  In this paper, we investigate the credit risk in the loan portfolio of banks
following different business models. We develop a data-driven methodology for
identifying the business models of the 365 largest European banks that is
suitable for very granular harmonised supervisory data. Our dataset allows us
to take into account the full range of the activities in which banks are
involved. The proposed method combines in an optimal way data clustering,
dimensionality reduction and outlier detection. We identify four business
models and exclude as 'outliers' banks that follow idiosyncratic business
models. Furthermore, empirical evidence is provided that banks following
different business models differ significantly with respect to the credit risk
they undertake in their loan portfolios. Traditional commercial banks are
characterized by the lowest levels of credit risk while the loan portfolios of
securities holding banks are riskier compared to the other banks.
