Grace Walsh

Introduction 

In this essay, I aim to outline the role of artificial intelligence in fintech’s future, through examining what both fintech and artificial intelligence and their relationship in the finance  industry.  

What is Fintech? 

Fintech, financial technology, is first and foremost an example of the financial world’s  tendency to use unnecessary jargon and abbreviations, but in reality, is a term used to  describe everyday tools used in a myriad of global markets. An intimidating word to the  untrained eye yet exemplified by Revolut, tapping a phone to pay for something, buying and  selling, anything online and sending or receiving money from others are all examples of  fintech. Becoming an increasingly cashless world (Sprout on Dawson Street doesn’t even  take cash) means that fintech is being used on a wider scale. It is innovative and  transformative and has revolutionised the way money is spent therefore consumption and  hence has deepened global economic integration. (Stephanie Waldon, Doug Whiteman, 2022) 

Innovation in the world of finance has been a concept around for decades, although  the rate of change now is much faster than it was before. Changes in the financial world are  hard to keep up with and it can be difficult for regulators and policymakers to understand the  full effects of these technological innovations. Companies like Stripe, make money off  transactions and are therefore making money off people spending money similarly to PayPal. People can now also gamble online and are reminded with colourful and enticing  notifications that its time for them to return to the app and give up more of their money to the  hands of fate (attached to a man named Paddy Power).  

Firms in the financial industry facilitate transactions across markets, this behaviour  has become increasingly simple and accessible in recent decades with the emergence of  companies, like those mentioned above. 

What is Artificial Intelligence? 

Over the past decade AI has made significant developments and is becoming increasingly  accessible and useful in all areas of life; including finance. The IMF claimed that AI would  have a larger impact on the financial sector than COVID-19, in 2021, a shocking estimation  at the time, but now proving to be true. (Boukherouaa, AlAjmi, Deodoro, Farias, Ravikumar,  2021). The introduction of AI through sources like ChatGPT, has changed information access  forever, to a point where anyone can ask it to explain something to them as if they are a child,  in simpler terms than most websites, books or academic papers. People can learn about more  things in a way that suits them and far quicker than before. It took ChatGPT a mere five days  to gain one million users which took Instagram almost two months to reach. (Dave Ver Meer, 2024). Google’s revenue generated via online advertisements has reduced by over 30 billion  US dollars since the launch of ChatGPT according to Statista. It is no doubt that AI means  greater access to information which may help people make better investments and ensure  markets are efficient yet it also has the power to sway opinions, depending on the prompts it  is proposed it may play a role in confirmation bias. Nonetheless, it will change the future of  finance, for better or for worse.  

Potential benefits  

The world of AI has changed methods of information access forever. With this change in  financial markets, information access and communication is also changing, which can affect the financial sector. As seen in the infamous GameStop incident whereby Reddit was the  platform used by masses of day traders who determined the fate of wealthy short-sellers. AI  can now act as advisement in terms of investment decisions or help in creating a budget based  on personal goals and needs. AI has the potential to serve as an inexpensive financial advisor,  whether it is a good one or not is up for debate but regardless its influence exists and can only  grow from here. (Gomber, Kauffman, Parker and Weber, 2018).  

AI is a useful tool which can use financial and economic models, variables and  studies to predict and estimate macroeconomic changes in an economy. This means it is a much more flexible tool than using individual models of examination on the aggregate  economy or performance of markets. AI’s access to new data sources that other models may  not consider such as data uncovered through social media and many other previously ignored  variables. (Boukherouaa, AlAjmi, Deodoro, Farias, Ravikumar, 2021). AI uses machine  learning (ML) and deep learning (DL), which are the tools it uses to translate data into  knowledge that it can use to draw conclusions to certain questions or blend with other 

knowledge that it has previously attained. They are the key traits of AI which allow it to  operate as a useful tool in the finance industry, in the form of data analysis and forecasting.  (Plummer, 2024) 

In some areas of the financial sector including investment banking fintech is the  backbone of its operation. AI offers new opportunities for reaching new market participants,  through the increased accessibility as well as a new form of customer service via the AI  chatbots, hence reducing investment banks costs and increasing its efficiency. (Boukherouaa, AlAjmi, Deodoro, Farias, Ravikumar, 2021). These uses of AI in finance, as a fintech tool  reduce the amount of human contact drastically and significantly streamline the financial  institutions normal practices. The use of fintech and AI in the banking sector is a slowly  developing relationship in most cases due to regulation although there has been, increased  adoption of chatbots as a form of customer service and ATMs and online banking are also  forms of fintech. Through the use of other forms of fintech such as banking apps or online  shopping, machine learning can then be used through machine learning as a component of  creating forecasts about the future of economic stability and financial markets. There is the  potential for banks to use it as a method of risk assessment and credit decisioning by  assessing an individual’s financial history along with other considerations to determine a risk  level. (Plummer, 2024). In a similar way, this data can be used to create personalised  recommendations to customers based on their transactions and interactions with fintech.  

Potential Risks 

This form of data access, as mentioned above through social media or other streaming and  algorithmic services may have negative effects on financial markets as it is difficult to  regulate privacy in this area. As seen in recent years algorithms, social media and even  notifications play a role in politics, as seen in the case of Cambridge Analytica where debates  on data breaches during the Trump Campaign in the U.S. came to light. (Tett, 2017). The data  acquired may also be incorrect and create errors within AI responses to certain questions or  dilute the reliability of it as a source. (Boukherouaa, AlAjmi, Deodoro, Farias, Ravikumar,  2021) The reliability of AI on accurate data is a vulnerability which requires regulation and  supervision, as well as methods of intervention in preparation for crises. It poses ethical  issues as it already has built-in biases and tendencies, if stereotypes and generalisations exist  in data, social media or in anything which AI uses as a source then the same ideology is  absorbed by AI. Since the Global Financial Crisis of 2008, there have been much higher  levels of financial regulation in the banking and non-banking sectors worldwide. The 

development of technology sometimes seems as though it is happening first and the scramble  to come up with policies and regulatory requirements happens after. While AI creates new  challenges for regulators and policy makers it can also be used as a tool for both. It is being  used more and more as an integrated part of a system to detect fraud, money laundering and any other financial crimes which may exist through its accumulation of data about  transactions and patterns of behaviour. (Plummer, 2024) 

Future of AI as a Part of Fintech and Conclusion  Technological innovation has been the key driver for growth in almost all modern, open  economies and hence the pace of innovation is growing faster. Fintech developments are  disruptive to the financial industry, the days of phoning a stock broker are over, and the same  tasks can be fulfilled from anywhere at any time. The evolution of the financial industry from  the introduction of online banking and shopping to now the incorporation of AI. (Gomber,  Kauffman, Parker and Weber, 2018). The use of AI in finance will no doubt lead to further  global integration of markets and economies, increasing the vulnerability of the global  economy to a rampant economic crisis. Regulation and policymaking must coincide with the  future of fintech. AI has only begun to change the financial sector, it will continue to expand  and become more accurate and useful as it becomes integrated into banking systems and  models. It can help understand customers better, increase productivity and market  participation, give better access to information which (if correct) will improve market  efficiency, prevent fraud, reduce costs for financial institutions and make the overall world of  finance more accessible. (Plummer, 2024). With all these benefits comes questioning around  the ethics of its use, the potential of data breaches through ML and DL. The future of AI in  finance is unknown, and will take years of understanding, regulation and integration before it  can be used optimally and safely.