Gabi Svobutaite

Introduction: 

Contemporary advancements within the spheres of Artificial Intelligence (AI) are rapidly reshaping  the financial services industry by revolutionizing operations through increased security, boosted  innovation, and accelerated digitization. Artificial Intelligence is particularly pervasive throughout  the fintech industry, automating processes which formerly required considerable time debt and a  sizeable labor force. The influence of AI may be seen beyond simply advancing the current  paradigm of financial services, it is furthermore facilitating the growth of future innovations and  sustainable business models that were previously deemed unimaginable.  

This paper seeks to provide a comprehensive overview and analysis of AI and its advancements in  fintech through a multifaceted approach. Firstly, this paper provides a background on financial  technologies and the areas of such which saw the most growth at the hands of AI. Secondly, this  paper discusses the societal impacts of increased AI use in fintech. Finally, this paper details the  extent to which and how AI is shaping the future of sustainable banking models. 

Fintech: What is it? 

Fintech refers to an emergent branch of financial services where finance and technology merge. It  encompasses a new era of technology which uses specialized algorithms to digitalize fundamental  financial functions which affect how consumers store, spend and save money. Fintech also impacts traditional investment approaches, having established cryptocurrencies such as Bitcoin as a major  player in the world of trading. The term “fintech” applies to a vast variance in innovations regarding  consumer transactions, whether it be developments in mobile banking (MB) or managing  investments through an online brokerage platform.¹ 

The fintech market may be segmented by four major consumer classes; (i)business-to-business  (B2B) for banks, (ii)clients of B2B banks, (iii)business-to-consumer (B2C) for small businesses, and  (iv) consumers. The increased data security, digitalization and analytics precision which fintech allows for has enabled the aforementioned user parties to interact and collaborate with a new level  of efficiency. ² 

The Fintech Surge: 

The fintech sector saw staggering rates of growth in the latter half of the last decade, with venture  capital (VC) entities investing $19.4 bn into fintech startups in 2015 and an astonishing $33.3 bn in  2020. The Covid 19 pandemic furthermore brought about an era of mass digitalization, causing a  whopping $92.3 bn boost in fintech funding, as well as an expected deal activity increase of 19%.  This upsurge in investments proved to be short-lived, however. Declining geopolitical and  macroeconomic conditions in 2022 provoked a destabilization of the global business environment,  leading to fintech funding returning to standard, pre-surge levels. This devaluation, although  expected, caused many fintech companies to lose vast amounts of capital, and quickly. Venture  capital funding suffered a decrease of $459.6 bn in 2022 from $683.1 bn in 2021. Total investments  in fintech were additionally faced with a 40% decline following this, now sitting at $55 bn as  opposed to $92 bn in 2021.³ 

Though the fintech sector still faces many obstacles in the future, there are also many potentials that  have not yet been fully realized. Forecasts for the development of AI technology point to a stable rise in sophistication, thus leading to a steady rise in popularity and use as opposed to more  traditional banking practices. Fintech’s will continue to profit from the dramatic shift and integration  of AI in the banking sector, the quick uptake of other emerging digital technologies, and the global  expansion of e-commerce, especially in developing nations.⁴ 

ML and AI Chatbots: Leading Areas of Growth 

Machine learning (ML) is an emergent area of AI in fintech which utilizes data reading algorithms  and software to better analyze and individualize services provided by financial institutions. ML is  revolutionizing how Big Data is assessed and trading market risk is predicted, without the need for  human employees.  

With the rapid growth and improved computational capabilities of modern technologies, large  amounts of data are becoming more readily available for economic and governmental entities to utilize. Analyzing this data, however, would require vast amounts of capital, time and people  resources. Therefore, companies and governments alike have turned to alternative methods of  analysis like AI to identify, process and study Big Data.  

ML uses intricate coding and algorithm models to source smaller data sets from the generalized pool  of Big Data, and thus create predictions which stem from the data sets identified. The algorithm  then uses recognition technologies to identify patterns for learning and remembering data, which  allows for abnormal activity and pattern change recognition when introduced to new data sets. 

Machine learning thus has become a large player in various industries such as banking and investing, or marketing and fraud regulations. Financial sectors may use ML to recognize trading opportunities  or identify market risks for loans and investments. Marketing and advertisement industries can  utilize machine learning algorithms to learn about their consumer’s online media activities to provide  them with the best suited and individualized content recommendations.⁵ 

AI Chatbots have spearheaded the digitalization of customer service in the domains of financial  service and fintech industries. E-commerce firms and startups now employ AI via the means of  virtual assistants and chatbots to offer immediate and around-the-clock assistance to their  customers. Through using complex algorithms and data recognition software to analyze individual  customer transaction data, the aforementioned AI-powered virtual assistants can offer customized  and instant advice to each customer.⁶ 

AI in Fintech: Societal Impacts 

The integration of AI in the spheres of fintech and the financial services industry has and continues  to have an extraordinary impact on society both on a micro and macro level. Firstly, through the  introduction of mobile banking (MB) and other e-commerce platforms, AI has allowed for fintech  to acquire a far greater consumer base, enabling individuals from disadvantaged and thus  underbanked regions to manage their finances effectively and with ease. According to a 2021 study  by McKinsey, the demand for e-commerce structures is notably higher in developing countries;  Africa was home to virtually half of the world’s total MB accounts, approximately 800 million users  were estimated. Fintech with its adoptation of AI had additionally served to further overall economic growth. AI has allowed fintech to establish new competitors within the financial industry,  thus driving prices down and allowing for increased choice within the market.⁷ 

However, the growing popularity and mass usage of ML, MB and virtual advisory services has  contributed to a wide scale trend of employee layoffs. Several tech firms have already quoted AI as  the reason for their staff layoffs and reconsideration of tech graduates for recent hires as Silicon  Valley, Big Tech and commercial banks seek to keep up with recent AI advances. One of the  principal ways to pivot business activities to improve key operating metrics and hit the bottom line is  reducing headcount. Cutting headcount expenses to maintain profitability whilst backing  technological innovations is especially prevalent in the current climate of slowed VC and fintech  funding. Due to the increasingly automized nature of Big Data analytics, the increased consumer  preference of mobile banking systems and e-commerce platforms for capital management and  investment activities, and of course AI-powered virtual assistants overtaking existing financial  advisors in the banking industry, the need for physical employees is becoming increasingly  nanoscopic. ⁸ 

Fig 1: data retrieved from FRED, Federal Reserve Bank of St. Louis. 

Figure 1 shows the fluctuation in US commercial bank employees, a definite decline may be seen in  recent years with a current downward trend visible after 2023.⁹ 

Utilizing AI to Pursue a Path of Future Sustainable Growth: 

AI and fintech have the power to mitigate the largest growing social challenges we face today,  namely climate change and reaching national sustainability goals. Such issues require highly  progressive and innovative solutions, those which artificial intelligence and machine learning systems can produce.  

Sustainable investing is an area which massively favors AI and utilizes it to aid investors in sourcing and analyzing large volumes of new information when considering the risks and opportunities  relating to ESG (Environmental, Social and Governance) investment obligations of companies and  governmental bodies. Additionally, AI algorithms allow for an in-depth analysis of all the  information available about a certain company, which can be an immense and costly task for a  human employee to undertake. This leveraging of AI and ML technologies enables potential  greenwashing companies to be discovered in a far timelier manner. ¹⁰ 

Conclusion: 

To offer some parting words, there is no doubt that the integration of artificial intelligence (AI) in  fintech has revolutionized ways in which the global majority manages their finances. With its ability  to analyze vast pools of data, predict capital market risks and trends, and provide customized user  experiences, the journey of AI in fintech will undoubtedly continue to evolve, encouraging a new age  of financial innovation.