[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117275-en":3,"doc-seo-117275-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117275,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting Default Loans using Machine Learning - Master Thesis","This master’s thesis evaluates how effectively machine learning algorithms predict default loans and compares algorithm performance to identify stronger models. The study starts by reviewing available machine learning methods, then implements and processes six algorithms on a labeled dataset containing 30,000 credit lenders from a Taiwanese credit lending company. Models are trained, tested, and validated using AUC as a primary metric, supported by Recall, Precision, and Accuracy. Results show strong predictive effectiveness, with Neural Network and Decision Tree achieving the best AUC values.","EuropeW/O0ISsl1 Jf(ClwT23) 􀂓  \nHandelsh0ysllolen Bl GRA 19703 Master Thesis  \nThesis Master of Science 100% - W  \nPredefinert informasjon  \nStartdato:  \nSluttdato:  \nEllsamensform: Flowkode:  \nIntern sensor:  \n09-01-2023 09:00 CET  \n03-07-2023 12:00 CESTT 202310ll11184IIINOOIIWIIT (Anonymisert)  \nTermin:  \nVurderingsform:  \n202310  \nNorsk 6-trinns sllala (A-F)  \nDelta􀂔er  \nNavn: Alexander Noreddin Habibi  \nlnformasjon fra delta􀂔er  \nTittel •: Predicting Default Loans using Machine Leaming  \nNaun pli ueileder •: Emil Stoltenberg  \nlnneholder besuarelsen Nei konfidensielt  \nmateriale7:  \nKan besuarelsenoffentliggj•res?:  \nJa  \nGruppe  \nljruppenaun: (Anonymisert)  \nljruppenummer: 287  \nAndre medlemmer i Deltakeren har innleuert i en enlleltmannsgruppe gruppen:  \nHandelshøyskolen BI  \nOslo, Fall 2023  \nPredicting Default Loans using Machine  \nLearning  \nHow effective are machine learning algorithms in  \npredicting  \ndefault loans?  \nAlexander Habibi  \nSupervisor: Emil Stoltenberg  \nMaster Thesis  \nMajor in Business Analytics  \nHandelshøyskolen BI  \nAcknowledgments  \nI now present to you my master’s thesis. This thesis marks the end of my master’s degree in Business Analytics at BI Handelshøyskolen, Oslo.  \nWorking on this thesis has been demanding but also exciting and educational. Throughout this semester, I have learned how to process and model machine learning algorithms to predict default in loans. These methods have been, and are still in high development, which makes the subject more attractive and challenging, which suited me perfectly.  \nI want to express my gratitude and appreciation to my supervisor for this master’s thesis Emil Stoltenberg. Your guidance has been valuable, and you have been helpful when needed and have pushed me through the semester.  \nI also would like to thank my father, brother, and girlfriend, who is always there for me and is a pillar of support.  \nAbstract  \nThis thesis aims to determine whether machine learning algorithms effectively predict default loans and which machine learning algorithms are better performers than others. The research started by gathering information about which machine learning methods there are, implementing and processing their algorithms, and determining their performances.  \nFor this thesis, I have used a dataset from a Taiwanese credit lending company consisting of 30,000 credit lenders, whereas the defaulters ofthis dataset are known. The choice has been made to train, test and validate six different machine learning algorithms, determine their performances, and gather helpful information on whether they are accurate in their predictions or flawed.  \nThe main research question for this thesis is:  \nHow effective are machine learning algorithms in predicting defaults in loans?  \nSome model performance measures have been used to determine the machine learning algorithms’ performances. The Area Under the Curve has been set as a primary model performance measure. In this classifier measure, a score between 0 and 1 is calculated. While a classifier with 1 AUC is the perfect model, a classifier with 0.5 AUC is as good as a random guessing one. There are also three other measurements to determine the final models: Recall, Precision, and Accuracy. The table below showcases the performance of the six algorithms after training and validation on the original dataset.  \nTable 1. Summary of the performance of all the different algorithms when trained and validated on the original dataset.  \nAlgorithm AUC Recall Precision Accuracy  \nLogistic Regression Neural Network k Nearest Neighbors Decision Tree Random Forest XGBoost  \n0.762 0.790 0.760 0.784 0.766  \n0.783  \n0.34 0.34 0.33 0.38 0.30  \n0.37  \n0.67 0.68 0.63 0.60 0.67  \n0.64  \n0.816 0.820 0.815 0.819 0.815  \n0.822  \nFrom the performances, it has been determined that machine learning algorithms are highly effective in predicting default in loans. The average accuracy between the algorithms is 81.7%, and AUC is relatively high. Th","cbCaitYoT8GdVyvb","https://ap.wps.com/l/cbCaitYoT8GdVyvb","pdf",982516,1,81,"English","en",105,"# Acknowledgments\n# Abstract\n# 1. Introduction\n## 1.1 Project Context\n## 1.2 Problem Description\n## 1.3 Research Objective\n## 1.3.1 Main research question\n## 1.3.2 Sub Questions\n# 2. Literature review\n# 3. Theory/ Classification\n## 3.1 Definition of credit risk\n## 3.2 Definition of Default\n## 3.3 Machine Learning\n## 3.3.2 Learning methods","[{\"question\":\"What is the main research question of the thesis?\",\"answer\":\"How effective are machine learning algorithms in predicting defaults in loans?\"},{\"question\":\"Which algorithms are evaluated in the study?\",\"answer\":\"Six algorithms are trained and validated: Logistic Regression, Neural Network, k Nearest Neighbors, Decision Tree, Random Forest, and XGBoost.\"},{\"question\":\"How is model performance measured in the thesis?\",\"answer\":\"AUC is used as the primary metric, and the study also reports Recall, Precision, and Accuracy to assess final model quality.\"}]",1785674936,204,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"predicting-default-loans-using-machine-learning-master-thesis","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/predicting-default-loans-using-machine-learning-master-thesis/117275/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main research question of the thesis?","Question",{"text":74,"@type":75},"How effective are machine learning algorithms in predicting defaults in loans?","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which algorithms are evaluated in the study?",{"text":79,"@type":75},"Six algorithms are trained and validated: Logistic Regression, Neural Network, k Nearest Neighbors, Decision Tree, Random Forest, and XGBoost.",{"name":81,"@type":72,"acceptedAnswer":82},"How is model performance measured in the thesis?",{"text":83,"@type":75},"AUC is used as the primary metric, and the study also reports Recall, Precision, and Accuracy to assess final model quality.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]