I could not fail to thank Mariana Vieira, Pedro Lopes, Miguel Cordeiro, and Pedro Gonçalves for all their support. A warm thank you to this incredible team.  \nAdditionally, I would like to sincerely thank Professors Roberto Henriques and Maria de Lourdes Afonso for all your guidance. The areas of specialization of each one were essential to being able to combine machine learning with actuarial expertise in this project. You were tireless in answering all of my concerns.  \nFinally, I would like to express my gratitude to my family and friends for their support throughout this journey.  \nAbstract  \nLoss reserves are typically one of the largest liabilities on an insurer’s balance sheet since they can have a significant impact on profits as well as the insurer’s solvency. The Chain Ladder model is an outstanding actuarial reserving technique that has been applied over the years to estimate Incurred But Not Reported claims.  \nThis project aims to provide the most accurate estimates possible for the calculation and prediction of reserve claim amounts in the context of corporate health insurance. For this, the Chain Ladder approach is compared with machine learning algorithms such as the Support Vector Machine (SVM), the Random Forest (RF), the Extreme Gradient Boosting (XGBoost) and Neural Networks (NN) .  \nKeywords: IBNR, Health Insurance, Chain Ladder, Machine Learning, Predicting Claims","cbCaibVXKO6rN1ps","https://ap.wps.com/l/cbCaibVXKO6rN1ps","pdf",1757457,1,83,"English","en",105,"# Abstract\n# Acknowledgements\n# Introduction and Objectives\n# Methodology\n## Chain Ladder model\n## Machine learning algorithms (SVM, RF, XGBoost, Neural Networks)\n# Keywords","[{\"question\":\"Why are loss reserves important in health insurance?\",\"answer\":\"Loss reserves are typically a major liability for insurers and can materially affect profits as well as solvency.\"},{\"question\":\"What does the project use to estimate Incurred But Not Reported claims?\",\"answer\":\"It relies on the Chain Ladder actuarial reserving technique to estimate IBNR claims.\"},{\"question\":\"Which machine learning models are compared with Chain Ladder?\",\"answer\":\"The project compares Chain Ladder with SVM, Random Forest, XGBoost, and Neural Networks to predict reserve claim amounts.\"}]","IBNR TECHNIQUES IN HEALTH INSURANCE - 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