[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124680-en":3,"doc-seo-124680-105":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},124680,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Predicting Lapse Rate in Life Insurance - An Exploration of Machine Learning Techniques","The study investigates the implementation of machine learning techniques to predict the lapse rate in life insurance, defined as policy cancellations or expirations. It emphasizes the lapse rate’s impact on insurer viability through pricing decisions, risk management, and strategic planning. Data comes from a risk survey of policyholders, including personal characteristics, policy details, and historical lapse patterns, followed by multiple algorithm evaluations. Results show strong forecasting performance, led by Extreme Gradient Boosting, C 5:0, and random forest.","Mestrado em Estatística e Gestão da Informação  \nMaster Program in Statistics and Information Management  \nPredicting Lapse Rate in Life Insurance: An Exploration of Machine Learning Techniques  \nDiogo da Cunha Alcaide  \nDissertation submitted in fulfillment of the requirements for the degree of  \nMaster of Science 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  \nInstituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa  \nPREDICTING LAPSE RATE IN LIFE INSURANCE:  \nAN EXPLORATION OF MACHINE LEARNING TECHNIQUES  \nby  \nDiogo da Cunha Alcaide  \nDissertation presented as a requirement for obtaining the Master's degree in Information Management, with a specialization in Risk Analysis and Management  \nAdvisor: Rui Alexandre Henriques Gonçalves  \nFebruary 2023  \n Abstract  \nThe implementation of machine learning techniques for the prediction of the lapse rate in life insurance is investigated in this study. The lapse rate, which refers to the rate of policy cancellations or expirations, plays a crucial role in the viability of life insurance companies as they determine pricing strategies, manage risk, and plan for the future.  \nData was collected through a risk survey administered to policyholders, covering their characteristics, policy details, and historical lapse patterns. A variety of machine learning algorithms were then applied to the collected data to evaluate their performance in predicting the lapse rate.  \nThe results of the study demonstrate the e􀀋ectiveness of machine learning methods in forecasting the lapse rate in life insurance. The Extreme Gradient Boosting, C 5:0, and random forest algorithms produced the best results when applied to the dataset. Additionally, several key policy and customer characteristics were identified as having significant predictive power in regards to the lapse rate.  \nHowever, the limitations of the study must be taken into consideration. Further research is necessary to validate the results on larger and more diverse datasets and to examine the practical applications of the models in the life insurance industry.  \nIn conclusion, this study makes a contribution to the existing body of knowledge on the use of machine learning in the insurance industry and holds the potential to inform the development of more e􀀎cient risk management practices in the life insurance sector.  \nKeywords: Life Insurance, Lapse Risk, Machine Learning, Classification Problem, Risk Management, Risk Assessment  \n Resumo  \nOs seguros de ramo vida são uma importante rede de segurança financeira paramuitos indivíduos e famílias. Um fator-chave na viabilidade de uma seguradora é orisco de lapso, ou seja, a taxa de cancelamento ou expiração de apólices por partedos segurados. A previsão precisa desta taxa de lapso é essencial para as seguradoraspoderem preçar corretamente as apólices, gerir os riscos e planear o futuro estrategicamente.  \nNeste estudo, foi explorado o uso de métodos preditivos de Data Mining para prever a taxa de lapso em seguros de vida. Teve como base a análise e tratamento de dados, tendo em conta um questionário de risco com as características dos segurados, detalhes das suas apólices e padrões históricos de lapso. Com esta informação foi aplicada umagama de métodos preditivos e feita uma avaliação de performance relativa à previsãoda taxa de lapso.  \nOs nossos resultados mostraram que os métodos preditivos podem ser eficazes ecoerentes na previsão da taxa de lapso em seguros de vida. Em particular, foi encontrada uma boa performance de resultados nos algoritmos Extreme Gradient Boosting, C 5:0 e Random Forest. Além disso, com este estudo foi possivel identificar várias características importantes para conseguir prever as apólices e clientes em risco de lapso.  \nEmbora os nossos resultados apontem para uma promessa no uso d","cbCailyuDZrWPwXt","https://ap.wps.com/l/cbCailyuDZrWPwXt","pdf",2347353,1,188,"English","en",105,"# Introduction\n# Literature Review\n## Exploring the Foundations: A Study of Background and Definitions\n## An Analysis of the Factors Influencing Life Insurance\n## Evolving Regulations and Industry Development: A History of Life Insurance and Solvency Standards\n## Machine Learning (ML)\n### Machine Learning Approaches\n### Machine Learning Models\n### Generalized Linear Model: Logistic Regression (LR)","[{\"question\":\"What does lapse rate mean in life insurance, and why is it important?\",\"answer\":\"Lapse rate refers to the rate of policy cancellations or expirations. Accurate prediction supports correct pricing, effective risk management, and future planning for life insurers.\"},{\"question\":\"What data source was used to build the prediction models?\",\"answer\":\"The study collected data through a risk survey administered to policyholders. It includes policyholder characteristics, policy details, and historical lapse patterns.\"},{\"question\":\"Which machine learning algorithms performed best for predicting lapse rate?\",\"answer\":\"Extreme Gradient Boosting, C 5:0, and random forest achieved the best results on the dataset. Several key policy and customer characteristics also showed significant predictive power.\"}]","Predicting Lapse Rate in Life Insurance - An Exploration of Machine Learning Techniques | PDF",1785893878,474,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-lapse-rate-in-life-insurance-an-exploration-of-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@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/predicting-lapse-rate-in-life-insurance-an-exploration-of-machine-learning-techniques/124680/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does lapse rate mean in life insurance, and why is it important?","Question",{"text":75,"@type":76},"Lapse rate refers to the rate of policy cancellations or expirations. Accurate prediction supports correct pricing, effective risk management, and future planning for life insurers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source was used to build the prediction models?",{"text":80,"@type":76},"The study collected data through a risk survey administered to policyholders. It includes policyholder characteristics, policy details, and historical lapse patterns.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms performed best for predicting lapse rate?",{"text":84,"@type":76},"Extreme Gradient Boosting, C 5:0, and random forest achieved the best results on the dataset. 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