Credit is a must in financial systems. For all financial institutions, whose role is to allocate credit, it is necessary to fully understand the risk behind it and to correctly decide who to give credit and who not. To do so, they make use of credit scoring, which is one of the most successful application of statistical and operational research modelling in finance. The aim of this thesis is to combine supervised and unsupervised machine learning models to predict the probability of default of a set of individuals who asked a loan to a bank, and to correctly classify them according to their individual propensity to default.