Blockchain Analytics Reference Architecture for FinTech - A Positioning Paper: Advancing FinTech with Blockchain, Data Analytics, and Enterprise Architecture

Abstract

Financial services industry is one of the highly impacted industries with what is currently happening in the digital economy due to the “Digital Transformation” wave derived by the rapid evolution of the “Disruptive Emerging Technologies”. “FinTech” is the current dominating trend describing the role played by these technologies in the financial services industry. Although all financial sectors are affected, the insurance sector had been much disrupted due to not only the severe competition but also due to its data sensitivity and dependability. This makes insurance companies in an urgent need for the transparency and trust as well as the need to be “Data-Driven Enterprises” that competes through a “Data-Centric Strategy” that utilizes the power of “Big Data”. This made the “InsurTech” to become a standalone trend recently. The Blockchain technology can provide transparency and trust. However, having an "Enterprise-Wide Analytics" capability is the key strategic enabler to this needed data-centric strategy to make informed decisions that are inspired by data. Not to mention the competitive advantage that results from the synergistic effect of building a "Blockchain Analytics" capability specifically. Furthermore, many digital transformation initiatives are chaotic, Blockchain technology is under critical development and analytics is so time and money consuming. Traceability to strategic objectives are missing or implicit and no standardization is available. Something is needed to navigate these challenges and achieve this ambitious transformative vision. This paper will explain how “Value-Driven Enterprise Architecture”, with the “TOGAF” and the “Capability-Based Planning” technique, as a discipline and the “ArchiMate” as a modelling framework can provide a master blueprint for designing a “Blockchain Analytics" solution within an "Enterprise Wide Analytics" capability following the latest standards of the “Enterprise Data Lakes”. This blueprint will make the traceability to the strategic objectives explicit and provide the basis for the needed standardization. This paper cab be considered as a positioning paper that try to propose a vision for new research lines about this topic, which can be validated, enhanced, and extended through further empirical research projects in the future.