This paper presents the application of artificial intelligence technique to develop a Multi-Layer Perceptron neural network model for determining the status (solvent or insolvent) of commercial banks in Nigeria. The common traditional classification techniques based on statistical parametric methods are constraint to fulfill certain assumptions. When those assumptions fail, the techniques do not often give sufficient descriptive accuracy in classifying the status of the banks. However, a class of feed-forward architecture of neural network known as Multi-Layer Perceptron (MLP) is not constraint by those parametric assumptions and offers good classification technique that competes well with the traditional statistical parametric techniques. In this study, data were sourced from the central bank of Nigeria and financial reports of the commercial banks in Nigeria. The banks specific variable of age, history of merger, time, total assets and total revenue are used as the input variables to the neural network. The solvency or insolvency as status are the two possible outputs of the neural network for each commercial bank in the period of 1994-2015. The developed MLP neural network model has 5 input neurons, 3 hidden neurons and 1 output neuron. Sigmoid activation function for the hidden neurons and "purelin" transfer function for the output neurons were utilized in training the MLP neural network. The results demonstrate that MLP neural networks are a viable technique for status classification of commercial banks in Nigeria.
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