Abstract
The financial risk of investing in Bitcoin is increasing, and everyone participating in the transaction is aware of it. The rise and fall of bitcoin's value is difficult to predict, and the system is fraught with uncertainty. As a result, this study proposed to use the "Deep learning" technique for predicting fi-nancial risk associated with bitcoin investment, that is linked to its "weighted price" on the bitcoin market's volatility. The dataset used included Bitcoin historical data, which was acquired "at one-minute intervals" from selected exchanges of January 2012 through December 2020. The deep learning lin-ear-SVM-based technique was used to obtain an advantage in handling the high-dimensional challenges related with bitcoin-based transaction transac-tions large data volume. Four variables ("High", "Low", "Close", and "Volume (BTC)".) are conceptualized to predict weighted price, in order to indicate if there is a propensity of financial risk over the effect of their interaction. The results of the experimental investigation show that the financial risk associated with bitcoin investing is accurately predicted. This has helped to discover engagements and disengagements with doubts linked with bitcoin investment transactions, resulting in increased confidence and trust in the system as well as the elimination of financial risk. Our model had a significantly greater prediction accuracy, demonstrating the utility of deep learning systems in detecting financial problems related to digital currency.