Designing a Model of Factors Influencing FinTech Optimization in the Financial Market Using a Hybrid Approach

Abstract

The present study examines the factors influencing the optimization of FinTech in financial markets using a hybrid approach. FinTech, as one of the fundamental transformations in the financial world, has significantly altered financial interactions, particularly through the application of advanced technologies such as artificial intelligence, blockchain, and big data processing. The objective of this research is to identify and model the components that affect FinTech optimization in financial markets and to employ optimization models to enhance their performance. Initially, to identify the key components, a qualitative method and meta-synthesis technique were utilized. In this phase, documents and articles related to FinTech and the banking industry were reviewed, and data extracted from other studies were analyzed. These data encompass various dimensions, including cybersecurity, artificial intelligence indicators, system scalability, and the economic and social impacts of financial technologies. After identifying the main components, the quantitative phase of the research involved modeling FinTech optimization based on these components using mathematical programming methods. The proposed optimization models were implemented using GAMS software with the epsilon constraint algorithm and MATLAB software with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The results obtained from the optimization models indicate that improvements in the identified components can lead to increased efficiency and reduced costs in FinTech systems. This study also demonstrates that employing a hybrid approach and simultaneously analyzing qualitative and quantitative data can facilitate a more accurate simulation of FinTech performance in financial markets and offer more effective solutions for improving financial processes.