Describes how a statistical Taguchi approach and a backpropagation neural network model were devised to evaluate the effect of various parameters and identify the optimal parameter setup values in a gas‐assisted injection moulding process. In applying the Taguchi approach, an L 18 orthogonal...

Owing to some uncontrollable factors, only a portion of an experiment can be completed. Such incomplete data are generally referred to as censored data. Conventional approaches for analysis of censored data are computationally complicated. In this work an effective means of applying neural...

Investigates the possibility of applying artificial intelligence to solve practical auditing problems faced by the public sector, namely the tax auditor of the Internal Revenue Services, when targeting firms for further investigation. Suggests that organizations which incorporate an operational...

Introduces a new analytical review procedure that measures the degree to which a data set’s digit distribution deviates from a Benford digit distribution. This deviation can indicate potential manipulation and can be used to signal the need for further audit testing. An artificial neural...

Explores the appropriateness of quantifying focus group discussions. Basic problems involved in quantification of text are addressed. Prior approaches are discussed and briefly evaluated. A new neural network algorithm for analyzing excerpts from in‐depth interviews is presented. Keywords...

Purpose – Quantitative methods known as scoring models have been traditionally developed for credit granting decisions using statistical procedures. The purpose of this paper is to develop a non‐parametric credit scoring model for micro enterprises that are not maintaining balance sheets,...

Explains the strengths and weaknesses of neural networks and uses them to analyse racial patterns in 1994 mortgage (conventional and FHA) data for the city of Atlanta (USA). Admits the difficulty of interpreting the results of neural network models but suggests that race does have an impact on...

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models,...

Summarizes the main hypotheses used in previous research on dividend policy and reports a study of patterns in dividend payouts/growth using neural networks as a data mining technique. Discusses the properties of neural networks, recognizes that they are unsuitable for hypothesis testing and...