
In recent years, a significant percentage of the population has engaged in the trade and use of cryptocurrencies, demonstrating the widespread interest in the field. The usefulness of cryptocurrencies as an option to fiat currency is one of the most pressing issues that arise. The fiat money system is primarily reliant on commercial banks, which require bank accounts to process individual payments. The legality of cryptocurrencies has been susceptible to subjective interpretation, and this research presents a new objective AI methodology for determining whether currencies comply with Sharia. The framework consists of an unsupervised BIRCH clustering method that allows for the grouping of volatilities and logarithmic returns based on a variety of periodic data. The approach gave a solid rationale for deciding automatically which cryptocurrency may not comply with Sharia law. The results indicate that, over longer time intervals, the volatility of various cryptocurrencies vary substantially. This permits the proper distinction between cryptocurrencies that comply with Sharia and those that do not. The methodology proposes an automated and data-driven method to objectively establish the Compliance with sharia of cryptocurrencies, enabling users to readily determine whether it is permitted to use such cryptocurrencies.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
