Özet:

Purpose- Digital infrastructure and technology advancements are steering the innovations in financial sector globally. The technology and data driven aspect has fueled the Fintech sector, evolving at the tangent of mighty finance sector and revolutionary technology domain, especially the digital technologies. The purpose of this paper is to show that most FinTech innovations, are significantly driven by big data analytics and its efficient implementation. Methodology- The use of latest ICT technologies lightens up the finance operations and services to exponential levels. Big data analytics is new and requires comprehensive studies as a research field specially in the finance domain. The intent here is to study an adoption model specially IT diffusion mode to Big data analytics that could detect key success predictors. The study tests the model for adoption of big data as novel technology and the related issues. The paper also presents a review of academic journals, literature, to study the diffusion and adoption of big data in to the finance domain. Findings - The research reflects a significant interest and utility about Big data analytics value that epitomizes the rise of Fintech phenomenon. Big data analytics may provide some competencies to the organizations that may consider its several dimensions along with its framework in the pre-adoption phase or adoption phase or implementation or diffusion phase. The research also attempts to describe the several dimensions of Big data analytics as a new technology. This shall be of good interest to the researchers, professionals, academicians and policy-makers. Conclusion- The paper first defines big data to consolidate the different discourse and literature on big data. We also reflect the point that predictive-analytics (with structured data) overshadows other forms: descriptive and prescriptive analytics (with unstructured data) which constitutes more than 90% of big data. We also reflected on analytics techniques for unstructured data: audio, video, and social media data, as well as predictive analytics. In the analysis and testing part we also performed the testing of the IT diffusion model which concludes that there are significant relationships among IT-planning, IT-implementation and IT-diffusion.

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