[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128645-en":3,"doc-seo-128645-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128645,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Classifying Corporates Default and Non-Default Using Machine Learning Artificial Neural Network - Multilayer Perceptron","This technical report presents a machine learning approach for classifying corporate default versus non-default using an Artificial Neural Network, specifically a Multilayer Perceptron. It defines the research problem and objectives, reviews key concepts including corporate default prediction and alternative modeling such as the KMV-Merton model, and describes a full workflow from data collection and preprocessing to parameter selection and network structure design. The study evaluates model behavior through case processing, network information, classification outcomes, and parameter estimates.","UNIVERSITI TEKNOLOGI MARA  \nTECHNICAL REPORT  \nCLASSIFYING CORPORATES DEFAULT AND NON-DEFAULT USING MACHINE LEARNING ARTIFICIAL NEURAL NETWORK: MULTILAYER PERCEPTRON  \nNUR INSYIRAH BINTI MOHAMAD RADZI –(2021107095)  \nMURNI SALINA BINTI ROSIDI –(2020853918) NUR ASYURA IZZATI BINTI ZAILAND –(2020489772)  \n(P26M23)  \nReport submitted in partial fulfillment of the requirement  \nfor the degree of  \nBachelor of Science (Hons.) (Mathematics) College of Computing, Informatics and Mathematics  \nAUGUST 2023  \nACKNOWLEDGEMENTS  \nIN THE NAME OF ALLAH, THE MOST GRACIOUS, THE MOST MERCIFUL  \nThe success and outcome of this final year project required a lot of guidance and assistance from many people.  \nAll praises to Allah S.W.T for giving us His blessing and the strength for us to complete this final year project titled Corporate Default Prediction using Machine Learning Artificial Neural Network (Multilayer Perceptron) successfully. We are so grateful for all the opportunities that have been showered to finish our writing.  \nFirst and foremost, we would like to sincerely express our deep appreciation and indebtedness to our beloved supervisor, Madam Norliza Binti Muhamad Yusof for her endless support, guidance, understanding, patience and most importantly her supervision and positive encouragement and warm spirit to finish this final year project during the project duration. It has been a great pleasure and honor to have Madam Norliza as our supervisor. Also, we would like to thank our lecturers in semester 5 and semester 6, Dr Liyana and Hjh Noraimi Azlin that involved in this project and helped us with their suggestions to make our project better.  \nNext, we would like to express our deepest gratitude and thanks towards our group members. It is such an honor to have an effective team member in completing this final year project. This project's completion could not have been possible without the cooperation and participation of our group members.  \nFinally, we would like to thank our family, friends, and colleagues for always being with us and supporting us in every situation. The support, encouragement and positive spirit given by friends is so meaningful and gives us enthusiasm to finish our final year project.  \nTABLE OF CONTENT  \nACKNOWLEDGEMENTS ............................................................................................. ii  \nLIST OF TABLES ........................................................................................................... iv  \nLIST OF FIGURES ......................................................................................................... iv  \nABSTRACT....................................................................................................................... v  \nCHAPTER 1 INTRODUCTION .................................................................................... 1  \n1.1 Problem Statement ................................................................................................ 2  \n1.2 Objectives ............................................................................................................. 2  \n1.3 Significance of the Study ...................................................................................... 3  \n1.4 Scope and Limitation of Study.............................................................................. 3  \n1.5 Definition of Terms............................................................................................... 4  \nCHAPTER 2 BACKGROUND THEORY AND LITERATURE REVIEW.............. 6  \n2.0 Introduction .......................................................................................................... 6  \n2.1 Corporate Default.................................................................................................. 6  \n2.2 Artificial Neural Network (ANN) ......................................................................... 6  \n2.3 Default Prediction using Artificial Neural Network (ANN) ...............","cbCaiqitmoQmVFk8","https://ap.wps.com/l/cbCaiqitmoQmVFk8","pdf",161880,1,5,"English","en",105,"# Acknowledgements\n# Table of Contents\n# Chapter 1 Introduction\n## Problem Statement\n## Objectives\n## Significance of the Study\n## Scope and Limitation of Study\n## Definition of Terms\n# Chapter 2 Background Theory and Literature Review\n## Corporate Default\n## Artificial Neural Network (ANN)\n## Default Prediction using Artificial Neural Network (ANN)\n## Default Prediction using KMV-Merton model\n# Chapter 3 Methodology and Implementation\n## Collecting Data\n## Implementing the Data\n## Analysing the Data\n## Sample Data\n## Set Parameter\n## Classify the variables\n## Select network structure for the output\n# Chapter 4 Results and Discussion\n## Case Processing Summary\n## Network Information\n## Model of the ANN\n## Model Summary of the output\n## Parameter Estimates\n## Classification","[{\"question\":\"What is the main goal of this project?\",\"answer\":\"The project aims to classify corporates into default and non-default categories using a machine learning model based on an Artificial Neural Network (Multilayer Perceptron).\"},{\"question\":\"Which concepts and related models are reviewed in the report?\",\"answer\":\"The background includes corporate default, Artificial Neural Networks, default prediction using ANN, and default prediction using the KMV-Merton model.\"},{\"question\":\"How is the model implemented and evaluated?\",\"answer\":\"The workflow covers collecting and implementing data, analyzing and preparing sample data, setting parameters, classifying variables, selecting the network structure, and evaluating results through case processing, network information, classification, and parameter estimates.\"}]","Classifying Corporates Default and Non-Default Using Machine Learning Artificial Neural Network - Multilayer Perceptron | PDF",1786002283,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"classifying-corporates-default-and-non-default-using-machine-learning-artificial-neural-network-multilayer-perceptron","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/classifying-corporates-default-and-non-default-using-machine-learning-artificial-neural-network-multilayer-perceptron/128645/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this project?","Question",{"text":76,"@type":77},"The project aims to classify corporates into default and non-default categories using a machine learning model based on an Artificial Neural Network (Multilayer Perceptron).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which concepts and related models are reviewed in the report?",{"text":81,"@type":77},"The background includes corporate default, Artificial Neural Networks, default prediction using ANN, and default prediction using the KMV-Merton model.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the model implemented and evaluated?",{"text":85,"@type":77},"The workflow covers collecting and implementing data, analyzing and preparing sample data, setting parameters, classifying variables, selecting the network structure, and evaluating results through case processing, network information, classification, and parameter estimates.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":21,"slug":138},19,"General","general"]