DOI : https://doi.org/10.18517/ijods.6.1.29-39.2025

AI Adoption Patterns in Banking: A PCA-Based K-Means Clustering Analysis Using Evident AI Index Rankings

Catherine Ngo (1), Orson Chi (2)
(1) Department of Agriculture, Food, and Resource Sciences, University of Maryland Eastern Shore, Princess Anne, Maryland, United States
(2) Department of Computer Science and Engineering Technology, University of Maryland Eastern Shore, Princess Anne, Maryland, United States
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C. Ngo and O. Chi, “AI Adoption Patterns in Banking: A PCA-Based K-Means Clustering Analysis Using Evident AI Index Rankings ”, Int. J. Data. Science., vol. 6, no. 1, pp. 29–39, Jun. 2025.
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The study investigated the adoption of AI in the banking sector using a PCA-based k-means clustering method, drawing on data from the Evident AI Index Rankings. The objective was to identify distinct patterns in banks' integration and use of AI technologies, with an emphasis on talent, innovation, leadership, and transparency. Utilizing PCA for dimensionality reduction, the study distilled the intricate aspects of AI adoption into fundamental components, thereby improving the comprehension of clustering patterns among banks. The k-means clustering identified unique segments within the sector, such as early AI adopters, innovation leaders, and conservative implementers, each exhibiting distinct levels of AI maturity and application focus. These findings provided valuable insights into the competitive landscape of AI utilization in banking, highlighting leading institutions in AI-driven transformation and those encountering adoption challenges. The insights from this analysis offered practical implications for stakeholders, guiding strategies for improved AI integration and competitive positioning. The study emphasizes the significance of data-driven benchmarking tools, such as the Evident AI Index, in assessing and guiding technological evolution across the sector.

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