Automatic Analysis Method of Audit Data Based on Neural Network Mapping

Abstract

The work solves the problem of increasing the efficiency and effectiveness of analytical audit procedures by automating data comparison by neural network mapping. The object of the research is the process of auditing the compliance of payment sequences and supply for raw materials. The vectors of signs for the objects of the sequences of payment and supply of raw materials are generated, which are then used in the proposed method. The created method, in contrast to the traditional one, provides for a batch mode, which allows the method to increase the learning rate by an amount equal to the product of the number of neurons in the hidden layer and the power of the training set, which is critically important in the audit system for the implementation of multivariate intelligent analysis, which involves enumerating various methods of forming subsets analysis. The urgent task of increasing the audit efficiency was solved by automating the mapping of audit indicators by forward-only counterpropagating neural network. A learning algorithm based on 𝑘-means has been created, intended for implementation on a GPU using CUDA technology, which increases the speed of identifying parameters of a neural network model