As many still worry about how robots are rapidly going to replace humans for repetitive and monotonous activities, Wall Street’s key players are already applying Artificial Intelligence for operations that until now would have required the most skilled financial brains available on the market.
Just as the name Machine Learning suggests, robots are now able not only to obey to our commands but also to learn and deeply analyze what they are being trained for, until they master the matter and they manage to self-improve, without the need of any kind of human intervention.
Although we might not like to admit it, we cannot strive for progress by ourselves, as the resources of the human nature are limited- just think about our time, our ability to stay focused and our processing speed.
A good turnaround for it is to collect our energies and organize them to build a set of algorithms that can make up for all our flaws and the characteristics that humans are missing.
Be careful though, as the Machine Learning topic is nowadays often misused and brought out in matters in which it might be not relevant or related at all.
Truth is that, as Artificial Intelligence researcher Eliezer Yudkowsky states,
“By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”
Artificial Intelligence can indeed be applied in the most diverse fields, but we need to clearly have in mind the matter, and see how each industry can benefit differently from this technology.
Due to the great amount of big data that its industry has, the financial sector is particularly fertile for the implementation of Artificial Intelligence and Machine Learning.
Artificial Intelligence and finance, in fact, represent a good match for many different reasons.
Retail banks are already looking into AI for the realization of more advanced chatbots, robo advisors, and compliance tools.
After having conducted a survey among 86 banks, UBS came to draft a prediction according to which, in the next couple of years, Artificial Intelligence could increase banks’ revenues by 3.4% and lower their costs by 3.9%.
Following this path, in the autumn of 2016 UBS SmartWealth was launched, with the purpose of providing real-time financial advice and suggesting personalize investment strategies.
On the same year, Capital One and Bank of America came up with similar products.
The first one launched a particular skill, aimed at helping customers manage their credit card and bank accounts through Alexa, Amazon’s virtual assistant, that, just like Apple’s Siri and Window’s Cortana, replies to — almost — every question we have.
BoA, on its side, introduced Erica as its clients’ new financial assistant, easily reachable through the bank’s mobile application, and able to help with all the possible daily tasks, such as balance checking and money transferring.
Another financial field in which AI can play a key role in fraud detection, that can be accomplished with the analysis of consumers’ behavior, with a heavy focus on suspicious transactions.
The relationship between AI and finance, however, goes far beyond the assistance in daily operations and the automation of certain tasks.
As we anticipated before, the potential of its technology could arrive at the point of overcoming human abilities and accomplishing incredibly difficult tasks.
For the amount of data available and for the complexity of the matter, trading is the financial branch that could be benefitting the most from the intervention of AI algorithms.
Just think about it: the stock market opens when the day starts in Sydney and closes when it ends in New York City.
Basically, there are transactions happening 24 hours per day, 7 days a week, excluding the few hours during which the weekend is happening all over the World.
Every day registers thousands of millions of transactions, and this makes the market extremely active and subject to sudden changes.
Decisions have to be taken rapidly and even the most experienced traders often face difficulties and make fatal errors in their predictions of the trends and in their forecasts.
Human beings might find hard to accept to change a strategy they have pondered so much before.
Machines, on the other hand, do not have that sentimental problem that makes them feel like their “married” to their original idea and they can’t change it.
Machines’ algorithms are programmed with the purpose of making them able to rapidly decide when to buy, sell or hold a stock, with a much better accuracy compared to human beings.
A big limitation to the implementation of AI algorithms for trading is represented by the lack of transparency that this system involves.
Machine Learning developers might not only deny sharing their technical secrets but could also find it hard to explain them, as, the more developed the algorithms are, the harder it is to make them understandable for someone that doesn’t have that much of an AI background.
This could represent a big limitation for the expansion of AI in trading, as, if the process is not fully understood, not everyone would agree to invest a relevant sum of money and give the entire control of it to a robot.
Most importantly, you need to have a user-friendly and clear platform, that explains the algorithms in a way that you can easily understand the conclusion that they came at and the process that guided them to it.
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