Artificial Intelligence in Financial Markets

Exploring the applications and challenges of AI in finance.

By Mirella Deng, Director of Finance (McGill)

From Siri to self-driving cars, the innovation landscape for artificial intelligence (AI) is rapidly expanding before our eyes. Rooted in pioneering advancements, AI is taking over various industries by storm. In the realm of finance, it is one of the transformational technologies that can reinvent and enhance critical business functions. Its usages range from robo-advisory services to natural language processing in investment analysis. Though there are certain trade-offs and limitations that we must consider, AI ultimately serves as a competitive advantage for many firms in today’s complex and interconnected global investment environment.

AI in a nutshell

According to Vasant Honavar, director of the Artificial Intelligence Research Laboratory at Penn State University, AI can be divided into two key components: engineering and the science of intelligence. Through very different processes, the former focuses on building tools that use intelligence, while the latter studies the ways in which a computer can be programmed to come up with a solution similar to that of a human brain. Ultimately, the goal of AI is for autonomous systems to emulate functions and processes of the human brain.

Within the finance industry, the use of AI is increasingly important. Companies that invest significant amounts of capital into this technology can differentiate themselves from their competitors in aspects of high-efficiency, heightened security, and more. Their technological edge can open new doors, leading to future growth opportunities. Below captures some of the top uses of AI in the financial markets:

Robo-advisory Services

Risk Analysis

Algorithmic Trading & High-frequency Trading

Natural Language Processing

Challenges & Limitations

Cost: To procure AI is costly. On top of the sheer investment that firms must make to implement the technology in an effective manner, AI also requires regular updates to cater to the needs of an ever-changing business environment. In cases of systematic failures, the costs to repair damages caused by these smart technologies can be immense. In 2012, Knight Capital Group, an American market maker suffered a $461M loss after its electronic trading systems went haywire. After taking 17 years to become one of the top trading houses on Wall Street, all was lost in under one hour.

Integration Challenges: From the potential lack of understanding of AI systems within a firm to challenges in its usability and interoperability with other systems and platforms, the integration process of AI is tricky due to a diverse combination of needs. Historical data is needed to train the machine learning models that drive AI while there is also the need to host a complex set of technologies. In addition, the prediction power of any algorithm is highly dependent on the quality of the data that it is fed.

Wide-reaching Unemployment: It is estimated that by 2030, up to 800 million people worldwide, including a third of the workforce in the U.S., will be jobless, with up to 30% of work hours worked globally being automated. As the use of AI becomes rampant, the vast wealth inequalities among countries are revealed, leading us to consider whether the development of AI is environmentally sustainable to society.

Privacy & Ethical Issues: Building on the severe unemployment concerns and wealth inequality gap, AI also poses ethical questions such as the looming fear of an AI takeover. While humans can creatively take individual circumstances into account when making decisions, AI lacks emotion and moral values, and therefore risks containing their programmers’ biases. Intertwined into the technological complexity of AI are also privacy concerns such as client security issues and the potential lack of transparency of the technology’s uses.

Over the last few decades, technological advancements in artificial intelligence have increasingly reshaped many industry landscapes, including the financial markets. Its systems can comprehensively improve operational efficiency, reduce costs, mitigate risk, generate higher returns, and enhance user experiences. Though AI’s incorporation into finance remains at an early stage, financial institutions that fail to exploit this technology will increasingly find themselves at a competitive disadvantage. As the technology becomes more accessible and computing power is continuously improved, the future presents AI with unprecedented insights and opportunities.

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