A Taxonomy of Banking Financial Model in Study of Computer Science

In the dynamic aspect of banking, the ability to bank financial models accurately is paramount for strategic decision-making, risk management, and regulatory compliance. A robust financial model serves as a cornerstone for understanding the complexities of banking operations, assessing profitability, and optimizing resource allocation. However, despite the extensive research in this field, there remains a notable gap in the literature regarding certain aspects of banking financial models, especially the utilization of computer science in the field. Therefore, this paper proposes a taxonomy of the banking financial model in computer science studies. We utilized a taxonomy approach introduced by Nickerson et al. [1] with the conceptual-to-empirical (C2E) technique to construct our taxonomy for organizing and categorizing computer science fields into hierarchical groups based on shared similar objects and characteristics. Based on the result, our taxonomy advised structuring and describing banking financial models through five objects, thirteen dimensions, and 127 characteristics in which the risk assessment algorithm, data encryption, optimization algorithm, and Monte Carlo simulation are dimensions discussed more often. Furthermore, the empirical examination allows researchers to identify the utilization of integrating an optimal decision support system with uncertainty handling and software architecture in this research domain as a potential future work.