Big data, AI and machine learning: A transformative symbiosis in favour of financial technology

Charalampos Stasinakis – University of Glasgow;

Georgios Sermpinis – University of Glasgow

-- The Financial Technology (FinTech) revolution is a reality, as the financial world is gradually transforming into a digital domain of high-volume information and high-speed data transformation and processing. The more this transformation takes place, the more consumer and investor behaviour shifts towards a pro-technology attitude of financial services offered by market participants, financial institutions and financial technology companies. This new norm is confirming that information technology is driving innovation for FinTech. In this framework, the value of big data, Artificial Intelligence (AI) and Machine Learning (ML) techniques becomes apparent. We believe that this triplet of data science ‘forces’ bond together and create the necessary ‘universe’ for the technological and digital transformation of financial services, and demonstrate this point in our recent paper, Big Data, Artificial Intelligence and Machine Learning: A Transformative Symbiosis in Favour of Financial Technology.”

The two main sections of the paper focus on the FinTech revolution through numbers. This analysis is based on banking and consumer expectations with a focus on how big data usage and AI and ML applications are transforming financial operations. We summarize several descriptive statistics from policy reports, consulting firms’ surveys, data science archives and academic research. It is clear that senior banker executives believe that FinTech firms (fintechs) are disrupting domains traditionally ruled by financial institutions, such as payments, savings and even loan or mortgages. Included surveys clearly illustrate that banks need to foster collaboration with fintechs, transforming the bank into a platform and opening and exchanging their customer base with each other, rather than attempting to develop new services in-house. This digital culture is a major shift in the strategic priorities of the financial institutions. Big data seem to be driving this transformation, as the statistics of banking data, data centers’ storage needs, global IP traffic inter alia show the importance of big data analytics. Even more astounding are the projections of GDP growth due to AI. Patent fillings, software development and hardware revenues based on AI and ML approaches are also prominent. Finally, ML applications and platforms remain the main driver of AI investment.

The fourth section of our work provides a non-technical summary of the techniques and their intersection with big data analytics. This is following the approach presented by the Financial Stability Board report (FSB, 2017). We describe the intersection of big data analytics with AI theory using the ML approaches (e.g. supervised, unsupervised and deep learning methods) as a medium of value exchange. This description allows the non-expert reader to understand and reflect on the data science puzzle revisited trough the recent advances of AI and ML. It is clear that big data remain crucial in data science, but their full potential is not exploited when observed as a separate entity. Only the transformative symbiosis of AI and ML can promote cognitive technologies and increase ‘black-box’ interpretability. This in turn can provide practical FinTech solutions adding value for bankers, investors, data analysts and computer scientists.

Finally, the last section summarizes how the above techniques impact banking, SMEs financing and FinTech related professions. We are focusing on supply and demand factors of financial adoption of AI and ML, hence applications such as P2P lending, fraud detection, digital wealth management and trading algorithms. Survey insights on the effects of AI adoption in the worldwide workforce balance are shared, while further analysis on the disruption of the financial analyst, accounting and data scientist profession is given. We posit that the symbiosis of big data, AI and ML techniques clearly transforms data scientists into the new paradigm of a modern professional ideal for promoting digital and technological innovation in financial services. In conclusion, this study’s main message is that big data, AI and ML ‘force’ is strong in Fintech. It is up to financial institution professionals, regulators, policy makers, government officials and educators to foster, expand and exploit it.

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