Application of neural networks for detecting erroneous tax reports from construction companies

In this study we develop an automatic detection model for discovering erroneous tax reports. The model uses a variety of neural network applications inclusive of the Multi-Layer Perceptrons (MLPs), Learning Vector Quantization (LVQ), decision tree, and Hyper-Rectangular Composite Neural Network (HRCNN) methods. Detailed taxation information from construction companies registered in the northern Taiwan region is sampled, giving a total of 5769 tax reports from 3172 construction companies which make up 35.98% of the top-three-class construction companies. The results confirm that the model yields a better recognition rate for distinguishing erroneous tax reports from the others. The automatic model is thus proven feasible for detecting erroneous tax reports. In addition, we note that the HRCNN yields a correction rate of 78% and, furthermore, generates 248 valuable rules, providing construction practitioners with criteria for preventing the submission of erroneous tax reports.

Research highlights
► We provide a model to detect erroneous tax reports.
► We find the upper and lower bounds for each account to facilitate auditing.
► The model yields the rules to improve review efficiency.