Strategically integrating artificial intelligence and machine learning into banking processes to enable data-driven innovation

Artificial Intelligence (AI) and machine learning (ML) are branches of innovations in computer science that seek to replicate or simulate human thought processes and actions in an automated fashion. In recent years, AI and ML have spurred the interest of academics and practitioners alike, owing to their prevalence and success across various disciplines. The advents of improved computational capabilities afforded by advanced computer architectures and quantum computing, the proliferation of big data enabled by the Internet and wireless access, and the advancements in theoretical foundations of AI have culminated in transforming the practice of operating businesses; these now include norms of increasing reliance on data-driven decisions enabled by AI and ML capabilities. Given the operational characteristics of the banking sector, such as their reliance on heavily rules-based and risk-averse practices, banks and financial institutions are investigating the feasibility of effectively integrating AI into their systems.