[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125767-en":3,"doc-seo-125767-105":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},125767,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Prediction of Loan Approval in Banks using Machine Learning Approach - Research Summary","Loan approval in the banking sector faces growing complexity as demand for borrowing increases and institutions must evaluate applicants’ eligibility while managing default risk. This research proposes combining machine learning models with ensemble learning to estimate the probability of loan acceptance for individual requests. The approach aims to improve accuracy in selecting qualified candidates, reduce the time required for sanctioning, and support both applicants and bank employees. Using Random Forest, Naive Bayes, Decision Tree, and KNN, the Naive Bayes model achieved the highest reported accuracy of 83.73%.","Prediction of Loan Approval in Banks using Machine Learning  \nApproach  \nViswanatha V 1, Ramachandra A.C2, Vishwas K N3 and Adithya G4  \n1Assistant Professor, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology,  \nBangalore, INDIA  \n2Professor, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology,  \nBangalore, INDIA  \n3Student, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore,  \nINDIA  \n4Student, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore,  \nINDIA  \n[3](3Corresponding Author: viswas779@gmail.com)[Corresponding Author: viswas779@gmail.com](3Corresponding Author: viswas779@gmail.com)  \nReceived: 01-07-2023 Revised: 16-07-2023 Accepted: 30-07-2023  \nABSTRACT  \nDue to significant technology advancements, people's needs have expanded. As a result, there have been more requests for loan approval in the banking sector. A few qualities, taken for consideration, when choosing a candidate for loan approval in order to, determine loan's status. Banks face a major challenge; when it, comes to assessing loan applications and lowering the risks associated with potential borrower defaults. Since they must thoroughly evaluate each borrower's eligibility for a loan, banks find this process to be particularly challenging. This research proposes combining machine learning (ML) models and ensemble learning approaches to find the probability of accepting individual loan requests. This tactic can increase the accuracy with which qualified candidates are selected from a pool of applicants. As a result, this method can be used to address the problems with loan approval processes outlined above. Both the loan applicants and the bank employees profit from the strategy's dramatic reduction in sanctioning time. Because of the banking industry's expansion, more people were applying to loans at banks. In order to predict the accuracy of loan approval status for applied person, we used four different algorithms namely Random Forest, Naive Bayes, Decision Tree, and KNN. By using these, we obtained better accuracy of 83.73% with Naïve Bayes algorithm as best one.  \nKeywords-- Safe Customers, Bank Loans, Trained Dataset, Random Forests, KNN, Decision Tree, Naive Bayes  \nI. INTRODUCTION  \nMany banks' primary line of business is loan distribution. Loans given to consumers account for the majority of a bank's revenue. Interest is charged by these banks on loans given to customers. Banks' handled. It merely has the values x and y as independent and dependent variables. Data primary goal is to invest their funds in dependable clients. Many banks have been  \nprocessing loans so far following a backward process of vetting and verification. However, as of right now, no bank can guarantee whether the customer who is selected for a loan application is secure or not. So, in order, to avoid this circumstance, we implemented the Loan Prediction System Using Python, a system for the approval of bank loans. The Loan Prediction System is apiece of software that determines if or not the specific customer is qualified to receive a loan. This technique examines number of variables, including the customer's marital status, income, spending, and other elements. For wide numbers of trained data set clients, this method/technique is used. These elements are, taken to consideration when creating the necessary model. In order for obtaining the desired outcome, this model is applied for the test data set. The result will be presented as either yes or no. If the answer is yes, then the customer is capable of repaying the loan; if the answer is no, then the consumer is not capable of repaying the loan. We can grant loans to clients based on these criteria. Machine learning is the study of how the systems of computers are used and developed to learn and adapt without explicit ins","cbCaijIve91nV7qg","https://ap.wps.com/l/cbCaijIve91nV7qg","pdf",1489907,1,13,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Problem background\n## Proposed loan prediction system\n## Machine learning overview","[{\"question\":\"What problem does the research address in banking?\",\"answer\":\"It addresses the challenge banks face when assessing loan applications and reducing risks from potential borrower defaults.\"},{\"question\":\"Which machine learning algorithms are used for loan approval prediction?\",\"answer\":\"The study evaluates Random Forest, Naive Bayes, Decision Tree, and KNN to predict loan approval status.\"},{\"question\":\"What accuracy result is reported and which algorithm performs best?\",\"answer\":\"The best reported performance is Naive Bayes with an accuracy of 83.73%.\"}]","Prediction of Loan Approval in Banks using Machine Learning Approach - Research Summary | PDF",1785901099,33,{"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},"prediction-of-loan-approval-in-banks-using-machine-learning-approach-research-summary","",{"@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/prediction-of-loan-approval-in-banks-using-machine-learning-approach-research-summary/125767/",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","2026-08-05",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 problem does the research address in banking?","Question",{"text":75,"@type":76},"It addresses the challenge banks face when assessing loan applications and reducing risks from potential borrower defaults.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used for loan approval prediction?",{"text":80,"@type":76},"The study evaluates Random Forest, Naive Bayes, Decision Tree, and KNN to predict loan approval status.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy result is reported and which algorithm performs best?",{"text":84,"@type":76},"The best reported performance is Naive Bayes with an accuracy of 83.73%.","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"]