We are interested in forecasting bankruptcies in a probabilistic way. Specifcally, we com-
pare the classification performance of several statistical and machine-learning techniques,
namely discriminant analysis (Altman's Z-score), logistic regression, least-squares support
vector machines and different instances of Gaussian processes (GP's) -that is GP's classifiers,
Bayesian Fisher discriminant and Warped GP's. Our contribution to the field of computa-
tional finance is to introduce GP's as a potentially competitive probabilistic framework for
bankruptcy prediction. Data from the repository of information of the US Federal Deposit
Insurance Corporation is used to test the predictions