AI micro-decisions in FinTechs: a mixed method research design

PurposeThe 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/approachThis 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.FindingsThree main themes (ambidexterity, data sovereignty and model explainability) emerge as underpinnings for effective AI micro decision-making in FinTechs.Practical implicationsThis 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/valueThis research pioneers as the very first empirical exploration delving into the essential factors that underpin effective AI micro-decisions in FinTechs.

Automated summary

This research explores the intricate mechanisms behind the micro-decision-making process employed by AI algorithms in the FinTech sector. It identifies ambidexterity, data sovereignty, and model explainability as the key factors for effective AI micro-decisions in FinTechs. This research is the first empirical exploration in this field.