A popular topic in the FinTech industry is Machine Learning (ML) and its numerous innovative applications. Andreas Braun, Director of Artificial Intelligence (AI) and Data Science at PwC, explained in a recent interview that in the last decade many AI, and more specifically ML, innovations have had a significant influence in the financial sector (England, 2022). But what exactly is machine learning? And what are its implications and applications in the FinTech industry?
Machine learning is a subdivision of artificial intelligence that uses data and algorithms to learn in the same way humans do, while progressively increasing its accuracy as it is learning (Sharbek, 2022). There are two main approaches to machine learning; supervised learning and unsupervised learning. Supervised learning is an approach where training materials with the correct output are fed to the algorithm (Azubi et al, 2018). The algorithm then learns to respond more accurately and quickly by comparing its output to the training materials.By learning from historical data the algorithm is able to predict future outputs. Unsupervised learning happens when no training materials are available and the algorithm is set out to find unidentified existing patterns from the data to derive rules from it (Azubi et al, 2018).
As you can imagine, an algorithm that can be trained to find patterns or predict future outputs is very valuable in the financial industry. This is why machine learning has been applied in many different ways. A few examples are fraud prevention, automated customer service, cyber security, and transactions and payment confirmations (Sharbek, 2022). In fraud prevention for example, the algorithm assesses a client’s purchasing patterns to identify abnormal activity (Sharbek, 2022). However, machine learning also brings some implications. Most prominently, because machine learning is relatively new in the financial industry its ethical standards have only been set recently (Rizinski et al, 2022). This means that until not too long ago, it is possible that certain organizations have been operating their algorithms with different ethical standards. Andreas Braun solidifies this concern in his interview by stating that the European AI Act is still under discussion and foresees a risk-based approach towards the use of AI (England, 2022). Overall, machine learning is proving to be a valuable asset in the financial world opening up doors to automating onerous tasks and solving complex issues.
References
Alzubi, J. et al. (2018) Machine Learning from Theory to Algorithms: An Overview. Journal of physics. Conference series. [Online] 1142 (1), 12012.
England, J. (2022). Why AI and ML is reshaping the fintech industry, Fintechmagazine.com. [Online]. Available at: https://fintechmagazine.com/financial-services-finserv/why-ai-and-ml-is-reshaping-the-fintech-industry (Accessed: 13 October 2022)
Sharbek, N. (2022) How Traditional Financial Institutions have adapted to Artificial Intelligence, Machine Learning and FinTech? Proceedings of the International Conference on Business Excellence. [Online] 16 (1), 837–848.
Rizinski, M. et al. (2022) Ethically Responsible Machine Learning in Fintech. IEEE access. [Online] 97531–97557.
