Modern enterprise financial accounting abnormal statistical data in the biopharma industry

Abstract

At present, abnormal data monitoring in biopharma industry is mainly realized through the difference between single characteristic attributes of data, which leads to low effective monitoring rate and high time cost. In order to improve the monitoring effect of modern enterprise financial accounting abnormal statistical data in biopharma industry, this paper studies a modern enterprise financial accounting abnormal statistical monitoring method based on data mining algorithm. After the preliminary processing of statistical data, the combination of information entropy and PCA dimension reduction is introduced to reduce the dimension of data. After the decision tree is established by C5.0 decision tree algorithm, the random forest is constructed to realize the abnormal monitoring of statistical data. The experimental results show that the monitoring rate of this method is more than 97%, the false alarm rate and time cost are low, and has good performance.