The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework
In order to identify the factors influencing the establishment of the CentralBank 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 CentralBank Digital Cur...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42139/ http://umpir.ump.edu.my/id/eprint/42139/1/The%20Determinant%20Factors%20for%20the%20Issuance%20of%20Central%20Bank%20Digital%20Currency%20%28CBDC%29%20in%20Malaysia%20using%20Machine%20Learning%20Framework.pdf |
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| author | Normi Sham Awang, Abu Bakar Norzariyah, Yahya Norbik Bashah, Idris Engku Rabiah Adawiah, Engku Ali Jasni, Mohamad Zain Erni Eliana, Khairuddin Ahmad Firdaus, Zainal Abidin Murtaj, Sheikh Mohammad Tahsin Siti Sarah, Maidin |
| author_facet | Normi Sham Awang, Abu Bakar Norzariyah, Yahya Norbik Bashah, Idris Engku Rabiah Adawiah, Engku Ali Jasni, Mohamad Zain Erni Eliana, Khairuddin Ahmad Firdaus, Zainal Abidin Murtaj, Sheikh Mohammad Tahsin Siti Sarah, Maidin |
| author_sort | Normi Sham Awang, Abu Bakar |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | In order to identify the factors influencing the establishment of the CentralBank 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 CentralBank 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 cross-validation process to ensure robustness. The accuracy achieved throughthe 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. |
| first_indexed | 2025-11-15T03:46:17Z |
| format | Article |
| id | ump-42139 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:46:17Z |
| publishDate | 2024 |
| publisher | Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-421392024-08-02T01:18:21Z http://umpir.ump.edu.my/id/eprint/42139/ The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework Normi Sham Awang, Abu Bakar Norzariyah, Yahya Norbik Bashah, Idris Engku Rabiah Adawiah, Engku Ali Jasni, Mohamad Zain Erni Eliana, Khairuddin Ahmad Firdaus, Zainal Abidin Murtaj, Sheikh Mohammad Tahsin Siti Sarah, Maidin QA75 Electronic computers. Computer science In order to identify the factors influencing the establishment of the CentralBank 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 CentralBank 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 cross-validation process to ensure robustness. The accuracy achieved throughthe 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. Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia 2024 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/42139/1/The%20Determinant%20Factors%20for%20the%20Issuance%20of%20Central%20Bank%20Digital%20Currency%20%28CBDC%29%20in%20Malaysia%20using%20Machine%20Learning%20Framework.pdf Normi Sham Awang, Abu Bakar and Norzariyah, Yahya and Norbik Bashah, Idris and Engku Rabiah Adawiah, Engku Ali and Jasni, Mohamad Zain and Erni Eliana, Khairuddin and Ahmad Firdaus, Zainal Abidin and Murtaj, Sheikh Mohammad Tahsin and Siti Sarah, Maidin (2024) The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework. Journal of Applied Data Sciences, 5 (2). pp. 808-821. ISSN 2723-6471. (Published) https://bright-journal.org/Journal/index.php/JADS/article/view/176/193 |
| spellingShingle | QA75 Electronic computers. Computer science Normi Sham Awang, Abu Bakar Norzariyah, Yahya Norbik Bashah, Idris Engku Rabiah Adawiah, Engku Ali Jasni, Mohamad Zain Erni Eliana, Khairuddin Ahmad Firdaus, Zainal Abidin Murtaj, Sheikh Mohammad Tahsin Siti Sarah, Maidin The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework |
| title | The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework |
| title_full | The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework |
| title_fullStr | The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework |
| title_full_unstemmed | The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework |
| title_short | The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework |
| title_sort | determinant factors for the issuance of central bank digital currency (cbdc) in malaysia using machine learning framework |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/42139/ http://umpir.ump.edu.my/id/eprint/42139/ http://umpir.ump.edu.my/id/eprint/42139/1/The%20Determinant%20Factors%20for%20the%20Issuance%20of%20Central%20Bank%20Digital%20Currency%20%28CBDC%29%20in%20Malaysia%20using%20Machine%20Learning%20Framework.pdf |