[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121979-en":3,"doc-seo-121979-105":30,"detail-sidebar-cat-0-en-105":95},{"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},121979,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Predicting Default Probability in Emerging Economies: A Comparative Machine Learning Approach in the Tunisian Banking Sector","Economic policymakers face serious risks when default exposure is ignored, undermining financial system stability and wider macroeconomic resilience. This dissertation applies advanced machine learning to predict loan default probabilities for Tunisian banking entities listed in the Tunisian central bank dataset from 2018 to 2022. Two modeling strategies are compared: continuous-variable-based features and ratio-focused features. Results show strong performance from both approaches, with Random Forest combined with SVM and SMOTE or Tomek Links resampling identified as the most effective. The study supports credit risk assessment in emerging markets.","Master Degree Program in Data Science and Advanced Analytics  \nPREDICTING DEFAULT PROBABILITY IN EMERGING ECONOMIES: A COMPARATIVE MACHINE LEARNING APPROACH IN THE TUNISIAN BANKING SECTOR  \nMohamed Ali FELFEL  \nDissertation  \npresented as partial requirement for obtaining the Master Degree Program in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School  \nInstituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nPREDICTING DEFAULT PROBABILITY IN EMERGING ECONOMIES: A COMPARATIVE MACHINE LEARNING APPROACH IN THE TUNISIAN  \nBANKING SECTOR  \nby  \nMohamed Ali Felfel  \nDissertation presented as partial requirement for obtaining the Master’s degree in Advanced Analytics, with a Specialization in Data Science  \nSupervisor: Afshin Ashofteh  \nFebruary 2024  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledge the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nMohamed Ali Felfel  \nLisbon, 29/02/2024  \nACKNOWLEDGEMENTS  \nTo my academic mentor, Prof. Afshin Ashofteh for providing me the opportunity to engage in this project. I extend my gratitude for his continuous guidance, encouragement, and unwavering commitment throughout the duration of this thesis.  \nFurthermore, I am indebted to my parents whose unwavering efforts have made this journey possible. I cannot thank them enough for their constant belief in me, unconditional love, and sacrifices. A special mention goes to my cherished brother for his consistent encouragement and positive energy.  \nIn addition, my heartfelt thanks go out to my entire family and friends in Tunisia, Portugal, and across the globe. I appreciate the moral support and constant inspiration they provided. Their unwavering belief in me has been a driving force, keeping my spirits high and motivation intact during this process. The shared beautiful moments hold significant value.  \nThis work is dedicated to everyone who played a role in making this incredible experience happen, contributing to shaping the person I am today. Having you all by my side fills me with gratitude, and I sincerely thank you for your unwavering support, love, and care.  \nABSTRACT  \nEconomic policymakers neglecting default risk can have severe consequences for the financial system and broader economic stability. This research addresses this critical challenge by deploying advanced machine learning models for loan default prediction, aiming to predict the default probabilities of companies listed in the Tunisian central bank between 2018 and 2022.  \nTwo distinct approaches were implemented to enhance the predictive accuracy of default probabilities. The first approach utilized a comprehensive set of continuous variables, capturing various financial indicators. The second approach focused on key financial ratios, providing a more nuanced understanding of companies' financial health. By employing these approaches, this research not only aimed to calculate default probabilities but also sought to compare the predictive power of both methodologies.  \nThe results of this study reveal promising findings, indicating excellent performance by both approaches in predicting default rates. Furthermore, the research identified Random Forest models with SVM SMOTE and Tomek Links resampling techniques as the most effective in each respective approach. These findings underscore the importance of leveraging advanced machine learning techniques for credit risk assessment, particularly in emerging economies where data environments may present challenges.  \nThis work contributes significantly to the field by demons","cbCaiob8ZlHPesgr","https://ap.wps.com/l/cbCaiob8ZlHPesgr","pdf",1718034,1,53,"English","en",105,"# Introduction\n# Literature review\n# Methodology\n## Data description and understanding\n## Data preparation\n## Model training and evaluation\n# Results and discussion\n# Conclusion and future work","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It tackles the consequences of neglecting default risk by predicting loan default probabilities using advanced machine learning for Tunisian banking sector entities.\"},{\"question\":\"Which time period and dataset are used?\",\"answer\":\"The study predicts default probabilities for companies in the Tunisian central bank between 2018 and 2022.\"},{\"question\":\"How are the two approaches different?\",\"answer\":\"One approach uses a comprehensive set of continuous variables to capture financial indicators, while the other focuses on key financial ratios to reflect companies' financial health.\"},{\"question\":\"What models and techniques performed best?\",\"answer\":\"Random Forest models using SVM SMOTE and Tomek Links resampling techniques showed the strongest effectiveness within each respective approach, achieving excellent prediction of default rates.\"}]","Predicting Default Probability in Emerging Economies: A Comparative Machine Learning Approach in the Tunisian Banking Sector | 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problem does the dissertation address?","Question",{"text":75,"@type":76},"It tackles the consequences of neglecting default risk by predicting loan default probabilities using advanced machine learning for Tunisian banking sector entities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which time period and dataset are used?",{"text":80,"@type":76},"The study predicts default probabilities for companies in the Tunisian central bank between 2018 and 2022.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the two approaches different?",{"text":84,"@type":76},"One approach uses a comprehensive set of continuous variables to capture financial indicators, while the other focuses on key financial ratios to reflect companies' financial health.",{"name":86,"@type":73,"acceptedAnswer":87},"What models and techniques performed best?",{"text":88,"@type":76},"Random Forest models using SVM SMOTE and Tomek Links resampling techniques showed the strongest effectiveness within each 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