Today technology is taking giant steps forward and has become a fundamental part of our daily lives. Non-stop and increasingly powerful innovations provide us a huge array of new opportunities that are only a click away. One example is artificial intelligence, a domain that has extended into the financial field, becoming an agent of change, capable of revolutionizing a large part of the world’s financial markets.
Global capital markets help corporations, governments, and other large institutional players exchange securities and commodities to accommodate their funding and investment returns requirements. Financial intermediaries like banks provide many services to these entities: access to markets, execution of large volumes with minimal market impact, the provisioning of liquidity in the markets (i.e. immediate trading at a fair price in the absence of other available trading counterparts), tailored financial products, and price formation.
In recent years, we have witnessed how a wave of innovation has disrupted the financial markets, and this has only increased since the global financial crisis. The main drivers are a change in investor preferences towards standard and simple financial products, a push from regulators towards transparency, increasing electronic financial transactions (i.e. the use of electronic channels to make trades instead of voice channels), and the fragmentation of liquidity pools (previously there was a single market or channel for instrument trading, now there are many).
This new environment brings with it profound challenges for financial intermediaries. On one hand, transaction volumes have tended to decline while the number of trades has increased significantly, leaving less margin for transactions and making the optimization of cost efficiency a key element. Moreover, there is an increasing amount of data available for decision-making, and its processing is becoming key for detecting business opportunities and prioritizing areas of greater comparative advantage.
One way of responding to these challenges is to exploit the opportunities offered by the increasing presence of electronic financial transactions, as well as the technologies that can digitize voice trading. It is thus possible to collect and analyze growing volumes of data, providing business units with more and better information about the markets and their own transactions. Additionally, computational algorithms can be used to automate and optimize trading activity, especially in those markets where small volumes of transactions are dramatically increasing.
"A robust technological base and the use of Big Data and artificial intelligence techniques will be key if financial intermediaries are to continue to be profitable"
Automation not only supports operational optimization and scaling up, it also allows teams to concentrate on transactions with higher volumes and/or margins. There is a consensus across the financial industry that a robust technological base and leveraging Big Data and artificial intelligence techniques will be key drivers for success.
Embracing these innovations is not without challenges. Electronic markets are in a constant state of evolution, and require tracking mechanisms to guarantee that the models and algorithms remain efficient and do not lead to serious market alterations. That is why new regulations such as the European MiFID II are greatly increasing requirements for transparency, control and auditing. In addition, although advanced artificial intelligence techniques, such as deep reinforcement learning, are becoming increasingly important with respect to trading algorithms, it is also true that they require considerable expert IT knowledge and resources for the massive data processing.
Even so, there does not appear to be any turning back. Increasing competition, due to the reduction of margins and the entrance of new, highly technological players, is challenging the status quo of traditional financial intermediaries, requiring them to adapt quickly and efficiently if they are to remain ahead.
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