American Journal of Theoretical and Applied Statistics 2017; 6(4): 214-220 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20170604.18 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online) Determining Solvency and Insolvency of Commercial Banks in Nigeria Yahaya Haruna U., Abdulkarim Muhammad Department of Statistics, University of Abuja, Abuja, Nigeria Email address: yahagumau@gmail.com (Yahaya H. U.), mmmhammmad@gmail.com (Abdulkarim M.) To cite this article: Yahaya Haruna U., Abdulkarim Muhammad. Determining Solvency and Insolvency of Commercial Banks in Nigeria. American Journal of Theoretical and Applied Statistics. Vol. 6, No. 4, 2017, pp. 214-220. doi: 10.11648/j.ajtas.20170604.18 Received: April 12, 2017; Accepted: April 26, 2017; Published: July 26, 2017 Abstract: 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. Keywords: Artificial Intelligence, Multi-Layer Perceptron, Neural Network, Solvent, Insolvent, Transfer Function 1. Introduction A bank serves as a conduit through which stabilization policy is transmitted to the economy at large. Therefore, the safety and soundness of banking industry is a very crucial prerequisite for economic stability, development and growth of any nation. Meanwhile, arising from a phenomenal increase in the occurrence of financial crises, efforts have been intensified to predict the health of banking system, with a view to empowering officials and market participants to recognize the symptoms of a financial crisis at an early stage. 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. Bank failure is neither new nor peculiar to Nigeria. In fact, 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. However, 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. Conceptually, solvency of a bank is a static issue in that it characterizes a bank or a banking system at a point in time. Predicting financial unsoundness is, however, a dynamic exercise. A proactive measure of a banking system’s health conditions should capture both the quantitative and qualitative determinants of bank insolvency. Bank raises funds by attracting deposits, borrowing money in the inter-bank market, or issuing financial instruments in the money market or a securities market. The bank then lends out most of these funds to borrowers. However, it would not be prudent for a bank to lend out all of its balance sheet. It must keep a certain proportion of its funds in reserve so that it