[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117307-en":3,"doc-seo-117307-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117307,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Essays in Machine Learning Applications in Credit Risk - Thesis","This dissertation investigates applications of machine learning models in credit risk, developing alternative approaches within the credit risk modeling pipeline to meet standards and regulations. Three papers analyze distinct dimensions of credit risk. The first paper studies algorithmic credit analysis and the use of discriminatory variables under machine learning fairness principles. The second paper estimates lifetime probability of default using survival analysis and ensemble methods across time maturity. The third paper evaluates credit risk via competing risks survival models using subdistribution hazards.","Cayan Atreio Portela Bárcena Saavedra  \nEssays in Machine Learning Applications in  \nCredit Risk  \nBrasília-DF  \nMay, 2023  \nCayan Atreio Portela Bárcena Saavedra  \nEssays in Machine Learning Applications in Credit Risk/ Cayan Atreio Portela Bárcena Saavedra.– Brasília-DF, May, 2023-  \n84 p. : il. (some colored.) ; 30 cm.  \nAdvisor: Dr. Herbert Kimura  \nThesis (Ph.D.)– University of Brasilia  \nSchool of Economics, Business and Accounting (FACE)  \nGraduate Program in Management , May, 2023 .  \n1. Machine Learning 2 . Survival Analysis 3 . Competing Risk 4 . Machine Learning Fairness I. Advisor: Dr. Herbert Kimura II. University of Brasilia III. School of Economics, Business and Accounting (FACE) IV. Essays in Machine Learning Applications in Credit Risk  \nCDU 02:141:005.7  \nCayan Atreio Portela Bárcena Saavedra  \nEssays in Machine Learning Applications in Credit Risk  \nThesis submitted to the Graduate Program in Business Administration at University of Brasilia as partial fulfillment of the requirements for attainment [of Ph.D. degree](of Ph.D. degree) in Business Administration, with major in Finance and Quantitative Methods.  \nThe examining committee, as identified below, approves this dissertation:  \n\n| Dr. Herbert Kimura\u003Cbr>Advisor |\n| --- |\n| Dra. Juliana Betini Fachini Gomes\u003Cbr>University of Brasília |\n| Dr. Fabiano Guasti Lima\u003Cbr>University of São Paulo |\n\nDr. Leonardo Fernando Cruz Basso  \nMackenzie Presbyterian University  \nBrasília-DF May, 2023  \nTo my mom, the most intelligent person I’ve ever known.  \nAbstract  \nThis dissertation explores applications of machine learning models in credit risk. Statistical and machine learning techniques are investigated, seeking to develop alternative methods in the credit risk modeling pipeline, aiming at comply with standards and regulations. We develop three papers in this dissertation, analyzing different aspects of credit risk using machine learning. In the first paper, Algorithmic Credit Analysis and the use of Discriminatory Variables, concerning machine learning fairness and the use of sensitive variables. In the second paper, Lifetime Probability of Default with Survival Analysis and Ensemble Methods, application of survival analysis models for the entire time maturity of a credit operation. Finally, in the third paper, Credit Risk Assessment with Machine Learning and Competing Risk Survival Analysis Models, an adaptation in competing risks subdistribution hazards. In the three applications, different machine learning models are explored, and the results are discussed, aiming to contribute to the credit risk literature.  \nKeywords: Machine Learning; Survival Analysis; Competing Risk; Machine Learning Fairness.  \nList of Figures  \nFigure 1 – Range of the monthly interest from 2022 from different loan types (Brazilian Central Bank) ............................. 18  \nFigure 2 – ROC Curve and AUC for balanced bagging classifiers trained on a random split dataset and removing the following features: (a) None (b) Gender (c) Gender and Education (d) Gender, Education and Age ... 27  \nFigure 3 – ROC Curve and AUC for smote classifiers trained on random split dataset and removing the following features: (a) None (b) Gender (c) Gender and Education (d) Gender, Education and Age ......... 28  \nFigure 4 – ROC Curve and AUC for balanced bagging classifiers trained conside  \nring a time-window dependency and removing the following features:  \n(a) None (b) Gender (c) Gender and Education (d) Gender, Education and Age ................................... 29  \nFigure 5 – ROC Curve and AUC for smote classifiers trained considering a timewindow dependency and removing the following features: (a) None (b) Gender (c) Gender and Education (d) Gender, Education and Age ... 30  \nFigure 6 – Random split: (a) Recall and (b) Precision results............ 31  \nFigure 7 – Time-dependency split: (a) Recall and (b) Precision results....... 32  \nFigure 8 – Time-Debt to income distribution per time maturity","cbCaiepVUrqNMHCS","https://ap.wps.com/l/cbCaiepVUrqNMHCS","pdf",7082790,1,85,"English","en",105,"# Abstract\n## Machine learning for credit risk modeling\n## Paper 1: Algorithmic credit analysis and fairness\n## Paper 2: Lifetime probability of default with survival analysis\n## Paper 3: Competing risks survival analysis models","[{\"question\":\"What is the main goal of the dissertation?\",\"answer\":\"To explore machine learning models in 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hazards.\"}]",1785675098,214,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"essays-in-machine-learning-applications-in-credit-risk-thesis","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/essays-in-machine-learning-applications-in-credit-risk-thesis/117307/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What 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