AI in investments – this is how artificial intelligence works in brokerage

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AI in investments The topic of AI (artificial intelligence) or AI (artificial intelligence) is on everyone’s lips, and yet consumers and investors usually have no concrete idea of ​​it. In fact, our everyday life is already characterized by numerous intelligent systems such as voice recognition in smartphones, smart homes or translation programs and security systems. Artificial intelligence is now also used in financial investments. We show you how AI works, where it is actually used and what limits it (still) has. Creating an artificial intelligence, i.e. developing a machine that thinks and acts like a human, has fascinated researchers for centuries. In simple terms, artificial intelligence is computer programs that develop themselves and can think, learn and act in a similar way to humans.

Today’s AI computer systems belong to the so-called “weak AI”. This means that this form of artificial intelligence is able to process huge amounts of data, so-called “big data” with the help of machine learning, and to solve certain problems. But compared to the human brain, this AI does not gain a deeper understanding of problem solving. She is reactive and inflexible.

A so-called “strong AI” would be able to think logically like humans, make decisions on their own initiative, communicate naturally and combine all of these abilities in order to achieve a goal. To date, however, it has not yet been possible to develop a strong artificial intelligence.

PUT SIMPLY: As of today, AI is very good at recognizing patterns in very large data sets and evaluating them. Typical areas of application for AI are therefore self-driving cars, speech recognition or image recognition and virtual assistants. For use in the financial sector, the ability to recognize patterns in large amounts of data is also an advantage.

In summary, AI is:

  • A digital system that uses algorithms to analyze patterns and calculate probabilities.
  • A self-learning computer system based on fixed databases or self-created data. Through self-learning, it improves its algorithms and learns to understand data better. Learning can be networked with various AIs. In this way, computing power can be increased and the ability to interpret data improved.


Artificial intelligence requires, on the one hand, a lot of data and, on the other hand, rules as to how it should handle this data. These rules are called algorithms. An AI therefore always starts with rules that programmers have created. Only with increasing data volume can it derive and develop its own rules in a self-learning manner. To this day, a lot of preparatory work by people is necessary when using artificial intelligence. Even sophisticated systems still require more than 90 percent human work.

In the financial world, new data is produced every second. But it is difficult for an AI to assign this data. Because there are no general standards for the respective data sets. For example, an AI must first learn which key figures are really relevant in which company, in which industry or at which moment. If the AI ​​learns these relationships incorrectly, it can also misinterpret data by recognizing meaningless relationships. The use of artificial intelligence for financial investments is therefore linked to complex preparatory work by financial experts, precisely because the world of numbers in the financial sector is constantly confronted with changing conditions.


AI is already used in various forms in the financial sector.

Automation of processes: Financial institutions use strongly rule-based AI, for example for credit checks or for transactions in capital markets. AI helps companies map processes faster and more cost-effectively.

Big data processing: AI analyzes stock prices, stock news or analyst reports for banks. The algorithms can automatically process and prepare both structured and unstructured data.

Recognition of patterns: With the help of so-called “neural networks” and machine learning, AI can record unusual account movements, for example. It is also possible to predict course developments or customer activities. Pattern recognition is particularly useful for developments on the capital market that can be predicted on the basis of rules or conditions through data analysis. AI also plays a central role on the stock market in so-called high-frequency trading. Artificial intelligence buys or sells stocks within a fraction of a second.

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