[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119696-en":3,"doc-seo-119696-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119696,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","A Machine Learning Approach to Predict Health Insurance Claims - Internship Report","Health insurance contract renewal in Multicare’s Tailor Made policies relies on estimating last-quarter costs three months before annuity end, using information from the first three quarters. The ARIMA time-series algorithm currently supports this process, but estimation errors create conflicting risks: overestimation can cause clients to overpay and seek alternatives, while underestimation generates company losses. This project builds a machine learning model to improve inpatient claims cost and frequency estimates, testing multiple algorithms and comparing them with ARIMA.","A MACHINE LEARNING APPROACH TO PREDICT HEALTH INSURANCE CLAIMS  \nSubtitle  \nMiguel Filipe Martins Cordeiro  \nInternship report presented as partial requirement for obtaining the Master’s degree in Advanced Analytics, with a Specialization in Business Analytics  \n2022  \nA MACHINE LEARNING APPROACH TO PREDICT HEALTH INSURANCE  \nCLAIMS  \nMiguel Filipe Martins Cordeiro  \nMAA  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nA MACHINE LEARNING APPROACH TO PREDICT HEALTH INSURANCE  \nCLAIMS  \nby  \nMiguel Filipe Martins Cordeiro  \nInternship report presented as partial requirement for obtaining the Master’s degree in Advanced Analytics, with a Specialization in Business Analytics  \nAdvisor: Nuno Miguel da Conceição António  \nNovember 2022  \nACKNOWLEDGEMENTS  \nThis thesis would not have possible without the support, knowledge, help and patience of all of those who were in some way involved with this project, therefore I want to thank and show my deepest gratitude to all of them.  \nFirst, I want to thank my advisor, professor Nuno António, for all his insights and readiness to help. His advice was very important to improve and enrich this project.  \nSecond, I must thank my supervisors at Multicare, Maria do Carmo Ornelas and Filipa Marques, forgiving me the opportunity to develop my thesis at a leading company in its field and for offering me the guidance and knowledge necessary to understand the insurance business. Also, the help given tome by my colleagues, particularly those who worked closest to me: Mariana, both Pedro’s, and Catarina; cannot be undervalued. I want to thank them for always being available to discuss their ideas with me, allowing me to grow and to improve my work.  \nI also want to acknowledge my friends, for their support throughout the development of this project.  \nFor always motivating and pushing me further, I have to thank my girlfriend, Carolina. Her words of encouragement always made me move closer to my goals.  \nFinally, I must thank my brother, for standing by my side during this process and offering to help anyway he could; and thank my parents, for all their work and sacrifice. I have them to thank for all that I have achieved so far. Their support and guidance was, and always will be, vital in the most important times.  \nABSTRACT  \nRenewing health insurance contracts is, usually, an annual process, in the Tailor Made policies branch. At Multicare, this process starts three months before the end of the clients’ annuity, by estimating the costs of the last quarter using information from the first three. This estimation process is critical to the renewal of insurance contracts, since, if the estimation is too high, the client will overpay for their insurance and might seek more competitive alternatives. In contrast, if the predictions are too low, it will result in losses for the company. This part of the renewal process is currently performed by a time series algorithm, specifically an ARIMA model.  \nThis project aims to build a machine learning-based model that will provide more accurate estimations of the claims’ cost and frequency, in the Inpatient coverage, to Multicare. Several algorithms were tested: Linear and Logistic Regressions, Decision Trees, Random Forests, Gradient Boosting and XGBoost; and their results were then compared to the ones of the current ARIMA model. This study showed that a machine learning technique, the XGBoost, is more powerful than the ARIMA, as it projects 9% above the real costs, against the ARIMA’s global error of-25% . These conclusions can lead to changes in Multicare’s approach to predicting claim costs and, consequentially, its way of doing business.  \nKEYWORDS  \nClaim Forecasting; Ensemble; Health Insurance; Machine Learning; Tailor Made Policies; XGBoost  \nINDEX  \n1. Introduction .................................................................................................................. ","cbCaihkftwd7RMrB","https://ap.wps.com/l/cbCaihkftwd7RMrB","pdf",2213930,1,52,"English","en",105,"# Introduction\n## Problem Statement\n## Goal Definition\n# Literature Review\n# Methodology\n## Frequency Dataset\n### Exploratory Data Analysis\n## Cost Dataset\n### Exploratory Data Analysis\n## Pre-Processing\n### Missing Values\n### District\n### Unlimited Spending Limits\n### Inflation\n### Correlation\n### Outliers\n## Modelling\n### Train Te","[{\"question\":\"Why is accurate claims cost estimation important for Multicare’s renewal process?\",\"answer\":\"Renewal starts three months before annuity end, and last-quarter cost predictions drive pricing. Overestimation makes clients overpay and potentially switch, while underestimation leads to losses for the company.\"},{\"question\":\"Which baseline model is used in the current system for predicting claims cost?\",\"answer\":\"The renewal process is currently supported by a time-series algorithm specifically using an ARIMA model.\"},{\"question\":\"What machine learning approach performed best compared with ARIMA?\",\"answer\":\"The study found XGBoost to be more powerful than ARIMA, projecting 9% above real costs, whereas ARIMA showed a global error of -25%.\"}]","A Machine Learning Approach to Predict Health Insurance Claims - Internship Report | PDF",1785725827,131,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-machine-learning-approach-to-predict-health-insurance-claims-internship-report","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-to-predict-health-insurance-claims-internship-report/119696/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is accurate claims cost estimation important for Multicare’s renewal process?","Question",{"text":76,"@type":77},"Renewal starts three months before annuity end, and last-quarter cost predictions drive pricing. Overestimation makes clients overpay and potentially switch, while underestimation leads to losses for the company.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which baseline model is used in the current system for predicting claims cost?",{"text":81,"@type":77},"The renewal process is currently supported by a time-series algorithm specifically using an ARIMA model.",{"name":83,"@type":74,"acceptedAnswer":84},"What machine learning approach performed best compared with ARIMA?",{"text":85,"@type":77},"The study found XGBoost to be more powerful than ARIMA, projecting 9% above real costs, whereas ARIMA showed a global error of -25%.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]