[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127844-en":3,"doc-seo-127844-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127844,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Understanding and predicting lapses in mortgage life insurance using a machine learning approach","Mortgage Life Insurance (MLI) creates retention challenges for insurers, especially after Europe’s 2009 regulatory changes and intensified competition from low-premium entrants. The research develops a predictive model identifying MLI policies at risk of lapse and separating the factors that drive that risk. Using data from an insurance company and its partner bank, it compares Logistic Regression, Random Forest, Neural Networks, and XGBoost, with XGBoost performing best. SHAP explanations improve interpretability and highlight bank-derived features, supporting proactive customer identification, engagement, and policy reformulation.","Expert Systems With Applications 255 (2024) 124753  \nContents lists available at ScienceDirect  \nExpert Systems With Applications  \njournal [homepage:](homepage: www.elsevier.com/locate/eswa)[ www.elsevier.com/locate/eswa](homepage: www.elsevier.com/locate/eswa)  \n| Understanding and predicting lapses in mortgage life insurance using a machine learning approach\u003Cbr>*\u003Cbr>Carlos Manteigas , Nuno Anto´nio\u003Cbr>NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>External data sources Lapse risk\u003Cbr>Machine learning Mortgage life insurance |  | Mortgage Life Insurance (MLI) offers lucrative opportunities for insurers. However, customer retention has proven to be a daunting challenge, particularly following the regulatory changes of 2009 in Europe. New market entrants strategically employing low-premium tactics have reshaped the competitive landscape, leading established insurers and banks to grapple with retaining their MLI clientele. Consequently, increasing policy lapses hold critical implications for these financial entities. Responding to this intricate landscape, our research presentsa predictive model that pinpoints the MLI policies at risk of lapse and disentangles the underlying factors propelling this risk. The objective is to provide insurers with a practical and strategic tool to improve customer retention, enabling them to identify specific actions to reduce customer attrition, improve financial stability, and strengthen customer loyalty. We used a dataset obtained from an insurance company and its partner bank to build the model. The effectiveness of four machine learning models, namely Logistic Regression, Random Forest, Neural Networks, and XGBoost, is investigated, with XGBoost outperforming the others. SHapley Additive exPlanations (SHAP) were utilized to bolster interpretability, thereby facilitating the conception and explication of the predictive model’s most influential features. Underpinning the benefits of a nuanced exploration, the study’s focus on a solitary insurance protection product and integrating bank data enabled us to apprehend the multifaceted drivers of lapse behavior. The study accentuates the merit of comprehensive data encapsulating a holistic perspective, with the four most influential features originating from bank data. From an insurer’s standpoint, this research provides a strategic vantage point to proactively identify and engage with customers at risk of policy lapse and reformulate their policies to mitigate customer attrition. |\n\n1. Introduction  \nAn insurance contract is represented by a policy between an individual or an entity (policyholder) and an insurance company. The policyholder pays out an amount (called the premium) in exchange for financial protection if certain events or circumstances occur, such as accidents, illnesses, or damages to property. In essence, it allows individuals or organizations to transfer the financial impact of certain types of risks to an insurance company.  \nDepending on the type of insurance policy, insurance can cover a wide range of risks. The most common types are auto, health, homeowners, and life.  \nLife insurance provides financial support to the policyholder’s beneficiaries in case of their death. It is an important form of protection for individuals who have dependents or other financial obligations, providing peace of mind and ensuring that their loved ones are taken  \ncare of in case of their death.  \nWhile this basic idea of protection is simple, the technical workings of life insurance are a little more complex. The company must ensure that policyholders receive the appropriate amount of coverage at a fair price and that it can generate returns on their investments while maintaining sufficient reserves to pay out future claims. It is a difficult balance to maintain, considering that life","cbCaicraOC5HQTU0","https://ap.wps.com/l/cbCaicraOC5HQTU0","pdf",4616939,2,1,21,"English","en",105,"# Introduction\n## Mortgage life insurance context\n## Data sources and modeling objective\n# Methods\n## Machine learning models evaluated\n## Model interpretability with SHAP\n# Results and implications\n## Most influential features from bank data","[{\"question\":\"Why is predicting policy lapses important for mortgage life insurance providers?\",\"answer\":\"Policy lapses directly affect insurers and banks managing retention after regulatory and competitive pressures. Identifying lapses helps improve customer retention and financial stability.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"The study investigates Logistic Regression, Random Forest, Neural Networks, and XGBoost. XGBoost outperforms the other models.\"},{\"question\":\"How does the research improve interpretability of the predictive model?\",\"answer\":\"SHapley Additive exPlanations (SHAP) are used to support interpretability and identify the most influential features behind predicted lapse risk.\"}]","Understanding and predicting lapses in mortgage life insurance using a machine learning approach | PDF",1785942301,53,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"understanding-and-predicting-lapses-in-mortgage-life-insurance-using-a-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/understanding-and-predicting-lapses-in-mortgage-life-insurance-using-a-machine-learning-approach/127844/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",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 predicting policy lapses important for mortgage life insurance providers?","Question",{"text":76,"@type":77},"Policy lapses directly affect insurers and banks managing retention after regulatory and competitive pressures. Identifying lapses helps improve customer retention and financial stability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are evaluated in the study?",{"text":81,"@type":77},"The study investigates Logistic Regression, Random Forest, Neural Networks, and XGBoost. XGBoost outperforms the other models.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the research improve interpretability of the predictive model?",{"text":85,"@type":77},"SHapley Additive exPlanations (SHAP) are used to support interpretability and identify the most influential features behind predicted lapse risk.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]