Today, only the lazy do not discuss Artificial Intelligence (AI) and its potential to revolutionize practically every aspect of our lives, including finance. Indeed, there is a startling growth in the AI market—it surpassed $184 billion in 2024, $50 billion more than in 2023. Moreover, this blossoming is expected to continue, and the market will exceed $826 billion by 2030.
But this is only one side. On the other hand, research shows increasing problems with AI’s implementation, especially in finance. In 2024, it will increasingly face issues related to privacy and personal data protection, algorithm bias, and ethics of transparency. The socio-economic question of potential job losses is also on the agenda.
Is everything related to AI problematic? Let's consider real challenges to AI’s ubiquitous implementation in finance and the pitfalls we need to solve now so that AI can still reach the masses.
Real Challenges for Massive AI Integration
Initially, the goal was to create artificial intelligence at the level of human consciousness—the so-called strong AI—Artificial General Intelligence (AGI). However, we have not yet achieved this objective; moreover, we are nowhere near reaching it. Although we seem to be on the verge of introducing real AGI, there are still more than five-seven years left to do so.
The main problem is that current expectations of AI are vastly overstated. While our technologies are impressive today, they are only narrow, specialized AI systems that solve individual tasks in particular fields. They do not have self-awareness, cannot think like humans, and are still limited in their abilities. Given this, scaling AI becomes a challenge for AI’s spread. As AI is more valuable when used at scale, businesses still need to learn how to effectively integrate AI across all processes but retain its ability to be adjusted and customized.
Moreover, concerns around data privacy are not AI's main problem as many may think. We live in a world where data has not been confidential for a long time. If someone wants to get information about you, it can be done without the help of AI. The real challenge of AI’s integration is making sure it is not misused and deployed responsibly, without unwanted consequences.
The ethics of using AI is another question before AI reaches mass dissemination.
The main problem in existing systems is censorship: Where is the line when we prohibit neural networks from sharing a bomb recipe and censor responses from the point of view of political correctness, etc.? Еspecially since the “bad guys” will always have access to networks without restrictions imposed on them. Are we shooting ourselves in the foot by using limited networks while our competitors are not?
However, the central ethical dilemma is the issue of long-range aiming. When we create a strong AI, we will face the question: Can we use a reasonable system to perform routine tasks and turn it into a kind of slave? This discourse, often discussed in science fiction, can become a real problem in the coming decades.
What Should Companies Do for Seamless AI Integration?
In fact, the responsibility for solving AI problems lies not with the companies that integrate AI but, on the contrary, with the companies that develop it. Technologies are quietly being implemented as they become available. There is no need to do anything special—this process is natural.
Artificial intelligence works well in narrow niches where it can replace a person in communication, such as chat rooms. Yes, this is annoying for some, but the process will become more accessible and more pleasant over time. One day, AI will finally adjust to human communication style and become much more helpful, and the technology will become increasingly involved in customer service.
AI is also effective in pre-analytics when large amounts of heterogeneous information must be processed. This is especially relevant for finance, as there have always been departments of analysts engaged in uncreative but essential work. Now, when AI is attempted to be implemented for analytics, efficiency increases in this area. On Wall Street, they even believe this profession will disappear—AI software can do the analysts' work far more quickly and cheaply.
To achieve seamless AI integration, companies should take a strategic approach beyond adopting the technology. They need to focus on preparing their workforce for the change, educating them on AI tools, and fostering a culture of adaptability. In this way, everything related to reducing the burden on a person in routine tasks continues to evolve. As long as AI implementation gives companies competitive advantages, they will introduce new technologies as they become available.
The key is to strike a balance between AI’s efficiency and the challenges it may present.
AI’s Potential in Revolutionizing Finance
AI in the form of more traditional approaches and other methods have been used for a long time in the financial market, long before the last decades. For example, a few years ago, the topic of high-frequency trading (HFT) became especially relevant. Here, AI and neural networks are used to predict the microstructure of the market, which is important for quick transactions in this area. And the potential for the development of AI in this field is quite large.
When it comes to portfolio management, classical mathematics and statistics are most often used, and there is not much need for AI. However, it can be used, for example, to find a quantitative and systematic method to construct an optimal and customized portfolio. Thus, despite its low popularity in portfolio management, AI has development opportunities there. The technology can significantly reduce the number of people needed to work in call centers and customer services, which is especially important for brokers and banks, where interaction with retail customers plays a key role.
In addition, AI can perform the tasks of junior-level analysts, especially in companies that trade a wide range of instruments. For example, you may need analysts to work with different sectors or products. Still, you can entrust the preliminary collection and processing of data to AI, leaving only the final part of the analysis to experts. In this case, language models are advantageous.
However, many of the AI capabilities in this market have already been used, and only small improvements still need to be made. In the future, when artificial general intelligence (AGI) appears, there may be a global transformation of all industries, including finance. However, this event may happen only in a few years, and its development will depend on solving the ethical issues and other problems mentioned above.
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