[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123846-en":3,"doc-seo-123846-105":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},123846,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Predictive Modeling for Insurance Pricing - Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms","This thesis examines insurance pricing with the goal of improving predictive accuracy through a comparative analysis of traditional actuarial techniques and modern machine learning algorithms. Using real-world datasets from insurance companies, the research applies five distinct methodologies to analyze key variables in the insurance dataset. The primary objective is to identify the most effective approaches for forecasting claim amounts, delivering predictive gains and practical business value for risk management innovation and excellence in the insurance industry.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nPredictive Modeling for Insurance Pricing: A Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms  \nPermalink  \n[https://escholarship.org/uc/item/0p45d0bv](https://escholarship.org/uc/item/0p45d0bv)  \nAuthor  \nLyu, Ting  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nPredictive Modeling for Insurance Pricing:  \nA Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms  \nA thesis submitted in partial satisfaction of the requirements for the degree  \nMaster of Science in Statistics  \nby  \nTing Lyu  \n© Copyright by Ting Lyu 2024  \nABSTRACT OF THE THESIS  \nPredictive Modeling for Insurance Pricing:  \nA Comparative Analysis of Actuarial Techniques  \nand Machine Learning Algorithms  \nby  \nTing Lyu  \nMaster of Science in Statistics  \nUniversity of California, Los Angeles, 2024  \nProfessor Yingnian Wu, Chair  \nThis thesis examines insurance pricing with the goal of improving predictive accuracy through a comparative analysis of traditional actuarial techniques and modern machine learning algorithms. By utilizing real-world datasets from insurance companies, the research applies five distinct methodologies to analyze the key variables within the insurance dataset. The primary objective is to identify the most effective approaches in forecasting claim amounts. The findings ofthis study seek to advance predictive accuracy and provide substantial business value, thereby promoting innovation and excellence in risk management within the insurance industry.  \nThe thesis of Ting Lyu is approved.  \nArash A. Amini  \nHongquan Xu  \nYingnian Wu, Committee Chair  \nUniversity of California, Los Angeles  \n2024  \nTo my parents  \nfor all the love and support  \nTable of Contents  \n1. Introduction .......................................................................................................................... 1  \n2. Literature Review ................................................................................................................3  \n2.1 Traditional Actuarial Techniques ....................................................................................3  \n2.2 Evolution of Machine Learning in insurance...................................................................4  \n2.3 Potential Limitations between Traditional and Modern Techniques ...............................8  \n3. Data Description................................................................................................................. 10  \n3.1 Description of Data Collection Process and Data Sources ............................................ 10  \n3.2 Explanation of the Dataset Used for Analysis ............................................................... 11  \n3.3 Evaluation Metrics ......................................................................................................... 14  \n4. Methodology ....................................................................................................................... 17  \n4.1 Generalized Linear Models (GLMs) .............................................................................. 17  \n4.2 Credibility Theory.......................................................................................................... 19  \n4.3 Decision Trees ...............................................................................................................21  \n4.4 Random Forests .............................................................................................................23  \n4.5 Gradient Boosting ..........................................................................................................25  \n5. Comparative Analysis........................................................................................................2","cbCaijOulfAcTmu8","https://ap.wps.com/l/cbCaijOulfAcTmu8","pdf",706929,1,42,"English","en",105,"# Introduction\n# Literature Review\n## Traditional Actuarial Techniques\n## Evolution of Machine Learning in insurance\n## Potential Limitations between Traditional and Modern Techniques\n# Data Description\n## Description of Data Collection Process and Data Sources\n## Explanation of the Dataset Used for Analysis\n## Evaluation Metrics\n# Methodology\n## Generalized Linear Models (GLMs)\n## Credibility Theory\n## Decision Trees\n## Random Forests\n## Gradient Boosting\n# Comparative Analysis\n# Discussion\n## Exploring Advanced Ensemble Methods\n## Hyperparameter Optimization of Machine Learning Algorithm\n## Ethical and Regulatory","[{\"question\":\"What is the main goal of the thesis on insurance pricing?\",\"answer\":\"The thesis aims to improve predictive accuracy by comparing traditional actuarial techniques with modern machine learning algorithms for insurance pricing.\"},{\"question\":\"Which methodologies are compared to forecast claim amounts?\",\"answer\":\"The research applies five methodologies, including generalized linear models (GLMs), credibility theory, decision trees, random forests, and gradient boosting.\"},{\"question\":\"What data and evaluation focus support the comparative analysis?\",\"answer\":\"The study uses real-world insurance datasets and focuses on analyzing key variables and employing evaluation metrics to determine the most effective approaches for forecasting claim amounts.\"}]","Predictive Modeling for Insurance Pricing - Comparative Analysis of Actuarial Techniques and Machine Learning Algorithms | PDF",1785818860,106,{"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},"predictive-modeling-for-insurance-pricing-comparative-analysis-of-actuarial-techniques-and-machine-learning-algorithms","",{"@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/predictive-modeling-for-insurance-pricing-comparative-analysis-of-actuarial-techniques-and-machine-learning-algorithms/123846/",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","2026-08-04",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 is the main goal of the thesis on insurance pricing?","Question",{"text":75,"@type":76},"The thesis aims to improve predictive accuracy by comparing traditional actuarial techniques with modern machine learning algorithms for insurance pricing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which methodologies are compared to forecast claim amounts?",{"text":80,"@type":76},"The research applies five methodologies, including generalized linear models (GLMs), credibility theory, decision trees, random forests, and gradient boosting.",{"name":82,"@type":73,"acceptedAnswer":83},"What data and evaluation focus support the comparative analysis?",{"text":84,"@type":76},"The study uses real-world insurance datasets and focuses on analyzing key variables and employing evaluation metrics to determine the most effective approaches for forecasting claim amounts.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]