基于灰色聚类优化模型的审计检查风险评价

Audit detection risk assessment based on optimization model of grey clustering

基于灰色聚类的优化模型,研究了审计检查风险评价问题。通过最大化风险评价指标白化权函数值的离差平方和,构建了审计检查风险评价模型,从而更好地区分聚类信息,使得审计检查风险评价的效果更加显著。数值算例表明,灰色聚类优化模型可以根据审计对象的自身状态数据确定各风险评价指标的权重,有效减少指标权重的主观性导致的评价结果偏差,具有较优的适用性。

This paper investigates the assessment problem of audit detection risk based on the optimization model of grey clustering.By maximizing the quadratic sum of the deviations of the index whitenization weight function values,the clustering information is better distinguished,making the effect of audit detection risk assessment more significant.Numerical example shows that the grey clustering optimization model has better applicability,because this model can determine the weight of each risk assessment index according to the audit object’s own state data,and effectively reduce the bias of the assessment result caused by the subjectivity of the index weight.