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- W4387639648 abstract "Purpose The exponential growth of artificial intelligence (AI) technologies, coupled with advanced algorithms and increased computational capacity, has facilitated their widespread adoption in various industries. Among these, the financial technology (FinTech) sector has been significantly impacted by AI-based decision-making systems. Nevertheless, a knowledge gap remains regarding the intricate mechanisms behind the micro-decision-making process employed by AI algorithms. This paper aims to discuss the aforementioned issue. Design/methodology/approach This research utilized a sequential mixed-methods research approach and obtained data through 18 interviews conducted with a single FinTech firm in France, as well as 148 e-surveys administered to participants employed at different FinTechs located throughout Europe. Findings Three main themes (ambidexterity, data sovereignty and model explainability) emerge as underpinnings for effective AI micro decision-making in FinTechs. Practical implications This research aims to minimize ambiguity by putting forth a proposition for a model that functions as an “infrastructural” layer, providing a more comprehensive illumination of the micro-decisions made by AI. Originality/value This research pioneers as the very first empirical exploration delving into the essential factors that underpin effective AI micro-decisions in FinTechs." @default.
- W4387639648 created "2023-10-15" @default.
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- W4387639648 date "2023-10-17" @default.
- W4387639648 modified "2023-10-15" @default.
- W4387639648 title "AI micro-decisions in FinTechs: a mixed method research design" @default.
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- W4387639648 doi "https://doi.org/10.1108/md-10-2022-1336" @default.
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