Data Science for Finance: Best-Suited Methods and Enterprise Architectures

• Main Authors: Galena Pisoni, Bálint Molnár, Ádám Tarcsi
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Applied System Innovation
• Subjects: knowledge management; big data; business intelligence; organizational science; data science
• Online Access: https://www.mdpi.com/2571-5577/4/3/69

We live in an era of big data. Large volumes of complex and difficult-to-analyze data exist in a variety of industries, including the financial sector. In this paper, we investigate the role of big data in enterprise and technology architectures for financial services. We followed a two-step qualitative process for this. First, using a qualitative literature review and desk research, we analyzed and present the data science tools and methods financial companies use; second, we used case studies to showcase the de facto standard enterprise architecture for financial companies and examined how the data lakes and data warehouses play a central role in a data-driven financial company. We additionally discuss the role of knowledge management and the customer in the implementation of such an enterprise architecture in a financial company. The emerging technological approaches offer opportunities for finance companies to plan and develop additional services as presented in this paper.