In today’s fast-paced financial industry, knowledge management and data-driven decision making have become essential for the success of financial technology (FinTech) companies. Big data (BD) is a prevalent phenomenon that can be found across many industries, including finance. Despite its complexity and difficulty to comprehend, big data is a critical component of financial services enterprises and technology architectures. We examine BD from various aspects, considering data science (DS) techniques and methodologies that can be applied during the operation of an enterprise. Our aim is to provide an overview of knowledge management (KM) practices and data analysis (DA) strategies and techniques in the daily operations of financial companies. We address the role of knowledge management, data analytics in a financial institution. The paper demonstrates financial institutions’ enablement for new services resulting from technological advancements.

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.

Business process modeling and verification have become an essential way to control and assure organizational evolution. We overview the opportunities for the application of blockchain in Business Process Management and Modeling in Finance and we focus on in-depth analysis of claim process in insurance as a use case. We investigate the utilization of blockchain technology for model checking of Workflow, Business Processes to ensure consistency, integrity, and security in a dynamically changing business environment. We create a UML profile for the blockchain, then we combine it with a UML activity diagram followed by a verification using Petri nets to guarantee a distributed computing system and scalable with mutable data. Our paper creates a unified picture of the approaches towards business processes modeling used in the financial industry organized around the set of premises intending to develop a future research agenda for blockchain business process modeling, specifically for the financial industry domain.

Because of the competitive economy, organizations today seek to rationalize, innovate, and adapt to changing environments and circumstances as part of business process improvement efforts. The strength of blockchain technology lies in its usage as an apt technology to enhance the efficiency and effectiveness of business processes; furthermore, it prevents the use of erroneous or obsolete data and allows sharing of confidential data securely. The use of superior technology in the execution and automation of business processes brings opportunities to rethink the specific process itself as well. Business processes modeling and verification are essential to control and assure organizational evolution, therefore, the aim of this paper is three-fold: firstly, to provide business process management patterns in finance, based on blockchain, specifically for the loan-application process in the banking industry and claim process in the insurance industry that could be used and customized by companies; secondly, to critically analyze challenges and opportunities from the introduction of such approach for companies, and thirdly, to outline how companies can implement the loan business process as a web service. Partner companies (a bank and an insurance company) formulated the potential requirements for M2P along with the application of blockchain technology. An experimental design framework was established that gave the necessary services to model the requirements, check the models, and operationalize the models. The applied research methodologies are as follows: design science research paradigm and software case study, model-to-programming (M2P) of business processes, and utilization of patterns of workflow and blockchain.

Many companies look for novel ways to trace their operational sustainability. The application of AI to analyze and make sense of the big data the company holds represents one promising approach for this aim. The authors study how one can set and design an AI-based solution for improving the sustainability of complex business processes and decision-making in companies of different types. First, they provide a general analysis of current frameworks for measuring adherence to sustainability goals for companies, then they present a conceptual framework and architecture design for an AI-enabled sustainability service for companies. The implications of our research suggest that AI can provide distinct functions: (a) automation: taking big data from different departments and analyzing them with the aim of tracing the sustainability of the company; (b) support: to help decision-making and create relevant insights for stakeholders that are coherent with defined sustainability decision criteria. To the authors' knowledge, no previous research has provided analysis and design of such AI solution for companies.