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Banks are interested in evaluating the risk of the financial distress beforegiving out a loan. Many researchers proposed the use of models based on theNeural Networks in order to help the banker better make a decision. Theobjective of this paper is to explore a new practical way based on the NeuralNetworks that would help improve the capacity of the banker to predict the riskclass of the companies asking for a loan. This work is motivated by theinsufficiency of traditional prevision models. The sample consists of 86Tunisian firms and 15 financial ratios are calculated, over the period from2005 to 2007. The results are compared with those of discriminant analysis.They show that the neural networks technique is the best in term ofpredictability.

Pr\'evision du risque de cr\'edit : Une \'etude comparative entre l'Analyse Discriminante et l'Approche Neuronale