On Data-driven Transformation Practices In Financial Services and The Scalability of Artificial Intelligence

Abstract

This paper addresses Data-driven Transformation Practices as the fundamental enterprise competence needed to deploy Artificial Intelligence (AI) for improving decision-making processes. Machine Learning is the type of (sub-symbolic, inductive) AI mostly used in enterprises today. The immanent inseparability of Machine Learning from Data makes monetization difficult to achieve by algorithmic work carried out in the tradition of Computer Science. Furthermore, the absence of enterprise-wide transformation practices largely explains the so-called Scalability problems of AI. Practical examples in financial Services will be used to illustrate ongoing challenges and solutions.