By Maria Otto
From high politics and policy debate, to the rapid reorganisation of big business, artificial intelligence has been the subject on everyone’s tongue since OpenAI first released ChatGPT in November 2022. Over the following two-and-a-half years, it has rapidly permeated seemingly every aspect of our daily lives. The same is true for the financial sector. In this period, the finance industry has been racing to automate and integrate tasks with AI that have historically been performed by humans. Currently, AI is being used to automate processes ranging from basic tasks like summarizing documents to more complex ones such as risk analysis, financial modeling, and portfolio allocation.
The question is no longer if but how AI will transform the financial sector, and what that means for those currently working in or aspiring to work in an industry upended by new technology. This has resulted in widespread fears that many so-called ‘back-office’ and entry level jobs are doomed to eventually be replaced by AI. However, both the quantitative and qualitative research is showing that AI is transforming the skills needed to be hired and retained in these positions, rather than replacing the jobs in their totality.
The financial industry is particularly ripe for AI implementation due to its need for quantitative and research heavy roles, the so-called ‘back office’ that historically compiled the data and crunched the numbers needed to create and run complex financial models. However, this is a time-consuming task requiring thorough research, and any minor errors in calculations can have an extreme effect on a model,thus costing firms greatly. Now, AI can take over much of this work, cutting research turnaround time and reducing human error, as it is capable of performing tasks both faster and more accurately than even the best trained quants. Companies are eager to adopt these AI models as they increase efficiency while decreasing the need for large teams of quants. A giant in the financial sector, JP Morgan Chase has been at the forefront of this shift, recently rolling out its own AI model, IndexGPT. This model tracks news articles to create ‘thematic indexes’ to identify possible investments based on trends. Essentially, it allows users to find stocks outside of the mainstream, allowing for potentially higher gains while tailoring to specific client needs
AI is not only being used to perform complex analysis and streamline financial tasks. As in most sectors, it is also being integrated in order to reduce time spent on repetitive tasks such as summarizing documents, creating drafts for presentations or reports, and drafting emails. In the insurance sector of finance, Robotic Process Automation (RPA) allows users to automate data entries and document processing reducing time spent digitalizing. AI is also being employed to automate fraud detection, credit scoring, and customer service at many of the largest banks and financial institutions. On the trading side, High Frequency Trading uses algorithms to execute automated buy and sell orders, reducing the need for traders. In 2016, this type of trading made up nearly 70% of all trading volume in the U.S. Equity Markets. This trend towards automation has been present for nearly a decade, driven by the sector’s fundamental demand for both efficiency and highly accurate analysis to deliver the profits all investors seek.
As AI becomes more deeply integrated into the financial sector, conducting both mundane and complex tasks, it is important to highlight that AI’s success relies heavily on human creativity and thought. Although AI is useful for automating and performing calculations, it is much more limited in its ability to innovate and offer new ideas when compared to human labour. In this regard, AI is very unlikely to simply wipe out roles entirely. Instead it will reshape those that traditionally focused on quantitative analysis and research into roles that leverage expertise to pinpoint specific strategies and develop new approaches for analyzing and forecasting markets. In a world where nearly all firms might rely on AI for quantitative analysis and research, the true differentiator and driver of profits will be attracting talent that can not only interpret models, but innovate upon them. The world of the back office will be more reminiscent of a think lab or academic research lab, creating a sector that thrives on adaptability and ideas. As Vivienne Ming of Soco Labs remarked, “I think the future of global competition is, unambiguously, about creative talent… everyone will have access to amazing AI… creative talent…will be who you are”.
Nonetheless, there are tangible challenges as well as ethical dilemmas that have resulted from this rapid AI adoption in the financial sector. The first and foremost is the significant environmental impact arising from the use of artificial intelligence. The technology has shown to be extremely energy inefficient, as each execution of a task consumes large amounts of energy. Furthermore, the data centers that power AI demand enormous natural resources, energy, cooling and electricity. All of which carry their own negative environmental impacts. Aside from the environmental impact, there is growing concern that these models are becoming more advanced at increasingly rapid speeds, thus outpacing regulation. This increases privacy concerns as well as concerns of data manipulation, potential security breaches, as well as concerns around the accuracy and bias of these models. Currently, financial institutions are asking regulators to develop standards for internal models and guidance to avoid these risks, although no government regulation exists yet. Now, it is up to financial institutions to address these concerns on their own and establish internal safeguards to circumvent the risks of unrestrained AI integration.
Although the ethical and regulatory concerns must be addressed by financial firms, AI adoption as the norm is becoming a reality. However, the financial industry will not stop hiring due to AI adoption, it will instead require different skills from its applicants. A Bloomberg Markets report found that job listings in the financial service industry that require AI knowledge increased by 60% in that year alone. University students should feel assuaged by their access to both the time and resources to understand and harness AI to further their career. The biggest takeaway is that firms are looking for applicants that not only have the financial expertise needed, but also can understand agentic models while interpreting and contextualizing the data they receive from them.
As companies continue to move towards AI integrated workplaces both in and outside of the financial sector, there’s been an increased emphasis on building both soft and digital literacy skills for those looking to enter the job market. MIT’s Career and Development Office, cited these as the ‘deciding factor’ for many firms when hiring. Many financial firms have adopted the coding language Python for a wide range of uses including financial data analysis and visualization, algorithmic trading, financial modeling, and risk management along with many other applications. Still, in many regards the demand for new AI-related skills has been matched by the supply of open and accessible learning sources for those threatened by the rise of new technologies. Coursera and Harvard offer a wide variety of online coding classes; LinkedIn also provides access to free courses to help leverage AI in the job market; and many generative AI models are open to access on the internet for students wishing to practice and improve their familiarity. In other words, the threat posed by AI automation is not inevitable or unavoidable, it is simply a technological development – as any other – that students must learn about and understand to compete in the dynamic modern job market.
Although the ethics and morality of AI are highly debated, the new technology has already made significant in-roads into the financial sector and shows little chance of stopping. In spite of this, AI is highly unlikely to replace human jobs in the financial industry outright, instead, reshaping the skillset necessary to succeed in the industry. While such sudden and dramatic change may seem daunting for many, especially those preparing to enter the job market, looking ahead to careers in finance, the picture need not be so bleak. Students are well placed, with the vast and accessible online resources, to learn how these models work and harness AI to highlight their individual value in the labour market.
The views expressed in this article are the author’s own and may not reflect the opinions of The St Andrews Economist.
Image Source: Pexels