基于大数据和卷积神经网络的财务数据异常告警方法

Abnormal Alarm Method of Financial Data Based on Big Data and Convolutional Neural Network

为提高财务数据处理的效率和准确性,研究基于大数据和卷积神经网络的财务数据异常告警方法。首先,将样本数据输入数据处理系统,通过对回归向量进行分类来确定数据方向;其次,对异常财务数据特征进行分类后,根据分类结果对异常数据进行预警;最后,进行实验对比分析。通过财务数据预处理、基于大数据的异常特征提取、基于卷积神经网络的异常特征分类以及财务数据异常告警,完成财务数据异常告警。实验结果表明,该方法的财务数据异常告警结果较为准确,优于对照组。

To improve the efficiency and accuracy of financial data processing,a financial data anomaly warning method based on big data and Convolutional Neural Networks is studied.Firstly,input the sample data into the data processing system and determine the data direction by classifying the regression vectors.Secondly,after classifying the characteristics of abnormal financial data,early warnings are given based on the classification results.Finally,conduct experimental comparative analysis.Complete financial data anomaly alerts through financial data preprocessing,anomaly feature extraction based on big data,anomaly feature classification based on Convolutional Neural Networks,and financial data anomaly alerts.The experimental results show that the financial data abnormal alarm results of this method are more accurate and superior to the control group.