[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121670-en":3,"doc-seo-121670-105":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},121670,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine Learning Approach for Credit Score Predictions","This paper tackles the growing demand for credit products faced by banking and financial institutions by proposing an adaptive, dynamic heterogeneous ensemble credit model. The approach integrates XGBoost and Support Vector Machine to learn patterns and trends from historical data for future risk assessment. It compares against existing credit scoring models using Accuracy, ROC AUC, Precision, Recall, and F1_Score, while addressing challenges such as class imbalance, verification latency, and concept drift. Results indicate improved evaluation performance with a balance between predictive accuracy and computational cost.","Machine Learning Approach for Credit Score Predictions  \nTsholofelo Mokheleli1, Tinofirei Museba2  \n1,2Department of Applied Information Systems, University of Johannesburg, Johannesburg, South  \nAfrica  \n[Email:](Email:1 217050248@student.uj.ac.za)[1](Email:1 217050248@student.uj.ac.za)[ 217050248@student.uj.ac.za](Email:1 217050248@student.uj.ac.za), [2](2 tmuseba@uj.ac.za)[ tmuseba@uj.ac.za](2 tmuseba@uj.ac.za)  \nAbstract  \nThis paper addresses the problem of managing the significant rise in requests for credit products that banking and financial institutions face. The aim is to propose an adaptive, dynamic heterogeneous ensemble credit model that integrates the XGBoost and Support Vector Machine models to improve the accuracy and reliability of risk assessment credit scoring models. The method employs machine learning techniques to recognise patterns and trends from past data to anticipate future occurrences. The proposed approach is compared with existing credit score models to validate its efficacy using five popular evaluation metrics, Accuracy, ROC AUC, Precision, Recall and F1_Score. The paper highlights credit scoring models’ challenges, such as class imbalance, verification latency and concept drift. The results show that the proposed approach outperforms the existing models regarding the evaluation metrics, achieving a balance between predictive accuracy and computational cost. The conclusion emphasises the significance of the proposed approach for the banking and financial sector in developing robust and reliable credit scoring models to evaluate the creditworthiness of their clients.  \nKeywords: Credit Score, Machine learning, Class Imbalance, SMOTE, Ensemble, XGBoost, SVM  \n1. INTRODUCTION  \nCredit scoring models have emerged as effective and efficient tools for banks and other financial institutions to distinguish, recognise, and discriminate against potential default borrowers and mitigate credit risk. Given such a scenario, a credit scoring model's prediction, recognition, and discriminatory performance are important for financial institutions and banks to generate profits. Financial institutions use a credit score to determine a client’s creditworthiness for a loan. Credit scores are generated by considering personal details such as historical track records on debt responsibilities, profiling, primary place of residence, earnings, job, demographic information, assets like vehicles and real estate, and census data. There has been a swift surge in the number of credit requests that financial institutions receive, and they have to assess the possible hazards associated with granting credit to their clients. The sooner financial institutions can ascertain  \nwhether or not to provide credit to their clients, the more advantageous it is. Credit scores are utilised by lenders, retailers, car dealerships, and real estate agents to appraise whether a client is eligible for a loan, credit card, automobile, or a new residence. Additionally, they determine the interest rate and credit limit that are applicable.  \nCredit scoring is useful for managing credit risk and minimising information asymmetry [1] [2] . Its purpose is to produce a score that can differentiate loan applicants into two categories: those who are creditworthy and likely to repay their loans and those who are risky and unlikely. This score is linked to the anticipated likelihood of default and is transformed into a classification task [3] . The creation of a robust, efficient, and adaptable credit scoring model has a significant impact on the profitability of financial institutions [4] . Every credit risk scoring model must comply with stringent regulations, and any violation may result in significant regulatory costs. Therefore, creating credit scoring models that are adaptable, efficient, and robust in accurately predicting loan defaults is crucial. Before the advent of machine learning, statistical models were used for credit scoring. Nevertheless, s","cbCainRqBD3EK9dP","https://ap.wps.com/l/cbCainRqBD3EK9dP","pdf",598178,1,21,"English","en",105,"# 1. Introduction\n# Abstract\n# Keywords\n# Credit Scoring Models and Machine Learning\n## Challenges in Credit Scoring","[{\"question\":\"What problem does the paper address in credit scoring?\",\"answer\":\"It addresses the need to manage a significant rise in credit-product requests by improving the accuracy and reliability of risk assessment models used by banks and financial institutions.\"},{\"question\":\"Which models are combined in the proposed approach?\",\"answer\":\"The proposed adaptive dynamic heterogeneous ensemble combines XGBoost and Support Vector Machine to better predict credit risk.\"},{\"question\":\"How does the study handle class imbalance?\",\"answer\":\"It uses oversampling to balance unequal data classes, and frames imbalance classification as building models under notable class imbalance conditions so the minority class is not neglected.\"}]","Machine Learning Approach for Credit Score Predictions | PDF",1785806108,53,{"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-approach-for-credit-score-predictions","",{"@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-approach-for-credit-score-predictions/121670/",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","2026-08-04",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},"What problem does the paper address in credit scoring?","Question",{"text":75,"@type":76},"It addresses the need to manage a significant rise in credit-product requests by improving the accuracy and reliability of risk assessment models used by banks and financial institutions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models are combined in the proposed approach?",{"text":80,"@type":76},"The proposed adaptive dynamic heterogeneous ensemble combines XGBoost and Support Vector Machine to better predict credit risk.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study handle class imbalance?",{"text":84,"@type":76},"It uses oversampling to balance unequal data classes, and frames imbalance classification as building models under notable class imbalance conditions so the minority class is not neglected.","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"]