About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
INFORMS 2020
Conference paper
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.