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Artificial Intelligence (AI) and Machine Learning are on their way to taking different industries and revolutionizing them with substantial technological advances.   According to Forbes contributor, Sameer Maskey, AI will save the banking industry more than $1 trillion by the year 2030. As time progresses, the technology is only becoming more and more prevalent in all sectors of the economy, but it is having a very notable impact within the finance sector. It is currently being used for things such as fraud detection, automation, and insurance adjustments, in order to save corporations massive amounts of time and manpower. Furthermore, the private sector is beginning to see artificial intelligence used for insurance adjustments, and entrances and exits into the stock market. The technology is also beginning to be used for human-like chatbots which create a streamlined customer support experience that is effective and efficient at satisfying customer concerns.

Within common parlance, AI is often confused with a computer script. While these two sorts of programs do share many superficial similarities, they are functionally quite different. While scripts generally operate according to a set of rules defined by the programmer, AI differs in that it uses programmatic neurons which are weighted according to numerical input from the user. When writing a script, programmers need to keep all possible use cases in mind throughout their development process. This generally limits the sorts of problems that the script is able to solve. In contrast to this, all AI is built with the same general principle. Afterward, it is finely tuned according to the immediate needs of the user. The end result of this is a very reusable program that is able to outclass the vast majority of scripts.

 
Although the technology is having an extremely notable effect on the private sector, it still has its limitations. The strength of AI lies in its ability to process a massive amount of data in a way that no human could ever match. However, it still heavily relies on human input and is not generally trusted to make the most predominant and impactful business decisions. Furthermore, in order for most AI to excel, it needs to have access to high-quality datasets that are often expensive and difficult to come across. This shouldn’t shy anybody away from looking into real-time AI high-frequency trading solutions. The area is very well suited towards AI, as it excels at making calculated split-second decisions.