Valuing Bitcoin- Mining, Fundamental Quantity Theory, The Puell Multiple, National Currency Comparison, Stock to Flow (STF), Store of Value (SOV)
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Valuing Bitcoin- Mining, Fundamental Quantity Theory, The Puell Multiple, National Currency Comparison, Stock to Flow (STF), Store of Value (SOV)
In order to efficiently combat fraud, financial institutions are turning to artificial intelligence (AI) and its applications such as machine learning.
In banking, the utilisation of AI has become more of a necessity than a “nice to have.” They are under intense pressure on multiple fronts with the rise of Fintech companies, accelerated digital disruption and widespread technological advances
Financial Services institutions are typically hard ones to break into with innovation. However, the application of computer vision techniques is transforming and solving some longstanding problems amongst financial firms.
AI is heartbeat of most Fintech firms. The ability of AI platforms to solve human problems, reduce costs and enhance efficiency makes it a must-have asset.
Banks incorporating VR and AR have created apps where customers are able to manage their accounts and payments in a virtual setting.
Standing in the year 2020, it is very exciting to anticipate that in which direction this collaboration of AI and banking is heading towards in the next 5 years.
We know that machine learning is making great strides in all major finance institutions increasing employee and customer ease-of-use processes. This will inevitably pave the way for more automated processes to replace manual and repetitive routines and procedures.
The three main issues found have been the performance when scaling of the technology for commercial application, ongoing regulatory uncertainties, and the difficulty of trust when bringing together various competitors.
Finance companies have managed to use artificial intelligence for risk management very efficiently and successfully.
