Warning signals for potential accounting frauds in blue chip companies – An application of adaptive resonance theory

With the constantly changing and deceptive strategies that can be concealed in complex of financial statements, traditional means of financial analysis is unable to detect these accounting frauds in advance. In order to detect new accounting frauds and find out the true meaning of off-balance sheet arrangements, we propose an easy and feasible method using an unsupervised learning system. In unsupervised learning, the training of the network is entirely data-driven and no target results are provided. The features that do not help in clustering can be removed. With unsupervised learning it is possible to learn larger and more complex relations than with supervised learning. In the demonstration, we extract four non-traditional warning signals using adaptive resonance theory, with Enron and WorldCom as prototypes to identify the possibility of potential fraud of a company that investors or analysts may be concerned with.