Transforming Financial Services with Digital Twin Technology
Mirroring large scale distributed systems into ontologically structured Digital Twin representations and the introduction of the canonical data model
Overview
Across verticals, large scale distributed architectures are evolving from collections of silo’ed functions to integrated environments that enable continuous data streaming [1]. This transformation is most evident in the financial services sector where the rapid introduction of artificial intelligence (AI) into the legacy environments is driving an immediate requirement to provision the AI engines with a continuous flow of high quality, consistently formatted data.
However, despite an estimated global investment of approximately US$30–40 billion in AI only about 5% of organisations report significant value extraction. The difference between the successful and the sub-optimal implementations appears to be less dependent on model quality, and more determined by implementation approach. This is being described as “the Pilot-to-Production Chasm” [2].