[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117980-en":3,"doc-seo-117980-105":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},117980,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning in Mortgage Scoring - A Comparative Analysis with Traditional Statistical Methods","This paper examines how machine learning improves real estate mortgage scoring compared with traditional statistical methods. The analysis focuses on efficiency, robustness, and productivity gains, emphasizing reduced reliance on extensive data preprocessing and less manual intervention. Findings indicate that machine learning can handle missing values and complex data issues more effectively, supporting faster and more accurate mortgage decisions. The study also discusses advantages beyond predictive performance, including time savings for data management and preprocessing.","University for Business and Technology in Kosovo  \nUBT Knowledge Center  \n\n| UBT International Conference | 2023 UBT International Conference |\n| --- | --- |\n| Oct 28th, 8:00 AM-Oct 29th, 6:00 PM\u003Cbr>Machine Learning in Mortgage Scoring: A Comparative Analysis with Traditional Statistical Methods\u003Cbr>Visar Hoxha\u003Cbr>University for Business and Technology-UBT, [visar.hoxha@ubt-uni.net](visar.hoxha@ubt-uni.net)\u003Cbr>Blerta Demjaha\u003Cbr>[blerta.demjaha@eukos.org](blerta.demjaha@eukos.org)\u003Cbr>Veli Lecaj\u003Cbr>University for Business and Technology-UBT, [veli.lecaj@ubt-uni.net](veli.lecaj@ubt-uni.net)\u003Cbr>Hazer Dana\u003Cbr>University for Business and Technology, [hazer.dana@ubt-uni.net](hazer.dana@ubt-uni.net)\u003Cbr>Fuat Pallaska\u003Cbr>University for Business and Technology-UBT, [fuat.pallaska@ubt-uni.net](fuat.pallaska@ubt-uni.net)\u003Cbr>Follow this and additional works at: [https://knowledgecenter.ubt-uni.net/conference](https://knowledgecenter.ubt-uni.net/conference)\u003Cbr> Part of the Law Commons |  |\n\nRecommended Citation  \nHoxha, Visar; Demjaha, Blerta; Lecaj, Veli; Dana, Hazer; and Pallaska, Fuat, \"Machine Learning in Mortgage Scoring: A Comparative Analysis with Traditional Statistical Methods\" (2023) . UBT International Conference. 4.  \n[https://knowledgecenter.ubt-uni.net/conference/IC/LAW/4](https://knowledgecenter.ubt-uni.net/conference/IC/LAW/4)  \nThis Event is brought to you for free and open access by the Publication and Journals at UBT Knowledge Center. It has been accepted for inclusion in UBT International Conference by an authorized administrator of UBT Knowledge Center. For more information, please contact [knowledge.center@ubt-uni.net](knowledge.center@ubt-uni.net).  \nMachine Learning in Mortgage Scoring: A Comparative Analysis with Traditional  \nStatistical Methods  \nVisar Hoxha1, Blerta Demjaha2 Veli Lecaj1 Hazer Dana1 Fuat Pallaska1  \n1 Faculty of Real Estate, UBT  \n[Visar.hoxha@ubt-uni.net](Visar.hoxha@ubt-uni.net)  \n2 Real Estate Department, College ESLG  \n[blerta.demjaha@eukos.org](blerta.demjaha@eukos.org)  \n1 Faculty of Real Estate, UBT  \n[Veli.lecaj@ubt-uni.net](Veli.lecaj@ubt-uni.net)  \n1 Faculty of Real Estate, UBT  \n[hazer.dana@ubt-uni.net](hazer.dana@ubt-uni.net)  \n1 Faculty of Real Estate, UBT  \n[fuat.pallaska@ubt-uni.net](fuat.pallaska@ubt-uni.net)  \nAbstract: This paper delves into the comparative advantages of machine learning over traditional statistical methods in real estate mortgage scoring. By examining the efficiency, robustness, and productivity gains of machine learning, the study underscores its potential to transform the financial industry, particularly in mortgage application processing. The findings highlight the reduced need for extensive data preprocessing with machine learning and its implications for faster and more accurate mortgage decision-making.  \nKeywords: Machine Learning, Real Estate Mortgage, Financial Industry, Data Preprocessing, Traditional Statistical Methods.  \n1. Introduction  \nThe evolution of machine learning (ML) has brought about significant changes in various sectors, including the financial industry. While traditional statistical methods have been effective, they often  \nnecessitate extensive data preprocessing and manual intervention. This paper aims to explore the advantages of machine learning over these traditional methods, especially in the realm of real estate mortgage scoring within large banking institutions.  \nBeyond the question of predictive performance, machine learning methods offer undeniable advantages over traditional parametric scoring approaches. These advantages include significant productivity gains, reduced time for data management and preprocessing, and the ability to handle missing values, strong correlations, and other data issues. The traditional approach of a statistician involves multiple steps, from data treatment to variable selection. In contrast, machine learning algorithms, such as classification trees and random forests, simplify these processes by aut","cbCaifzkGzJojazs","https://ap.wps.com/l/cbCaifzkGzJojazs","pdf",225790,1,7,"English","en",105,"# Introduction\n# Literature review","[{\"question\":\"What is the main purpose of the study on mortgage scoring?\",\"answer\":\"The paper aims to explore and compare the advantages of machine learning over traditional statistical methods for real estate mortgage scoring, especially within large banking institutions.\"},{\"question\":\"What benefits does machine learning provide compared with traditional statistical methods?\",\"answer\":\"The study highlights productivity gains, reduced time for data management and preprocessing, improved handling of missing values and correlated predictors, and overall robustness and efficiency.\"},{\"question\":\"How does the paper explain differences in data preparation and modeling steps?\",\"answer\":\"Traditional approaches require multiple manual steps such as data treatment and variable selection, while machine learning algorithms like classification trees and random forests can autonomously determine optimal groupings and manage correlated predictors.\"}]","Machine Learning in Mortgage Scoring - A Comparative Analysis with Traditional Statistical Methods | PDF",1785680623,18,{"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},"machine-learning-in-mortgage-scoring-a-comparative-analysis-with-traditional-statistical-methods","",{"@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/machine-learning-in-mortgage-scoring-a-comparative-analysis-with-traditional-statistical-methods/117980/",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","2026-08-02",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 purpose of the study on mortgage scoring?","Question",{"text":75,"@type":76},"The paper aims to explore and compare the advantages of machine learning over traditional statistical methods for real estate mortgage scoring, especially within large banking institutions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What benefits does machine learning provide compared with traditional statistical methods?",{"text":80,"@type":76},"The study highlights productivity gains, reduced time for data management and preprocessing, improved handling of missing values and correlated predictors, and overall robustness and efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper explain differences in data preparation and modeling steps?",{"text":84,"@type":76},"Traditional approaches require multiple manual steps such as data treatment and variable selection, while machine learning algorithms like classification trees and random forests can autonomously determine optimal groupings and manage correlated predictors.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]