There is an increasing interest in using AI and machine learning to help trading firms better serve their clients. Analyzing previous trading behaviour, for example, can help predict a client’s next order. Large volumes of data are generated by trading, and machine learning technologies often require this scale to perform well. Voice-to-text services will continue to grow in popularity, which means more trades conducted over the phone will provide data that can be added to the existing electronic platform data.
By using AI and machine learning, risk exposures can be managed more proactively. In order to assess whether a member’s trading account positions have elevated risk profiles and require action, exchanges can use machine learning as a foundation for risk modelling. Banks and other large trading enterprises have been able to centralise the risks that originate from multiple sections of their businesses by using risk management strategies based on big data analysis, such as a central trading book.
Artificial intelligence (AI) and machine learning (ML) can aid in the observance of trading restrictions. Deep learning-powered voice-to-to-text technology is one RegTech use of AI to trading. In order to comply with non-equity market transparency rules, this assists companies.
