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Limited transparency and interpretability, however, challenge trustworthiness and perceived fairness. This study applies the Shapley Additive Explanations (SHAP) approach to assess explainability in credit scoring models. Using the Lending Club dataset, it trains and evaluates Random Forest, XGBoost, and CatBoost, computes SHAP values, and identifies influential features.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/analysing-the-explainability-of-credit-scoring-machine-learning-models-using-shapley-additive-explanations-approach/291061/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/analysing-the-explainability-of-credit-scoring-machine-learning-models-using-shapley-additive-explanations-approach/291061.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address in credit scoring machine learning models?","Question",{"text":113,"@type":114},"The study addresses the lack of transparency and interpretability in credit scoring models, which can raise concerns about trustworthiness and fairness.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which dataset and models are used to perform the analysis?",{"text":118,"@type":114},"The Lending Club dataset is used, and the study trains and evaluates Random Forest, XGBoost, and CatBoost models.",{"name":120,"@type":111,"acceptedAnswer":121},"How does SHAP contribute to understanding model behavior?",{"text":122,"@type":114},"SHAP values quantify the importance of input features, enabling interpretability and identifying which factors significantly influence credit scoring predictions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291061,1789643348,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","ANALYSING THE EXPLAINABILITY OF CREDIT SCORING MACHINE LEARNING MODELS USING SHAPLEY ADDITIVE EXPLANATIONS APPROACH  \nRamakgahlele Merriam Thoka  \nA research report submitted in partial fulfillment for the degree of  \nMasters in eScience  \nin the  \nDepartment of Computer Science  \nFaculty of Science & Agriculture  \nUniversity of Limpopo  \nSupervisor: Prof TI Modipa  \nMay, 2025  \nDEDICATION  \nI dedicate this dissertation to my devoted family, whose unfailing encouragement and support have served as my beacon of hope along this academic journey. Their belief in my abilities and the compromises made along the way have been essential to my success. I would also like to extend my heartfelt appreciation to my friends who provided a stimulating and collaborative environment, fostering intellectual discussions and shared learning experiences. I am indebted to the research participants who generously volunteered their time and shared their knowledge, without whom this study would not have been possible. Lastly, I want to dedicate this work to everyone who aspires to truth, knowledge, and constructive change. May our collective efforts contribute to a better understanding and advancement in our respective fields. This dissertation is a testament to the unwavering support, love, and inspiration from all those mentioned and the countless others who have left an indelible mark on my academic and personal journey.  \nDECLARATION  \nI Ramakgahlele Merriam Thoka declare that Analysing the Explainability of Credit Scoring Machine Learning Models Using Shapley Additive Explanations Approach is my own original work and all information extracted from other sources is acknowledged as such by means of complete references. I further affirm that I have not submitted this work to any other university for any other degree or examination.  \nSignature: Date: 30/07/2025  \nAcknowledgements  \nIn order to complete this dissertation, I would like to sincerely thank everyone who helped and encouraged me.  \nAbove all, I want to express my sincere gratitude to Dr. TI Modipa, my supervisor, for his leadership, knowledge, and steadfast support during this study process. His invaluable advice, kind criticism, and support have greatly influenced the development of my work. I extend my sincere appreciation to the members of The Speech Technology Research Group in the Department of Computer Science committee for their valuable input, feedback, and expertise. Their thoughtful suggestions and helpful critiques have substantially improved the caliber of this study.  \nI am thankful to the participants for giving freely of their time and expertise, enabling me to collect the necessary data for this study. Their contributions have been fundamental to the findings and conclusions presented.  \nAbstract  \nIn recent years, machine learning models have gained popularity in credit scoring applications due to their ability to handle large volumes of data and capture complex patterns. However, the lack of transparency and interpretability in these models raises concerns regarding their trustworthiness and fairness. This study aims to address this matter by employing the Shapley Additive Explanations (SHAP) approach to analyse the explainability of credit scoring machine learning models. The lending club dataset, a comprehensive collection of loan applications and associated attributes, is utilized for this analysis. The methodology involves training and evaluating various credit scoring models, including Random Forest, XGBoost, and CatBoost, and generating SHAP values to quantify the importance of input features in the prediction process. The results reveal valuable insights into the factors influencing credit scoring decisions and provide a holistic understanding of the models’ behaviour. By utilizing SHAP explanations, we gain interpretability and can identify features that significantly impact the credit scoring outcomes. This knowledge can help stakeholders, including lenders and","cbCaitidsKShp1HZ","https://ap.wps.com/l/cbCaitidsKShp1HZ","pdf",1614680,73,"English","# Abstract\n# 1 Introduction\n## 1.1 Problem Statement\n## 1.2 Motivation\n## 1.3 Aim and Objectives\n## 1.4 Proposed Solution\n## 1.5 Dissertation Structure\n# 2 Literature Review\n## 2.1 Introduction\n## 2.2 Credit Scoring\n## 2.3 Machine learning algorithms used in credit scoring\n## 2.4 Shapley Additive Explanations\n## 2.5 Tree-based Credit Scoring Models\n## 2.6 Explainable AI Approaches\n## 2.7 Conclusion\n# 3 Methodology\n## 3.1 Introduction\n## 3.2 Dataset and Preprocessing\n## 3.2.1 Data distribution and feature analysis","[{\"question\":\"What problem does the study address in credit scoring machine learning models?\",\"answer\":\"The study addresses the lack of transparency and interpretability in credit scoring models, which can raise concerns about trustworthiness and fairness.\"},{\"question\":\"Which dataset and models are used to perform the analysis?\",\"answer\":\"The Lending Club dataset is used, and the study trains and evaluates Random Forest, XGBoost, and CatBoost models.\"},{\"question\":\"How does SHAP contribute to understanding model behavior?\",\"answer\":\"SHAP values quantify the importance of input features, enabling interpretability and identifying which factors significantly influence credit scoring predictions.\"}]","ANALYSING THE EXPLAINABILITY OF CREDIT SCORING MACHINE LEARNING MODELS USING SHAPLEY ADDITIVE EXPLANATIONS APPROACH | PDF",184]