Towards an ontology-based system for intelligent prediction of firms with fraudulent financial statements

Existing models for facilitating predictions of firms with fraudulent financial statements (FFS) are not semantics-based. The objective of this work was to design an intelligent web portal to serve as service provider for predicting which Greek manufacturing firms issue FFS. The portal serves as a meeting point among manufacturing firms, which are listed on the Athens Stock Exchange (ASE). It has been conceived to help users (e.g., chartered accountants) to detect FFS. The information contained in the portal is dynamically updated and it is related with financial statements. The portal helps users to find Greek manufacturing firms that provide FFS. For this purpose, the knowledge of the formal financial statements domain has been represented by means of ontology, which has been used to guide the design of the application and to supply the system with semantic capabilities. Additionally, the ontological component allows for defining an ontology-guided search engine. The reasoning engine of the proposed system executes logic rules related with well-established financial variables, which characterize a financial statement as fraudulent or not.