Intelligent audit planning system for multiple audiors

Abstract

The auditor assignment planning process is regarded as a multi-agent multi-objective decision making problem that needs to balance between the "auditing firm``s profit and audit risk" and the "individual auditor``s preference". The balance between the firm``s profit to be optimized by linear programming model and the audit models are a highly useful forecasting method, but are deficient in the sense that they merely extrapolate past patterns in the data without taking into account the expected irregular future events. To overcome this limitation, forecasting experts in practice judgmentally adjust the statistical forecasts. Typical judgmental factors may be treated as outliers in statistical analysis. To automatic the judgmental adjustment process, neural network models are developed in this study. To collect the data for judgmental events, judgmental effects are filtered out of raw data. The main trend is captured by a neural network model using the filtered data, while judgmental effects are modeled by another neural network. Then the judgmental effects are additively adjusted. Performance of this architecture is tested in comparison with five other architectures: