Financial innovation at large financial services businesses: Evidence from Eastern Europe

Abstract

Purpose: Big data analytics is a recently offered state of the art technology with potential business influence. Nevertheless, the roadmap for the effective implementation of its also the street to exploit its essential value remains unclear. This specific analysis seeks to create a much greater understanding of the enablers facilitating BDA implementation in the banking, in addition to the financial service part, from the view of interdependencies and interrelations. Design/Methodology/Approach: We use an integrated approach that incorporates Delphi assessment, interpretive structural modeling, and fuzzy MICMAC technique to identify the interactions between enablers, which determine the accomplishments of BDA implementation. Our incorporated method uses experts' domain understanding and gains a novel insight into the underlying causal relations about enablers, linguistic evaluation of the mutual impacts among variables, and also like two revolutionary techniques for visualizing the results. Findings: Our findings highlight the main key role of enabling components, including technical and skilled workforce, infrastructure readiness, financial assistance, and also selecting good main details strategies. These elements have considerable driving impacts on other enablers in a hierarchical style. The outcomes provide reliable, robust and easy insights into the qualities of BDA implementation in banking and financial programs as a whole system, while demonstrating attainable influences of all interconnected crucial components. Originality/Value: This analysis explores the main key enablers for good BDA implementation in the banking and financial service sector. Much more enough, it reveals the interrelationships of components by calculating operating as well as dependence degrees. This particular exploration provides managers with a clear strategic path toward effective BDA implementation.