Abstract
This paper presents the application of artificial intelligence technique to develop aMulti-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.
Highlights
A bank serves as a conduit through which stabilization policy is transmitted to the economy at large
In Nigeria, financial analyst assert that the recent Treasury Single Account (TSA) policy by the federal government would adversely affect the banking industry, a development that might lead to liquidity squeeze which may possibly cause another round of banks’ failure
The entries in the confusion matrix have the following meaning in the context of our study: θ1 is the number of correct predictions that an instance is negative, θ2 is the number of incorrect predictions that an instance is positive,θ3 is the number of incorrect of predictions that an instance negative, and θ4 is the number of correct predictions that an instance is positive
Summary
A bank serves as a conduit through which stabilization policy is transmitted to the economy at large. The phenomenon is almost as old as the industry In spite of their best endeavors, bank failure still occur in older banking societies like Britain, America, Spain, Indonesia, and many others till this moment. The banking sector in the third world economies has been grossly under managed when compared with their counterparts in the developed countries of the world. This has made it imperative for Nigerian banks to sanitize and restructure their operational processes so as to be in line with the global trends, and to survive the depressed economy
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