Kozminski Techblog

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Artificial Intelligence (AI) has its origins in the 1950s, when a group of researchers at Dartmouth College in the United States proposed the idea of creating “thinking” machines that could mimic human intelligence. This led to the development of the first AI programs, such as ELIZA (a computer program that could mimic human conversation). In the decades that followed, advances in computer technology and a deeper understanding of the human brain led to further developments in AI, including expert systems, neural networks, and machine learning. Today, AI is used in a wide range of applications, from drone cars to medical diagnostics to financial analysis.

Let’s break down what role AI plays in investing. It is to provide a more efficient and accurate way to analyze large amounts of data and identify patterns that can help in the decision-making process. AI can perform a variety of tasks, the main ones we’ll highlight are the following:

• Improving efficiency: AI can process and analyze large amounts of financial data quickly and accurately, which can help investors make clear and flexible decisions.
• Strategy development: AI-driven investment strategies can potentially outperform human-invented ones because they can identify patterns and make predictions that humans are incapable of making.
• Automation: AI can automate repetitive and time-consuming tasks such as data analysis and trading, freeing up time for investment professionals to focus on more complex and valuable phases of work.
• Risk management: AI can help investors identify and manage risk by monitoring portfolios and assessing risk levels.
• Regulatory Compliance: AI can help identify suspicious activities and ensure trading activity is compliant.
• Innovation: AI can help generate new investment ideas and identify new opportunities that analysts may miss.

In the future, AI is likely to continue to grow and integrate into various aspects of the investment process. AI has the potential to improve efficiency, accuracy, and decision-making in areas such as data analysis, forecasting, and automated trading. As technology and algorithms evolve and improve, AI will become more advanced and more capable of solving more complex problems.

In addition, legislators will need to adapt and ensure that the financial industry develops and uses AI in an ethical and legitimate manner.

That said, the risks associated with the use of AI cannot be overlooked:

• Lack of transparency: AI-based investment systems may not be easy to understand, making it difficult to identify and correct potential technical errors. Lack of transparency can also make it difficult for investors to assess the risk of specific investments on the same parameter.
• Bias: As strange as it may sound, AI systems are trained on historical data, which can be biased and this can reinforce existing biases in investment decisions. This can lead to disappointing results and increase the risk of material or reputational damage.
• Over-reliance: Investors may over-rely on artificial intelligence systems and miss important information or not fully understand the basic assumptions and limitations of artificial intelligence models. This can lead to poor investment decisions.
• Cybersecurity: AI systems are vulnerable to cyberattacks and data breaches that could compromise sensitive, financial information and violate investment regulations.
• Job liberation: The growing use of AI could lead to the displacement of professionals, which could have negative consequences for the economy, such as higher unemployment.
• Regulation: As AI technology advances, regulations may not keep pace with the pace of innovation, so gaps in oversight of AI-managed systems and AI-driven decisions may arise.

Thus, AI is increasingly being used to manage and optimize portfolios, find new investment opportunities, and detect fraud. However, it is important to note that AI is not a substitute for human experience and a deep professional perspective on the specifics of economic situations is superior to AI’s capabilities. Investment professionals still need to be able to correctly interpret and understand the results of AI, as well as use their own experience when making investment decisions.