Abstract

Selecting personal banking products requires evaluating multiple criteria under changing market conditions. Traditional multi-criteria decision analysis (MCDA) approaches are typically static and fail to account for temporal dynamics. This paper proposes a time-aware decision support system that extends the SPOTIS method by integrating longitudinal data into a unified evaluation framework. The system enables simultaneous modeling of criteria importance and temporal preferences, such as recency versus stability. An empirical study of Polish banks demonstrates that the proposed approach provides more informative and context-sensitive rankings compared to conventional methods. The results highlight the value of incorporating temporal information into decision support systems for dynamic environments.

Recommended Citation

Karczmarczyk, A., Karczmarczyk, A., Wątróbski, J. & Bączkiewicz, A.(2026). Towards a Time-Aware Decision Support System for Evaluating Personal Banking Products. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.160

Paper Type

Short Paper

DOI

10.62036/ISD.2026.160

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Towards a Time-Aware Decision Support System for Evaluating Personal Banking Products

Selecting personal banking products requires evaluating multiple criteria under changing market conditions. Traditional multi-criteria decision analysis (MCDA) approaches are typically static and fail to account for temporal dynamics. This paper proposes a time-aware decision support system that extends the SPOTIS method by integrating longitudinal data into a unified evaluation framework. The system enables simultaneous modeling of criteria importance and temporal preferences, such as recency versus stability. An empirical study of Polish banks demonstrates that the proposed approach provides more informative and context-sensitive rankings compared to conventional methods. The results highlight the value of incorporating temporal information into decision support systems for dynamic environments.