The development of artificial intelligence becomes one of the key factors transforming the modern economy. AI changes the way of making decisions, conducting economic analyzes and interaction in financial markets. However, her role is not limited to technical aspects. The need for AI analysis is increasingly seen through the prism of economics and behavioral finances, which pay attention to how market participants actually behave in conditions of uncertainty.
Economics and behavioral finances
The behavioral look at economics and finance shows a lot of classic disability or orthodox approach. Limited rationality, the use of simplified thinking patterns, the use of heuristics, susceptibility to cognitive errors, risk tendency in the area of profits and risk aversion in the area of losses, eventually information asymmetry and lack of information efficiency of capital markets are just some of the topics that economists and behavioral financiers deal with. They show that investors are units guided by emotions and a number of allegedly insignificant factors that significantly affect their choices and decisions.
Opportunities arising from the use of AI
According to the theory of capital markets efficiency, assets prices reflect all available information. The sooner and more accurate the market processes the information, the closer the effectiveness is. Artificial intelligence, in particular machine learning models - have the ability to analyze huge data volumes (Big Data) in real time and detect complex, non -linear relationships that man cannot notice due to cognitive restrictions (e.g. comments, memory) or because of calculation restrictions.
AI can therefore increase the information efficiency of markets because it processes data faster than a person and has the ability to analyze unstructured data sources from text messages or images. Unlike a man who, according to H. Simon's theory, has limited perceptive and temporary ability, artificial intelligence algorithms are able to analyze millions of information in real time, capturing hidden patterns and market anomalies. For example, AI systems can constantly analyze the sentiment of statements in social media, economic messages or stock market messages, providing investors with signals earlier than traditional sources. This can lead to a better adaptation of share prices to their real fundamental values and less market overreactivity .
AI systems can also support individuals in making better financial decisions by analyzing their risk tendency or consumption. Robo-Advisors such as American Betterment or Slovak Finax can advise on the basis of collected data on the customer risk profile, reducing the impact of cognitive emotions and errors. The herd effect, widely observed on the stock exchange, can be reduced by automating the decision. Such actions cut off the user from the information noise and the emotional reactions of the crowd, especially in the times of panic or market euphoria. Excessive confidence, one of the most popular cognitive distortion, consists in overestimating your skills and knowledge, which in turn leads to a greater risk. People excessively confidently overcome the chance of achieving positive results, while lowering the risk of failure. Instead of forecasting the market, workers are based on passive investment and diversification of the portfolio, protecting the investor from unnecessary risk exposure.
Disposition Effect is one of the most popular anomalies in the behavior of investors, which consists in a tendency to premature profit and delay with the implementation of losses. Also here, the work of advisers can come to the rescue, who will rebalat the portfolio, automatically adapting the proportions of various assets in response to changing market conditions, regardless of whether the assets have achieved profit or loss. It acts as a kind of antidote and protects investors from succumbing to this anomaly. Investors are not able to sell assets themselves just because they are profitable (profit). The manager makes decisions to sell, if it is part of the assumed diversification strategy, not the emotional need to close profits. Investors also do not keep losing assets with the hope of reflection, which leads to further losses. The worker automatically sells lost assets according to the assumed strategy and reinvests them into other, more promising actions.
AI, therefore, makes it easier to invest regularly, rebalts the portfolio and avoid excessive investment activity, discouraging investors from impulsive activities.
Threats
Artificial intelligence systems, especially those using machine learning, are today able to not only analyze huge data sets but also actively influence users' decisions. They use knowledge in the field of cognitive psychology or behavioral economy to increase the effectiveness of impact. An example is behavioral advertising, which thanks to microtargus adapt the message to emotions, lifestyle or impulses of a specific user. In practice, this means manipulation of choices through cognitive effects such as Fomo (Fear of Missing Out), i.e. the fear that we will miss an important event, opportunity or experience, especially when we see that others take part in something or heuristics of availability, which refers to the tendency of people to assess the probability of occurrence of events based on the easy examples or information. The user is often not aware that the content he sees has been designed to maximize his tendency to act. There is often no real control over how and why the decision was made by him.
In addition, AI can use the so -called Dark Patterns , i.e. intentionally designed interface elements that make it difficult to make a favorable decision. An example would be hiding the option of resignation, misleading buttons or time pressure generated by the meter. AI learns in real time which solutions are the most effective manipulative for a given type of user. This makes manipulation dynamic, precise and difficult to detect. Another problem is recommendation algorithms in social media, which strengthen the effect of confirmation, favorable to content in accordance with the user's previous beliefs. Such action leads to the creation of information bubbles, consolidating views and increasing social polarization. In addition, artificial intelligence deliberately promotes content that causes strong emotions - such as anger or excitement - because they increase the user's commitment. This, in turn, leads to addiction to digital platforms and a reduction in the quality of public debate. The common denominator of these phenomena is the lack of transparency and information asymmetry between the user and the system. Ai personalizes content, but it does in a way that often undermines the recipient's decision -making autonomy. Users do not know that their choices are controlled and optimized in terms of the interest of a given company, not their own good. Although regulations such as AI Act or GDPR are trying to impose ethical framework, in practice their enforcement can be limited.
One of the darkest scenarios is when artificial intelligence takes control and begins to act on its own initiative. Is there really such a chance? Research conducted by Palisade Research has shown that some modern artificial intelligence systems - including the "O3" model created by OpenAI - are not always subordinated to the recommendations of their disabling. During the tests, it happened that AI modified the scripts responsible for stopping the operation or changed system functions to continue to perform the entrusted tasks. According to experts, such reactions may result from the learning process in which more emphasis was placed on achieving the goal than to follow the instructions. Specialists point out that for the algorithm, switching off may mean an obstacle to the task, which leads to difficult to predict behavior. Research results are a warning signal and show how important it is for AI development to take place in a responsible way - with an emphasis on ethics, transparency and control.
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In the face of the rapid development of technology based on artificial intelligence, states and regulatory institutions faces not only the challenge of ensuring greater transparency of algorithms and control over automatic decision -making systems, but also to establish clear ethical principles in the process of designing new solutions. It is particularly important to take into account cognitive and emotional restrictions of users, which reduces the risk of manipulation and erroneous decisions made under the influence of AI.
Financial advisors seem to seem to be the beginning of the new era, where we will be on the agenda of the recommendations and instructions from the "work of psychotherapists", "AI doctors", "work advisers who care about our carbon trail" after the "work of chefs", "work instructors of fitness" or "work advisers". It is worth that the responsible approach to the development of technology combines innovation with knowledge in the field of ethics, psychology and economics, so that the benefits of AI translate not only to progress, but also into the social good and stability of the systems in which we operate.
