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Financial Artificial Intelligence takes the lead

Financial strategic forecasting is a business field where both the experienced “sharks” and rookies are involved in a fierce struggle to keep afloat. Therefore, such intense competition pushes them to look for the acutest solutions to grow and benefit. Regular improvements such as attracting best graduated and experienced talents on the market are still helpful, but obviously, the situation will change soon. Nowadays, more companies and business leaders start relying on advanced computing technologies. Although machines can’t replace humans in full yet, no one will deny the fact that the robot’s performance rate is much higher and less exposed to error risk than a human’s one.

Artificial Intelligence is capable of processing big data and calculation of incredibly complicated financial algorithms consisting of various parameters that lead to more farsighted market forecasts and efficient solutions. Moreover, using robots in financial planning helps to decrease expenditures for business needs maintenance and to focus human staff on handling more creative tasks. The experts and researchers haven’t explored yet even the theoretical boundaries of financial robots’ potential to the fullest extent, so the impact of Artificial Intelligence on the banking and investment businesses is expected to strengthen continuously in the future.

One of the recent trends of the financial market is the aspiration of the banks to gather vast volumes of archive data, which helps them to get better future insights and benefit from more accurate planning.

Robotic financial advisers have already obtained the ability to analyze data and to make conclusions like human reflection, along with that, computation capacity and spectrum of various tasks that can be performed simultaneously by Artificial Intelligence is much broader.

Is Artificial Intelligence ready to replace humans in financial forecasting?

Although robotic financial consultants haven’t substituted human employees yet, some experts believe that the times when only live people could perform financial planning operations have already gone. The researchers back this opinion showing the outflow of human personnel from the commercial advisory business, being unable to compete with cutting-edge specialized software. Indeed, it is hard to deny human weaknesses, such as natural dependence on psychological status or emotional stability rate. Besides, live analysts can fail due to the lack of objectivity or occasional missing one or a few critical parameters because of simple forgetting. In contrast, machines don’t have all these disadvantages and can work impartially and unmistakably.

The discussions to find out whether Artificial Intelligence or individual staff is more effective in strategic financial forecasting can last for ages. While the proponents are arguing about that, there is a group of experts who believe that truth lies in the middle: a human, the financial adviser, assisted with advanced apps, can deliver remarkable results. This is an intermediate stage of financial AI development when synthesizing computer’s and human analytical potential helps to figure out the most prospective elements of this collaboration to be used in the future.

Some statistics about financial AI

According to the recent research of the National Business Research Institute and Narrative Science, today, more than 1/3 of the commercial businesses are utilizing AI in such fields as financial forecasting, consulting, strategic optimization, data storage, and processing improvement and so on.

However, the experts suppose that this is just a small share of the real financial robots’ potential. For instance, it is expected that the size of funds managed by the robotic consultants will grow up to half of trillion dollars within the next two years. And after the next five years, the AI, used by the banks, hedge funds, and insurance companies, can transform the financial analysis and forecasting segment completely.

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Liberating financial predictions

Utilizing the software and robotic patterns in digital financial consulting help to deliver better banking services to regular clients. Financial robots-advisers are capable of handling more customers as they can work around the clock, preserving the same level of efficiency and concentration to please even the most sophisticated requirements. Furthermore, robotic machines can sustain equal approaching models when contacting various social and economic groups of customers.

A simple example: when human staff works in the crediting department, he can pay more time and effort to the client who is expecting several million dollars loan compared with the customer who is going to borrow just ten thousand. Although most of the live employees are regularly trained, the studies show, 10% of bank consultants are inclined to follow such inequality in their daily operations. To avoid that, financial Artificial Intelligence can provide liberated behavioral models under various conditions.

Another simple reason why more business leaders of the financial industry prefer machine learning technologies in financial planning over human staff is that maintenance of machines is much cheaper. The financial robots can perform heavier working load within increased working hours. Therefore it becomes more beneficial to integrate digitized Fintech forecasting systems instead of hiring a team of live analysts.

Saved funds can be then redirected to other sectors such as balancing bank’s reserves or investing in promising industries. Moreover, the integration of robotic financial analytical systems can help to conduct full-scale digitization of the company. The implementation of AI in the planning department will require the involvement of extra digital solutions such as big data management via the cloud, particularly blockchain, reinforced data security apps, micro-segmentation, and many others.

False data and online security – top risks for robotic financial advisers

Being a part of pervasive Fintech progress, AI solutions, integrated into the financial planning industry, are the object of the range of threats. Some of the risks can be of actual financial or, in other words, traditional nature; other dangers can be more of digital origin.

One such challenge for the robots-consultants is the ability to separate accurate information from false data. As financial market predictions strongly depend on information authenticity, wrong market vision, or incorrect parameter can make all the digitization efforts vain. The experts believe that there can be few solutions to this problem, for example, to focus more attention on the development of advanced data verification algorithms based on collected and analyzed similar pieces of information for as long periods as possible. The second available solution is to work harder on intuitive components of AI by the integration of alternative logical patterns. In a nutshell, both suggestions can be of use if showing real effect during operational trials.

Data security is another challenge to the AI planners as the software acting in the multisource online environment and gathering huge volumes of information, including suspicious fragments, is highly vulnerable in front of potential hacking. And this is a point where AI can be effectively assisted with the other products of digitization, in particular, blockchain and micro-segmentation technologies. As it is proved, any piece of data or company’s IT system can be hacked depending on safety measures taken and hacker’s skills, that’s it is worth to concentrate on making the task of hacking incredibly complicated. Separating the stored data into individual blocks and splitting the processes of data processing among various system spots will help to keep information protected.

When software developers and integrators are puzzling about which technology will be the most efficient to secure robotic financial advisers, IT experts are concerned about the lack of compatibility between online protection and Artificial Intelligence. To let the robots adopt data protection apps successfully, the developers of both techs have to collaborate tightly. Thus, it might be a proper time to design a specific cybersecurity standards system to be approached with each member involved in the financial Artificial Intelligence implementation.

As long as the role of the human, a financial adviser is still significant, within the next decade financial planning industry may become a place of Artificial Intelligence eruption as the machine learning technology shows extraordinary capabilities in big data and sophisticated algorithms processing. Yet, it is expected that the financial companies will tend to synthesize both human’s creative vision and robotic capacities, for example, by equipping human, business consultants with virtual assistants, so the quality of market predictions and level of consequent benefits will proliferate.

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