审计重大错报风险评估系统的构建与应用

摘要

随着全球一体化进程的加快和经济系统复杂程度的提高,我国企业重大错报风险问题日益突出,注册会计师在审计过程中需要对企业重大错报风险进行客观、科学、高效的评估.利用BP神经网络的自学习能力、自适应能力和容错能力,建立了基于BP神经网络的重大错报风险评价模型.在理论分析的基础上,针对19家上市公司进行实证性分析,结果表明,基于BP神经网络的审计重大错报风险评价与专家评价结果具有很好的一致性,能有效降低人为因素对审计重大错报风险评价的影响,从而提高审计质量,节约审计成本,降低审计风险.%Material misstatement risk assessment is an important link in the implementation of modern risk - oriented auditing. The paper constructs material misstatement risk assessment model based on BP neural network with the self -learning, adaptive and fault - tolerant ability of BP neural network. The results show the consistency between the credit risk assessment on BP neural network and expert evaluation. The model can reduce the effect of human factors on the material misstatement risk assessment, improve audit quality, retrench audit cost, and reduce audit risk.