[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119340-en":3,"doc-seo-119340-105":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":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},119340,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Research on Personal Loan Default Risk Assessment Based on Machine Learning","Rapid Internet and big-data growth have expanded the scale of personal loans while increasing the complexity of individual credit records, making accurate credit rating and default-risk assessment critical for financial decision-making. The study reviews recent machine-learning research and builds four models—Logistic Regression, Support Vector Machine, Naïve Bayes, and Deep Neural Networks. Using Kaggle bank and credit bureau data from India, the work preprocesses the dataset, trains and tests each model, and compares performance. Results show machine-learning models improve accuracy and efficiency, with the Deep Neural Network achieving the strongest overall performance.","Research on Personal Loan Default Risk Assessment Based on Machine Learning  \nGuangsen Liu  \nAberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Foshan, China  \nAbstract. In the present era of rapid development of the Internet and big data, the scale of personal loans and the complexity of personal credit data are growing rapidly. Accurately assessing personal credit rating and personal loan default risk has become an important topic in the financial field. This paper analyzes the current research status of other scholars on machine learning in personal loan default risk assessment in recent years, and selects Logistic Regression, Support Vector Machine, Naïve Bayes and Deep Neural Networks as research model. Meanwhile, this paper selects the Kaggle website data of a bank and credit information bureau in India, preprocesses the dataset and applies it to the training and testing of the models, and finally derives the performance results of the four models. The results of the study show that the machine learning models have better accuracy and higher efficiency in analyzing personal credit data and assessing the risk of personal loan default. Among them, the Deep Neural Network has the best overall performance compared to the other three machine learning models. The research in this paper has certain research significance for the research of machine learning in personal loan default risk assessment.  \n1 Introduction  \nIn the field of finance, the Personal Loan means borrowing of money from an organization such as Bank, Credit Cooperatives, etc. Banks grant loans to individuals to help them alleviate their financial difficulties and promote socio-economic development, and also allow the banks to earn interest from borrowers.  \nHowever, personal loans create an important financial problem, that is, loan default. Loan default is when a lender fails to repay a loan within a specified time period. Loan default can be harmful: For lending institutions, this can result in a loss of funds. If the amount of overdue loans is large, it can even result in a breakdown of the lending institution's financial chain. If the amount of overdue loans is large, it can even lead to a break in the lending institutions’financial chain. Therefore, predicting the risk of personal loan is essential for banks, which affects their decisions on whether or not to allow lending loan to borrowers [1] .  \nThe traditional method of evaluating the risk of default on personal loan is an expert’s evaluation based on experience [2]. However, as the development of the Internet and big data,  \nCorresponding author: [20213801012@m.scnu.edu.cn](20213801012@m.scnu.edu.cn)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nonline loans are gradually emerging and increasing year by year. Also, the threshold for individuals to apply for loans has been lowered compared to the past. At the same time, there has been a quantitative growth in the amount of personal credit data by leaps and bounds, and the types of data become more diversified [3]. At this point it is difficult to adapt the use of expert experience in evaluating personal credit is no longer sufficient for this situation. Therefore, in the context of combining the lending business with the Internet, how to assess the default risk of individual loans from multi-dimensional and ever-changing credit data has become a challenge for the lending industry.  \nIn the face of the current situation where the number of personal credit data has increased, the types have become complex, and the pressure on lending institutions to evaluate personal credit is growing, constructing machine learning models to predict personal credit rating as well as the risk of loan defaults will effectively imp","cbCaiu1cheIcytRZ","https://ap.wps.com/l/cbCaiu1cheIcytRZ","pdf",694865,1,14,"English","en",105,"# Introduction\n## Motivation and problem of loan default risk\n## Traditional expert-based evaluation vs. data-driven methods\n## Prior research and challenges (class imbalance, varied loan types)\n## Paper structure and methodology overview","[{\"question\":\"Why is assessing personal loan default risk important for banks?\",\"answer\":\"Loan default can cause direct losses for lending institutions and may disrupt their financial stability when overdue amounts grow. Predicting default risk supports lending decisions and risk management.\"},{\"question\":\"Which machine learning models are used in this research?\",\"answer\":\"The study selects Logistic Regression, Support Vector Machine, Naïve Bayes, and Deep Neural Networks as the research models for personal loan default risk assessment.\"},{\"question\":\"What dataset is used and what steps are applied before training?\",\"answer\":\"The paper uses Kaggle data from a bank and a credit information bureau in India, preprocesses the dataset, and then applies it to training and testing of the four models.\"}]","Research on Personal Loan Default Risk Assessment Based on Machine Learning | PDF",1785723782,35,{"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},"research-on-personal-loan-default-risk-assessment-based-on-machine-learning","",{"@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/research-on-personal-loan-default-risk-assessment-based-on-machine-learning/119340/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is assessing personal loan default risk important for banks?","Question",{"text":75,"@type":76},"Loan default can cause direct losses for lending institutions and may disrupt their financial stability when overdue amounts grow. Predicting default risk supports lending decisions and risk management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used in this research?",{"text":80,"@type":76},"The study selects Logistic Regression, Support Vector Machine, Naïve Bayes, and Deep Neural Networks as the research models for personal loan default risk assessment.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset is used and what steps are applied before training?",{"text":84,"@type":76},"The paper uses Kaggle data from a bank and a credit information bureau in India, preprocesses the dataset, and then applies it to training and testing of the four models.","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"]