[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124525-en":3,"doc-seo-124525-105":30,"detail-sidebar-cat-0-en-105":95},{"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},124525,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Predicting Travel Insurance Purchases in an Insurance Firm Through Machine Learning Methods After COVID-19","Travel insurance acts as a financial safeguard against unexpected losses during trips, and the COVID-19 era has intensified both demand and risk, requiring insurers to predict customers’ likelihood to purchase. The study builds machine-learning models to forecast which consumer segments are most likely to buy travel insurance, enabling targeted sales and improved revenue. A Kaggle dataset of prior clients is analyzed using KNN, decision tree, SVM, Naïve Bayes, logistic regression, and random forest, with cleaning, performance assessment via accuracy, F1, and ROC-AUC. KNN achieves the strongest overall results with 0.81 accuracy and 0.78 AUC.","Journal of Informatics and Web Engineering  \nVol. 2 No. 2 (September 2023) eISSN: 2821-370X  \nPredicting Travel Insurance Purchases in an Insurance Firm Through Machine Learning Methods After COVID-19  \nShiuh Tong Lim1, Joe Yee Yuan2, Khai Wah Khaw3*, XinYing Chew4  \n1,2,3School of Management, Universiti Sains Malaysia, E43, Jalan Sasaran, Minden Heights, 11800, Penang, Malaysia.  \n4School of Computer Sciences, Universiti Sains Malaysia, E43, Jalan Sasaran, Minden Heights, 11800, Penang, Malaysia.  \n*corresponding author: ([khaiwah@usm.my](khaiwah@usm.my); 0000-0003-2646-6477)  \nAbstract – Travel insurance serves as a crucial financial safeguard, offering coverage against unforeseen expenses and losses incurred during travel. With the advent of the proliferation of insurance types and the amplified demand for Covid-related coverage, insurance companies face the imperative task of accurately predicting customers’ likelihood to purchase insurance. This can assist the insurance providers in focusing on the most lucrative clients and boosting sales. By employing advanced machine learning techniques, this study aims to forecast the consumer segments most inclined to acquire travel insurance, allowing targeted strategies to be developed. A comprehensive analysis was carried out on a Kaggle dataset comprising prior clients of a travel insurance firm utilizing the K-Nearest Neighbors (KNN), Decision Tree Classifier (DT), Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF) models. Extensive data cleaning was done before model building. Performance evaluation was then based on accuracy, F1 score, and the Area Under Curve (AUC) with Receiver Operating Characteristics (ROC) curve. Inexplicably, KNN outperformed other models, achieving an accuracy of 0.81, precision of 0.82, recall of 0.82, F1 score of 0.80, and an AUC of 0.78. The findings ofthis study are a valuable guide for deploying machine learning algorithms in predicting travel insurance purchases, thus empowering insurance companies to target the most lucrative clientele and bolster revenue generation.  \nKeywords—Travel Insurance, Machine Learning Techniques, Predictive Modelling, Customer Segmentation, Revenue Optimization  \nReceived: 17 June 2023; Accepted: 7 July 2023; Published: 16 September 2023  \nI. INTRODUCTION  \nOur daily lives are often filled with uncertainties, particularly when we embark on a journey. All too frequently, incidents like lost luggage, delayed flights, mishaps on public transit, foodborne infections, injuries, and health problems occur. To mitigate potential losses, individuals often turn to travel insurance. Travel insurance is specially designed to cover expenses and losses incurred due to unanticipated events during travel [1] . It acts as a financial buffer against the risk and mishaps that travellers may encounter while on vacation, especially after the COVID-19 pandemic. This is because the pandemic has become a significant change in all sectors [2] . The scope of coverage varied depending on the policy encompassing risks such as baggage loss, travel deposit loss, trip cancellation, medical costs including coverage for COVID-19, physical injuries, travel service provider bankruptcy, personal liability for a  \ntort committed against citizens of the host countries, legal expenses, and even unforeseen situations typically not covered by standard policies, like terrorism and denied boarding [3] .  \nIn the current landscape, the COVID-19 pandemic has heightened the risks associated with travel, starting from the various modes of transportation. For instance, flight travel requires waiting in security lines and airport terminals full of regularly touched surfaces. Furthermore, maintaining social distancing becomes challenging during the flight itself. Additionally, travellers frequently experience last-minute flight disruptions due to travel restrictions by destination countries. Upon reaching their destinatio","cbCaijMLQrFkSB9s","https://ap.wps.com/l/cbCaijMLQrFkSB9s","pdf",1073836,1,16,"English","en",105,"# Introduction\n## Travel uncertainty and the role of travel insurance\n## COVID-19 driven changes in travel risk and coverage\n## Tourism impact and increased adoption of travel insurance\n# Method and evaluation\n## Dataset and machine learning models\n## Data cleaning and performance metrics","[{\"question\":\"Why is predicting travel insurance purchases important for insurers after COVID-19?\",\"answer\":\"The pandemic increased travel-related risks and expanded demand for coverage, so insurers need accurate predictions to focus on more likely buyers and improve sales targeting.\"},{\"question\":\"Which machine learning models are used to predict travel insurance purchase likelihood?\",\"answer\":\"The study evaluates K-Nearest Neighbors, Decision Tree Classifier, Support Vector Machines, Naïve Bayes, Logistic Regression, and Random Forest.\"},{\"question\":\"How are model performances assessed in the study?\",\"answer\":\"Performance is measured using accuracy, F1 score, and ROC curve with the Area Under Curve (AUC).\"},{\"question\":\"What result does the study report as the best-performing model?\",\"answer\":\"KNN outperforms other models, reaching 0.81 accuracy and an AUC of 0.78, with F1 score of 0.80.\"}]","Predicting Travel Insurance Purchases in an Insurance Firm Through Machine Learning Methods After COVID-19 | 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is predicting travel insurance purchases important for insurers after COVID-19?","Question",{"text":75,"@type":76},"The pandemic increased travel-related risks and expanded demand for coverage, so insurers need accurate predictions to focus on more likely buyers and improve sales targeting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used to predict travel insurance purchase likelihood?",{"text":80,"@type":76},"The study evaluates K-Nearest Neighbors, Decision Tree Classifier, Support Vector Machines, Naïve Bayes, Logistic Regression, and Random Forest.",{"name":82,"@type":73,"acceptedAnswer":83},"How are model performances assessed in the study?",{"text":84,"@type":76},"Performance is measured using accuracy, F1 score, and ROC curve with the Area Under Curve (AUC).",{"name":86,"@type":73,"acceptedAnswer":87},"What result does the study report as the best-performing model?",{"text":88,"@type":76},"KNN outperforms other models, reaching 0.81 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