[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124166-en":3,"doc-seo-124166-105":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},124166,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Early Warning Credit Risk Default Prediction using Machine Learning for Small and Medium American Businesses - Master’s Report","Early Warning Credit Risk Default Prediction using Machine Learning for Small and Medium American Businesses analyzes modern machine learning approaches for credit risk in small and medium U.S. firms. The study covers 50 organizations across five sectors—retail, fmcg, transportation, commercial services, and utilities—over 11 years from 2013 to 2023. It scrapes 27 financial indicators and applies grey correlation analysis to rank weighted indicators and determine principal components. Multiple ensemble and correlation-subset model combinations are evaluated, and explainable AI supports sector-wise insight from misclassifications.","Copyright by  \nSoorya Sriram 2024  \n1  \nThe Report Committee for Soorya Sriram  \ncertifies that this is the approved version of the following report:  \nEarly Warning Credit Risk Default Prediction using Machine Learning for Small and Medium American Businesses  \nSUPERVISING COMMITTEE:  \nGuoming Lai, Supervisor  \nJohn Hasenbein, Co-supervisor  \nEarly Warning Credit Risk Default Prediction using Machine Learning for Small and Medium American Businesses  \nby  \nSoorya Sriram  \nReport  \nPresented to the Faculty of the Graduate School of The University of Texas at Austin  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nMaster of Science in Engineering  \nThe University of Texas at Austin December 2024  \nEpigraph  \nWhat starts here changes the world.  \n—University of Texas at Austin  \nAcknowledgments  \nI would like to express my sincerest gratitude to my academic supervisor Dr. Guoming Lai from the Information, Risk and Operations Management Department from the McCombs School of Business. I thank him for giving me the opportunity to work under his guidance and providing me with the flexibility in allowing to pursue the topic and project of my choice with utmost freedom. His constant encouragement was the main driving force for me to complete the project within the time provided. I would like to thank him for his patience and efforts put in to take up doubts.  \nI would like to thank Dr. John Hasenbein, graduate advisor of Operations Research and Industrial Engineering department for his valuable advice throughout my graduate studies and for being the co-supervisor for this report and graduate studies.  \nI would like to thank my undergraduate thesis professor Dr. Kalpana P, from Indian Institute of Information Technology Design and Manufacturing for cultivating an interest in Operations Research.  \nI would like to thank all the professors at UT Austin ORIE specifically Dr. Kutanoglu and Dr. Eric Bickel for their guidance during the ORIE Applied Projects courses enabling me to learn new skills and work with industry stalwarts to pick their brains working on cutting edge problems.  \nLast but not the least, I would like to thank my family, fellow friends and ORIE classmates for their continuous support during my graduate studies.  \nAbstract  \nEarly Warning Credit Risk Default Prediction using Machine Learning for Small and Medium American Businesses  \nSoorya Sriram, MSE  \nThe University of Texas at Austin, 2024  \nSUPERVISORS: Guoming Lai, John Hasenbein  \nThere has been a lot of research and variants in credit risk prediction in the past using traditional operations research and quantitative analytical models. The purpose of this applied research study was to understand industry standard modern machine learning models used for small and medium American businesses. The goal was to analyze 50 organizations belonging to five different sectors namely retail, fmcg, transportation, commercial services and utilities over 11 years from 2013-2023 . Scraping 27 financial indicators from publicly available yet reliable sources, a study was made to identify the financial indicators which has the most impact on the accuracy of the model. Grey correlation analysis was used to find and rank weighted financial indicators which were then used to identify the number of principal components. Evaluating a number of different models, ensemble methods and correlation subsetsto find the best combination of machine learning models, explainable AI techniques were used to perform a sector wise analysis on the misclassified data to derive valuable insights.  \nTable of Contents  \nList of Tables .................................... 9  \nList of Figures ................................... 10  \nChapter 1: Introduction ............................. 11  \n1.1 Background ................................ 11  \n1.2 Motivation ................................. 12  \n1.3 Objectives ................................. 12  \nChapter 2: Literature Review ............","cbCail6Hi1K4Gyai","https://ap.wps.com/l/cbCail6Hi1K4Gyai","pdf",1128335,1,41,"English","en",105,"# List of Tables\n# List of Figures\n# Chapter 1: Introduction\n## Background\n## Motivation\n## Objectives\n# Chapter 2: Literature Review\n## Machine Learning for Credit Risk\n## Ensemble Methods for Credit Risk Performance\n## Importance of Interpretability\n## Optimization Models for Credit Risk\n## Correlation Based Classifier Selection\n# Chapter 3: Keywords and Organizations\n## Financial Indicators in Dataset\n## Training and Test Set\n# Chapter 4: Experiment and Results\n## Data Preprocessing\n## Grey Correlation Analysis\n## Principal Correlation Analysis\n## Model Selection\n## Model Performance\n# Chapter 5: Inferences\n## Sector Wise Analysis\n## Explainability\n# Chapter 6: Conclusion\n## Future Scope\n# Works Cited","[{\"question\":\"What is the main research goal of the study?\",\"answer\":\"To understand industry-standard modern machine learning models for credit risk default prediction in small and medium American businesses and identify approaches that improve accuracy.\"},{\"question\":\"What data and time span are used in the analysis?\",\"answer\":\"The study analyzes 50 organizations across five sectors over 11 years from 2013 to 2023.\"},{\"question\":\"How are financial indicators selected and ranked?\",\"answer\":\"The study scrapes 27 financial indicators, then uses grey correlation analysis to rank weighted indicators and determine the number of principal components for modeling.\"}]","Early Warning Credit Risk Default Prediction using Machine Learning for Small and Medium American Businesses - 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