[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122627-en":3,"doc-seo-122627-105":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},122627,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Auto-Insurance Fraud Detection Using Machine Learning Classification Models - Research Report","Auto-insurance fraud detection research compares six machine learning classification algorithms—XGBoost, Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, and Naïve Bayes—to identify the best model for fraudulent claim detection. Model performance is evaluated using a confusion matrix and metrics including Accuracy, Precision, Recall, and F1-measure. Random Forest achieves the highest accuracy, while XGBoost delivers the best precision and F1, and Decision Tree attains the top recall. Feature selection is guided by Random Forest classifier significance.","Auto-Insurance Fraud Detection Using Machine Learning Classification Models  \nToluwalope Owolabi 1 , Essa Q. Shahra 1 , and Shadi Basurra 1  \nFaculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham B5 5JU, United Kingdom;[Toluwalope.Owolabi, Essa.Shahra, Shadi.Basurra]@[bcu.ac.uk](bcu.ac.uk)  \nAbstract. This work explored six machine learning algorithms: Extreme Gradient Boosting (XGBoost), Logistic Regression, Random Forest, Decision tree, Support Vector Machine (SVM), and Naïve Bayes to determine the best algorithm for detecting insurance fraud. The following were used to evaluate the six models: Confusion matrix, Accuracy, Precision, Recall, and F1-measure. The result showed that Random Forest outperformed the others in terms of accuracy. Extreme Gradient Boosting (Xgboost) had the highest precision and F1-measure scores, while the Decision Tree had the highest Recall score. Although two methods (Analysis of Variance (ANOVA) and Random Forest Classifier) were compared to determine the best feature selection, the significant features were selected using the Random Forest classifier because of the many benefits of using this method. The results of this study will be beneficial to insurance companies, stakeholders and policyholders.  \nKeywords: Fraud Detection, Classification, Machine Learning, Random Forest.  \n1 Introduction  \nUnderstanding insurance and insurance fraud are paramount to understanding its significance and effect. Viaene et al in [1] define insurance as a contractual relationship between an insurer and an insurance taker in which the insurer pays the insurance taker for losses sustained by an insured party. As the number of cars increases, auto insurance is now increasingly important in the whole economic growth and the lives of individuals. The insurance covers natural disasters, vehicle accidents, including accidents caused by a third-party [2] . The market for insurance will therefore experience increase in capital as more insurers place their trust in its positive growth, resulting in more competition between the companies [3] . Insurance fraud has been defined by the California Department of Insurance as the intentional denial of a benefit a person is entitled to, or the intentional lying to achieve an advantage. There are a number of negative consequences associated with insurance fraud, including substantial losses for companies, negative impact on their price strategy and their economic advantage over time. According to [4], the costs of fraud relating to insurance  \n2 Toluwalope Owolabi et al.  \nare envisioned to be more than forty billion dollars annually, meaning that the average American family will have to pay between $400 and $700 in increased premiums due to insurance fraud every year. According to [5] there is a notable amount of money associated with insurance fraud worldwide. Due to this, both in the U.S. and around the world, it is imperative to explore the connection between fraud detection and auto insurance. The severity of insurance fraud maybe difficult to estimate due to the fact that it is often not detected and prosecuted, and this could be because auto insurance companies deal with so many claims. Therefore, it is impossible for them to manually verify each of them for fraud, which makes minimizing the fraud problem difficult. As a result, many use machine learning and data mining methods to minimize the problem of fraud in the insurance industry.  \nIn this paper, we aim to determine the best predictive machine learning model that can be used to detect fraudulent insurance claims and determining the features that significantly influence auto insurance fraud.  \nThe paper is organised as follows; section 2 presents the literature review. Section 3 demonstrates the methodology in more detail. Section 4 presents the results and discussion. Finally, section 5 concludes the work.  \n2 Literature Review  \nResearch and academics have been developing rob","cbCaijsqQProU31M","https://ap.wps.com/l/cbCaijsqQProU31M","pdf",483069,1,10,"English","en",105,"# Introduction\n# Literature Review\n# Methodology\n# Results and Discussion\n# Conclusion","[{\"question\":\"Which algorithms are evaluated for detecting auto insurance fraud?\",\"answer\":\"The study evaluates XGBoost, Logistic Regression, Random Forest, Decision Tree, Support Vector Machine (SVM), and Naïve Bayes.\"},{\"question\":\"How are the models evaluated in this work?\",\"answer\":\"Evaluation uses a confusion matrix and metrics including Accuracy, Precision, Recall, and F1-measure.\"},{\"question\":\"Which model performs best overall and for which metrics?\",\"answer\":\"Random Forest outperforms others in accuracy; XGBoost has the highest precision and F1-measure; Decision Tree has the highest recall.\"}]","Auto-Insurance Fraud Detection Using Machine Learning Classification Models - Research Report | PDF",1785811789,25,{"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},"auto-insurance-fraud-detection-using-machine-learning-classification-models-research-report","",{"@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/auto-insurance-fraud-detection-using-machine-learning-classification-models-research-report/122627/",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},"Which algorithms are evaluated for detecting auto insurance fraud?","Question",{"text":75,"@type":76},"The study evaluates XGBoost, Logistic Regression, Random Forest, Decision Tree, Support Vector Machine (SVM), and Naïve Bayes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the models evaluated in this work?",{"text":80,"@type":76},"Evaluation uses a confusion matrix and metrics including Accuracy, Precision, Recall, and F1-measure.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best overall and for which metrics?",{"text":84,"@type":76},"Random Forest outperforms others in accuracy; XGBoost has the highest precision and F1-measure; Decision Tree has the highest recall.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]