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The confluence of Artificial Intelligence (AI) and Machine Learning (ML) with the Financial Technology (FinTech) sector has ushered in a paradigm shift, fundamentally altering the contours of financial services. This scholarly endeavor undertakes a meticulous scrutiny of the evolutionary trajectory of AI and ML within the FinTech domain spanning the pivotal period of 2016 to 2020. Inextricably interwoven with notions of efficiency, security, and innovation, this exploration traverses the realms of operational processes, anti-fraud mechanisms, the bespoke landscape of personalized financial services, and the overarching influence on financial institutions. The canvas of this inquiry unfurls its historical panorama by anchoring in the pre-2016 epoch, elucidating the nascent manifestations of AI applications in finance. A discerning lens is cast upon pivotal technologies and algorithms that formed the bedrock of subsequent advancements. The narrative then unfurls to encapsulate the ascendancy of predictive analytics, the assimilation of both supervised and unsupervised learning paradigms, and the nuanced integration of Natural Language Processing (NLP) in the discerning analysis of financial data. Venturing into the substantive body of discourse, the examination scrutinizes specific strides, notably the assimilation of Robotic Process Automation (RPA) for the augmentation of operational efficiency. A close inspection follows the evolutionary trajectory of AI-driven algorithms tailored for the prophylaxis of fraud, fortifying the bulwarks against malfeasance within the financial ecosystem. Furthermore, the intricate tapestry of personalized financial services unfolds through the prism of recommendation systems, showcasing a nuanced blend of tailored financial offerings.
Paulin Kamuangu (Sat,) studied this question.