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2019 | OriginalPaper | Chapter

4. Identification of Business Models

Author : Rym Ayadi

Published in: Banking Business Models

Publisher: Springer International Publishing

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Abstract

The bank business models (BBM) analysis relies on sufficiently granular, good quality and comparable data collected from the savings n loans (SNL) database. For several years, the author led the research that developed several databases to test and analyse the concept of business models in banks and other financial institutions (e.g. credit unions) in Europe, US and Canada applying the Activity/Funding (A/F) definition proposed in the previous chapter. Earlier attempts of the BBM analysis were initiated to analyse how diverse is the banking sector in Europe and the ongoing research is applying this approach to other countries. This chapter describes the datasets and the methodology and shows the findings of bank business models (and credit unions) for Europe, US and Canada.

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previous chapter Defining a Business Model in Banks
next chapter Bank Business Models and Financial Stability Assessment
Footnotes
1
Ayadi et al. (2011, 2012, 2016, 2017) and Ayadi and de Groen (2014).
 
2
The research on bank business models succeeded two research works on banking diversity in Europe, which assessed diversity via the co-existence between shareholder and stakeholders’ banks (i.e. savings banks, public banks and financial cooperatives) (Ayadi et al. 2009, 2010).
 
3
More research in ongoing to collect similar data for other countries subject to availability.
 
4
In Canada, the data on credit unions is not publically and easily available.
 
5
Although most savings and cooperative institutions are local—leaving them outside the scope of this study—they nevertheless depend on the services of much larger central institutions, which typically provide their network institutions with liquidity and represent the group on a consolidated basis for supervisory purposes (Desrochers and Fischer 2005). Recent empirical work has shown that the local institutions have comparable performance and efficiency characteristics to their commercial peers and have largely weathered the financial crisis unscathed. However, a number of Spanish savings banks and the German central institutions have been hit hard. For more discussion on the European cooperative and savings banks, see Ayadi et al. (2009, 2010).
 
6
This research was commissioned by the Greens—European Free Alliance Political Group in the European Parliament to understand how banks activities evolved pre and post great financial crisis.
 
7
In a second study commissioned by the Greens-European Free Alliance Political Group in the European Parliament to advice on how bank business models can enhance the European regulatory framework on credit institutions—Mainly the Capital Requirements Directive IV and Capital Requirements Regulation (CRD IV—CRR) proposed by the European Commission in July 2011 that translates the Basel III standards adopted by the Basel Committee on Banking Supervision (BCBS) as part of the regulatory response to the great financial crisis.
 
8
This research exercise was a result of collaboration between the Financial Institution and Prudential Policy (FIPP) at the Centre for European Policy Studies (CEPS) and the International Observatory on Financial Services Cooperatives at Hautes Etudes Commerciales (HEC) Montreal.
 
9
This research exercise was conducted by the International Research Centre on Cooperative Finance of HEC Montreal, building on all the three previous research exercise on the topic.
 
10
The sample includes the EEA+CH banking groups and banking subsidiaries of institutions from outside this region. The list of countries is in Appendix B.
 
12
Savings institutions (also called thrift institutions, or thrifts) include savings banks, savings associations (formerly savings and loan associations) and cooperative banks.
 
13
No data available on the caisses populaires.
 
14
Ayadi et al. (2011, 2012, 2016, 2017) and Ayadi and de Groen (2014).
 
15
See Milligan (1981) and references therein for an assessment of different clustering methods.
 
16
The hybrid method, as applied to the two samples of US banks and credit unions, develops as follows:
  • Step 1: Perform a non-hierarchical algorithm; specify a very large number of groups (e.g. 500 groups)
  • Step 2: A hierarchical algorithm (Ward) is implemented by treating these 500 centroids as observations. This clustering will suggest taking Q groups
  • Step 3: Perform a non-hierarchical clustering with Q clusters using the Q centroids, found in the previous step, as the starting seeds.
Note that, for the success of the application of the hybrid method to this study, indicators, which are ratios, are not standardised as is common in the literature, because they are already dimensionless. In addition, standardisation results in clusters that are less intuitive to interpret.
 
17
Evaluating a variety of cluster stopping rules, Milligan and Cooper (1985) single out the Calinski and Harabasz index as the best and most consistent rule, identifying the sought configurations correctly in over 90% of all cases in simulations.
 
18
See Everitt et al. (2001) for an introduction to cluster analysis and some of the practical issues in the choice of technical procedures.
 
Literature
Ayadi, R., E. Arbak and W.P. de Groen (2011), Business Models in European Banking: A pre-and post-crisis screening, Centre for European Policy Studies (CEPS), Brussels.
Ayadi, R., E. Arbak and W.P. de Groen (2012), Regulation of European Banks and Business Models: Towards a new paradigm?, Centre for European Policy Studies (CEPS), Brussels.
Ayadi, R. et al. (2016), “Banking Business Models Monitor 2015: Europe”, Montreal, International Research Centre on Cooperative Finance, HEC Montreal.
Ayadi, R. et al. (2017), “Bank and Credit Unions Business Models in the United States”, Montreal, International Research Centre on Cooperative Finance, HEC Montreal.
Calinski, R.B. and J. Harabasz (1974), “A dendrite method for cluster analysis”, Communications in Statistics, Vol. 3, No. 1, pp. 1–27.CrossRef
Desrochers, M. and K.P. Fischer (2005), “The Power of Networks: Integration and Financial Cooperative Performance”, Annals of Public and Cooperative Economics, Vol. 76, No. 3, pp. 307–354.CrossRef
Everitt, B.S., S. Landau and M. Leese (2001), Cluster Analysis, Fourth Edition, West Sussex: Wiley, John & Sons Ltd.
Groen, W.P. de and D. Gros (2015), “Estimating the bridge financing needs of the Single Resolution Fund: How expensive is it to resolve a bank?”, European Parliament, Brussels.
Milligan, G.W. (1981), “A Review of Monte Carlo Tests of Cluster Analysis”, Multivariate Behavioral Research, Vol. 16, No. 3.CrossRef
Milligan, G.W. and M.C. Cooper (1985), “An Examination of Procedures for Determining the Number of Clusters in a Data Set”, Psychometrika, Vol. 50, No. 2, pp. 159–179.CrossRef
Ward, J.H. (1963), “Hierarchical grouping to optimize objective function”, Journal of the American Statistical Association, Vol. 58, No. 301, pp. 236–244.CrossRef
Metadata
Title
Identification of Business Models
Author
Rym Ayadi
Copyright Year
2019
Publisher
Springer International Publishing
Book
Banking Business Models

Print ISBN: 978-3-030-02247-1

Electronic ISBN: 978-3-030-02248-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-02248-8_4