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Because SMEs are crucial to economic growth and are frequent recipients of lending, evaluating their creditworthiness remains complex and resource-intensive. Building on the growing use of AI in finance for credit risk modeling, the study focuses on three SME groups in the USA that received support during the Covid-19 pandemic through the Payroll Protection Program (PPP).",{"@graph":69,"@context":122},[70,84,105],{"@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/default-probability-prediction-by-explainable-machine-learning-for-small-and-medium-sized-enterprises/290638/",{"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/default-probability-prediction-by-explainable-machine-learning-for-small-and-medium-sized-enterprises/290638.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-20","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address?","Question",{"text":112,"@type":113},"The paper targets credit risk assessment for small and medium-sized enterprises by predicting each SME’s default probability.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which data source is used for the analysis?",{"text":117,"@type":113},"It examines three SME groups in the USA that received support during the Covid-19 pandemic via the Payroll Protection Program (PPP).",{"name":119,"@type":110,"acceptedAnswer":120},"How does the study ensure interpretability of the machine learning results?",{"text":121,"@type":113},"It combines machine learning models with explainable AI methods, including LIME analytics and SHAP analytics using XGBoost and Logistic Regression as reference models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290638,1789642251,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","Default Probability Prediction by Explainable Machine Learning for Small and Medium-Sized Enterprises  \nAalborg University Business School Economic and Business Administration  \nMaster of Finance  \n4th Semester  \nDue to: 3rd June 2024  \nSupervisor  \nProfessor Cesario Mateus  \nStudent  \nMehdi Shadpour  \n20221764  \nNumber of pages: 55  \nNumber of stokes: 85330  \nThanks to:  \nThe entire faculty of finance at AAU especially my supervisor Prof. Mateus whose support made this research to be done  \nand  \nJoseph & Patryk for standing alongside, working incessantly and for what we learned and experienced together  \nTable of Contents  \nAbstract ........................................................................................................................... 1  \nIntroduction .....................................................................................................................2  \nLiterature Review ..............................................................................................................4  \nMethodology ....................................................................................................................8  \nMachine Learning ........................................................................................................... 9  \nModels Accuracy ......................................................................................................... 11  \nData ............................................................................................................................... 11  \nPaycheck Protection Program (PPP) .............................................................................. 12  \nResults ........................................................................................................................... 15  \nDataset Overall View .................................................................................................... 15  \nData Analytics ................................................................................................................ 17  \nMachine Learning Algorithms ........................................................................................ 17  \nExtreme Gradient Boosting (XGB) ................................................................................................................... 17  \nK-Nearest Neighbors Algorithm (KNN) ............................................................................................................20  \nLogistic Regression (LR) .................................................................................................................................21  \nDecision Trees ............................................................................................................................................... 23  \nRandom Forest .............................................................................................................................................. 24  \nSupport Vector Machine (SVM) ....................................................................................................................... 25  \nNaïve Bayes ................................................................................................................................................... 26  \nLIME Analytics ............................................................................................................. 27  \nShap analytics by XGBoost as the reference model ......................................................... 34  \nShap analytics by Logistic Regression as the reference model .........................................42  \nActual data investigation ................................................................................................ 47  \nOther findings ................................................................................................................. 52  \nResearch Limitations ..............................................................","cbCaijHOWbMkGf3O","https://ap.wps.com/l/cbCaijHOWbMkGf3O","pdf",3057482,66,"English","# Table of Contents\n## Abstract\n## Introduction\n## Literature Review\n## Methodology\n## Machine Learning\n## Models Accuracy\n## Data\n## Paycheck Protection Program (PPP)\n## Results\n## Dataset Overall View\n## Data Analytics\n## Machine Learning Algorithms\n## Extreme Gradient Boosting (XGB)\n## K-Nearest Neighbors Algorithm (KNN)\n## Logistic Regression (LR)\n## Decision Trees\n## Random Forest\n## Support Vector Machine (SVM)\n## Naïve Bayes\n## LIME Analytics\n## Shap analytics by XGBoost as the reference model\n## Shap analytics by Logistic Regression as the reference model\n## Actual data investigation\n## Other findings\n## Research Limitations\n## Conclusion\n## References\n## Appendix\n## Appendix A\n## Appendix B\n## Appendix C\n## Appendix D\n## Appendix E","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper targets credit risk assessment for small and medium-sized enterprises by predicting each SME’s default probability.\"},{\"question\":\"Which data source is used for the analysis?\",\"answer\":\"It examines three SME groups in the USA that received support during the Covid-19 pandemic via the Payroll Protection Program (PPP).\"},{\"question\":\"How does the study ensure interpretability of the machine learning results?\",\"answer\":\"It combines machine learning models with explainable AI methods, including LIME analytics and SHAP analytics using XGBoost and Logistic Regression as reference models.\"}]","Default Probability Prediction by Explainable Machine Learning for Small and Medium-Sized Enterprises | PDF",166]