Open AccessNeural network data processing for financial insolvency fore-casting of the water supply enterprises
Author(s) -
G O Krylov,
A S Vorobeva,
M.A. Nikolaeva
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012093
Subject(s) - artificial neural network , bankruptcy , multilayer perceptron , artificial intelligence , computer science , insolvency , boosting (machine learning) , machine learning , identification (biology) , finance , business , botany , biology
This work is devoted to creation of neural network methods with the increased stability and reliability for bankruptcy forecasting. In this paper, we investigate the performance of four different neural network (NN) methods for bankruptcy prediction of the resource provisioning system enterprises. Two methods based on NN-ensembles of classifiers (boosting-ensembles and bagging-ensembles) and another two tested methods are stand-alone classifiers (multilayer perceptron (MLP) and network of radial basic functions). We defined that bagging-ensembles of neural networks have advantage in comparison with classical - stand-alone classifiers. Developed bagging-ensemble of neural networks is currently used to the sphere of financial monitoring for identification of signs of financial insolvency of the water supply enterprises and assessment of risk of social tension in regions.