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

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...

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Vydáno v:Journal of banking regulation Ročník 26; číslo 3; s. 392 - 407
Hlavní autoři: Herdt, Manfred, Schulte-Mattler, Hermann
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Palgrave Macmillan 01.09.2025
Témata:
Asset allocation
Autoencoders
Balance sheet characteristics
Balance sheets
Banking industry
Banks
Business model of banks
Business models
Cluster analysis
Clustering
Decision making
Deep autoencoder-based clustering
Deep learning
Funding
Innovations
Investment
Management decisions
Neural networks
Ontology
Policy making
Return on equity
Strategic management
ISSN:1750-2071, 1745-6452, 1750-2071
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Shrnutí: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.
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ISSN:1750-2071
1745-6452
1750-2071
DOI:10.1057/s41261-025-00269-y