Key points are not available for this paper at this time.
This study investigates the heterogeneous impact of bank automation on institutional performance, emphasizing the role of mechatronic systems like automated teller machines (ATMs) and artificial intelligence-based tools such as chatbots and robo-advisors. Using Method of Moments Quantile Regression (MMQR), the analysis examines how these technologies influence key performance indicators, including return on equity (ROE), in the European Union (EU) banking sector from 2017 to 2022. The MMQR method allows for the differentiation of the effects of automation technologies by distinguishing between hardware-based mechatronic systems and software-driven AI solutions, providing a nuanced perspective on the digital transformation within the banking sector. The results highlight the heterogeneous effects of economic, financial, and institutional factors on banking performance in the EU. They emphasize the need for differentiated policy interventions to reduce performance gaps between EU economies and ensure that banks across all member states can leverage financial and technological advancements to enhance profitability. The findings underline the importance of strategic interventions to address digitalization disparities, promote financial inclusion, and establish a regulatory framework that fosters transparency, cybersecurity, and equitable access to AI-driven financial services.
Manta et al. (Fri,) studied this question.