Do AI Markets Drive Financial Performance in Chinese Banks? A Quantum-Inspired (QI) MCDM Approach
Wanke, P. Tan, Yong Floros, C.
Wanke, P.
Tan, Yong
Floros, C.
Publication Date
2026
End of Embargo
Supervisor
Rights
© 2025 The Authors. Published by Springer Nature. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-12-08
Institution
Department
Awarded
Embargo end date
Collections
Additional title
Abstract
This paper proposes a novel quantum-inspired multi-criteria decision-making (QI-MCDM) framework to assess the structural performance of Chinese banks considering emerging AI technological contexts. By embedding classical bank performance indicators within a quantum probability space, the model captures inter-criteria entanglement, decoherence from ideal benchmarks, and robustness under noise—constructs traditionally absent in conventional MCDM models. Empirical results reveal significant divergence in structural efficiency across bank types. Top-performing banks exhibit higher adaptability, often tied to agile governance and fintech integration, whereas lower-performing institutions are encumbered by legacy systems and structural fragmentation. Regression and random forest analyses further show that larger AI and smart city markets are paradoxically associated with reduced systemic entanglement, suggesting that contextual technological maturity fosters functional decoupling among traditional banking metrics. These findings provide theoretical and managerial insights into how technological complexity reshapes financial performance structures in emerging economies.
Version
Accepted manuscript
Citation
Wanke, Tan Y and Floros C (2026) Do AI Markets Drive Financial Performance in Chinese Banks? A Quantum-Inspired (QI) MCDM Approach. Information Systems Frontiers. Accepted for publication.
Link to publisher’s version
Link to published version
Link to Version of Record
Type
Article