The recent development of data-centric technologies such as artificial intelligence urges the transition from a traditional market to a data-driven market. Particularly in the banking industry, Open Banking (“OB”) regulations are enforced to open access to clients' data and services. The objective of the regulations is to enhance competition through equal data access and the development of data-centric services, which in turn can create additional total surplus. However, various challenges in the data-driven market are related to the problems of entrenched dominant positions, unfair practices, AI applications, and the shift in the distribution of resources and capacity.
The research examines the problems in transitioning from traditional banking to a data-driven ecosystem. Notably, it looks at the impact of OB regulations on the competitive environment. Moreover, as the regulations shift the resource distribution, the research aims to define the just distribution under which the regulatory impact can be interpreted.
The theoretical framework incorporates economics, competition, regulatory, and moral theories, applying the critical realism methodology. Quantitative methods include the Difference-in-difference method to measure the regulatory impact, big data algorithms to extract topics from tweets and user reviews, linear regression and SVM models to measure the network effect, market power and the regulatory impact prediction.
The modelling shows that OB regulations impact the competitive environment through several significant factors. Furthermore, the regulations have increased the total surplus, but it is not efficient under the Pareto test. Lastly, it predicts a possible long-term decline in competition.
