Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks

Manfred Herdt () and Hermann Schulte-Mattler ()
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Manfred Herdt: Brandenburg University of Technology
Hermann Schulte-Mattler: Dortmund University of Applied Sciences and Arts

Journal of Banking Regulation, 2025, vol. 26, issue 3, No 6, 392-407

Abstract: Abstract This paper presents a framework to operationalize the multidimensional construct of a bank’s business model (BBM). We conceptualize the construct from a structural perspective, defining it as its balance sheet’s strategic composition and structure, encompassing asset allocation and funding sources. In contrast to prior research, our study describes the strategic decisions made by bank management as the starting point for the analysis, excluding the results of entrepreneurial activity from the construct’s conceptualization. We analyze balance sheet data for 163 European SSM banks and their subsidiaries—which we call SSM institutions—from 2014 to 2023, sourced from the S&P MI platform’s SNL Financial Institutions database. The study focuses on six balance sheet positions—three from the asset and three from the liability side—expressed as ratios to total assets. We apply a deep autoencoder-based clustering (DAC) model to operationalize the construct and compare the results with the k-means and k-medoids approach. Our empirical analyses identify four BBMs: diversified retail, non-diversified retail, wholesale, and investment-oriented banking. The DAC model leverages the nonlinear capabilities of deep learning, outperforming traditional clustering methods. This paper contributes to the literature on BBMs on a theoretical and technical level. Theoretically, a methodology for operationalizing the construct of a BBM is presented, which can be used to conduct cause-effect analyses. Technically, advanced clustering techniques, including deep learning models, are used to improve classification accuracy and provide new insights into the diversity of banks. The approach presented in this study offers valuable applications for both academics and practitioners in analyzing the impact of BBMs on other constructs, such as performance. Policymakers can leverage this framework to evaluate and guide the development of resilient business models.

Keywords: Business model of banks; Balance sheet characteristics; Cluster analysis; Deep learning; Autoencoders; Deep autoencoder-based clustering (search for similar items in EconPapers)
JEL-codes: C38 C45 E58 G21 L21 N24 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41261-025-00269-y

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