Bernardo P. Marques

This paper identifies banks born with a digital business model (‘neobanks’) and examines their performance and riskiness vis-à-vis traditional peers. We propose a novel approach to identify neobanks, based on non-financial hand-collected data, and identify 65 neobanks operating in Europe. We show that neobanks perform worse than their traditional peers, while recording a similar level of risk. Namely, neobanks charge higher interest income, record higher impairment charges, and face higher non-staff expenses. Further analysis suggests the presence of economies of scale and scope in digital banking. Our findings are robust to endogeneity concerns and changes to our baseline specification.

We assess the impact of adopting a transnational supervisor on the distance-to-distress and credit risk of large and complex banks, exploring the establishment of the Single Supervisory Mechanism (SSM) in 2014 as a quasi-natural experiment. Using a differences-in-differences approach, we compare SSM banks vis-à-vis banks with a similar size and complexity operating in European countries outside the SSM. Our results suggest that adopting a transnational supervisor increases the distance-to-distress, particularly for banks operating in countries with larger banking sectors, higher market concentration and greater supervisory discretion. We also show that SSM banks reduced loan loss reserves and NPLs significantly more than non-SSM banks, but only among the most capitalized banks – which is consistent with the notion that well-capitalized banks are better able to weather haircuts induced by credit risk reduction initiatives. Interestingly, we find that SSM banks from countries with greater supervisory discretion saw their NPLs increase in the first years of the SSM, which could reflect the elimination of national idiosyncrasies in credit risk accounting. In general, the evidence presented in our paper suggests that transnational supervision bears a superior ability to increase the distance-to-distress, reduce credit risk, and harmonize supervisory practices among large and complex banks.

This paper examines which business choices are more likely to increase the profitability and distance to distress of banks, and whether changing business model pays off. We find that the profitability and distance to distress increase with the use of customer deposits and equity, and decrease with size; also, the top performers tend to have a high relationship banking orientation and/or operate a retail focused business model. Furthermore, we document that income diversification only bears a positive impact on the distance to distress of banks highly focused on relationship banking, and size only bears a negative effect on the profitability of these banks as well; additionally, only banks with a low relationship banking orientation significantly benefit from customer deposits. With respect to the effects of business model changes, we find that shifts from the retail diversified funding model to either the retail focused or the large diversified models improve profitability in the medium term. Finally, we find evidence that large diversified banks benefited from internal capital markets during the twin financial crisis by tapping into low-cost funding from subsidiaries. Our results are robust to changes to our baseline model that account for endogeneity and persistency issues.

Summary: The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.