A Neural Network Approach to Estimating the Allowance for Bad Debt

Abstract

Neural network computer systems, which mimic some of the characteristics of the human brain, have been developed and may offer an alternative method for estimating the allowance for bad debt. These systems can predict what events may happen, analyze what did happen, and adjust the factor weights accordingly for the next set of event predictions. Thus, it is noteworthy to explore the use of neural networks to predict what a reasonable allowance for bad debt should be for an entity based on an array of interacting variables. Since, a neural network can incorporate both endogenous and exogenous variables; it is feasible to use such a system to develop a tool which may generate a better estimation of the allowance for bad debt than the traditional approaches. Our research findings indicate neural networks are better predictors of a company’s ending allowance for bad debt than regression. On a case by case basis, even when neural networks provide a less accurate estimate than regression, statistical analyses show neural networks to be a less volatile method and the predictions less likely to result in significant differences from the actual balance sheet allowance amount.