International journal of engineering science and management (IJESM)
Machine Learning-Based Analysis of Cryptocurrency Market Financial Risk Management
| © 2024 by IJESM Journal |
| Volume-24 Issue-1 October |
| Year of Publication : 2024 |
| Author :Indrabayu, Taslinda, Rizka Irianty, Sitti Wetenriajeng Sidehabi |
| DOI : XX.XXXXX/XXXXXXXX/IJESM-XXXXXXXXX |
Abstract
Cryptocurrency has emerged as a significant player in the global financial landscape. Its evolving nature has led to various challenges that influence risk management practices. The rise of digital currencies has introduced a high level of risk in the financial sector, particularly concerning tax avoidance. Financial institutions, such as banks and regulatory bodies, are increasingly taking on the roles of risk assessors, bank managers, and guardians of compliance in the context of digital currency transactions, especially when it comes to identifying illicit funds. This study utilizes the Cryptocurrency System to examine the Hierarchical Risk Assessment and employ Machine Learning techniques. It aims to provide insights into the risks associated with digital currencies, including their likelihood and potential financial impact. Digital currency transactions are known for their elevated risk, primarily due to the possibility of unauthorized access to private keys. Professional cryptocurrency traders, possessing a higher level of expertise, tend to encounter fewer risks in comparison to those with limited knowledge in this field. When looking to maximize the effectiveness of risk management techniques, the Hierarchical Risk Assessment method has proven to be the most fruitful. The efficacy of the model is highlighted in the “Results” section in realigning the areas of strength across various segments and its utilization in assessing covariance windows.