[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122057-en":3,"doc-seo-122057-105":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},122057,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework","This study identifies determinant factors for establishing Central Bank Digital Currency (CBDC) in Malaysia through machine-learning methods. The Central Bank Digital Currency Project Index (CBDCPI) serves as the target variable, while Random Forest and XGBoost models are applied to extract determining variables via feature-importance scoring. Models are trained and validated with cross-validation for robustness, achieving 83% accuracy with Random Forest and 80% with XGBoost. Key factors include circulation of cash, cryptocurrency prevalence, effects on international trade, search interest, financial development, innovation value, and trade openness, supporting a proposed implementation framework for Malaysia.","The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework  \nNormi Sham Awang Abu Bakar1,*, Norzariyah Yahya2 , Norbik Bashah Idris3 , Engku Rabiah Adawiah Engku Ali4  , Jasni Mohamad Zain5, Erni Eliana Khairuddin6, Ahmad Firdaus Zainal Abidin7, Sheikh Mohammad  \nTahsin Murtaj8, Siti Sarah Maidin9  \n1,2,3,8Department of Computer Science, International Islamic University Malaysia, Gombak, 53100 Kuala Lumpur, Malaysia  \n4Institute of Islamic Banking and Finance, International Islamic University Malaysia, Gombak, 53100 Kuala Lumpur, Malaysia  \n5Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia  \n6School of Information Science, Universiti Teknologi MARA (UiTM) 40000 Shah Alam, Selangor, Malaysia  \n7Department of Cyber Security, Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), 26600 Pekan, Pahang, Malaysia  \n9Faculty of Data Science and Information Technology, INTI International University Malaysia, Nilai,71800, Malaysia  \n(Received: February 9, 2024; Revised: March 17, 2024; Accepted: April 15, 2024; Available online: May 31, 2024)  \nAbstract  \nIn order to identify the factors influencing the establishment of the Central Bank Digital Currency (CBDC) in Malaysia, this study leverages the machine-learning technique to determine the most critical factors leading to CBDC issuance in Malaysia. The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable, while two machine learning algorithms, Random Forest and XGBoost were utilized to identify the determining variables. These algorithms were chosen for their ability to handle high-dimensional data and provide feature importance scores, which were crucial in identifying the most significant factors. The models were trained and validated using a rigorous crossvalidation process to ensure robustness. The accuracy achieved through the Random Forest was 83%, and subsequently, 80% in XGBoost. This study explored a new research frontier by creating two machine-learning models that treated retail and wholesale CBDCPI as target variables. The data used in the process are gathered from various official sources such as the Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank. The Circulation of Cash, Prevalence of Cryptocurrencies, Effect of CBDC on International Trade, the Search Interest, Financial Development Index, Innovation Value, and Trade Openness are some of the most critical factors determining whether CBDC will be issued in Malaysia. Generally, are identified as important factors determining whether CBDC will be issued in Malaysia. Eventually, the factors identified will be used to develop a framework for the implementation of CBDC in Malaysia.  \nKeywords: Bank Digital Currency, Machine Learning, Random Forest, XGBoost, Framework  \n1. Introduction  \nFundamentally, a CBDC is a digital banknote. It could be used by individuals to pay businesses or other individuals (aretail CBDC) or it could be used by financial institutions or other wholesale market participants to settle trades in financial markets or other transactions (a wholesale CBDC) [1] . To date, it was reported that, there are 21 countries that have run pilot tests on the usage of CBDC in their economy. One of the biggest implementations of CBDC is in China, which currently reaches 260 million people, is being tested in over 200 scenarios, some of which include public transit, stimulus payments and e-commerce [2] . In relation to that, the motivation behind this study is to investigate the determinant factors on the success of the CBDC implementation in other countries and thus, finding the best application framework of CBDC in Malaysia.  \nOvertime, a variety of innovative payment methods have evolved to meet societal demands. The development of means of payment led to the creation of banknot","cbCaidMTp7vU9pdp","https://ap.wps.com/l/cbCaidMTp7vU9pdp","pdf",723826,1,14,"English","en",105,"# Introduction\n## Overview of CBDC and motivation for the study\n# Methodology\n## Target variable and machine-learning framework\n## Algorithms and feature-importance extraction\n## Model training and cross-validation\n# Results and Determinant Factors\n## Prediction accuracy and key influencing variables\n## Identified factors for CBDC issuance\n# Implications\n## Toward a CBDC implementation framework for Malaysia","[{\"question\":\"What is the main goal of this study on CBDC in Malaysia?\",\"answer\":\"The study aims to identify the determinant factors influencing the establishment of CBDC in Malaysia and to support an implementation framework.\"},{\"question\":\"Which machine-learning algorithms are used, and how are they evaluated?\",\"answer\":\"Random Forest and XGBoost are used, and both are validated with a rigorous cross-validation process to ensure model robustness.\"},{\"question\":\"What factors are identified as most critical for CBDC issuance in Malaysia?\",\"answer\":\"The study highlights circulation of cash, cryptocurrency prevalence, CBDC effects on international trade, search interest, financial development index, innovation value, and trade openness as key determinants.\"}]","The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework | 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