One of the problems still gaining a great attention in finance is the bankruptcy forecasts. The problem of efficient bankruptcy prognosis is of great interest both to scientists and practitioners. Recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVM) to the bankruptcy prediction problem in an attempt to suggest a new model and classification with better explanatory power and stability. Carried out experiments have shown a very promising results of SVM for bankruptcy prediction in terms of predictive accuracy and adaptability. One of the problems still gaining a great attention in finance is the bankruptcy forecasts. The problem of efficient of bankruptcy prognosis is of great interest both to scientists and practitioners. Recent studies have shown that machine learning techniques achieving better performance than traditional statistical ones. This paper applies support vector machines (SVM) to the bankruptcy prediction problem in an attempt to suggest a new model and classification with better explanatory power and stability. Carried out experiments have shown a very promising results of SVM for bankruptcy prediction in terms of predictive accuracy and adaptability .