PE-EDD: An efficient peer-effect-based financial fraud detection approach in publicly traded China firms

Abstract

Financial fraud arises from the exaggeration of business interests, and an accurate detection or prediction is a useful tool for both corporate management and capital market systems. A collection of computer technologies has been made on this problem so far, and one of the most important solutions is unsupervised learning algorithms. Among them, most approaches work by analysing the internal relations in financial data and finding a new description of non-fraud firms. However, current studies focus a lot on the geometry attribute of financial data, while overlooking the obvious behaviour patterns and peer effects among firms. This has limited the accuracy of representation and furthermore the detection performance. In this work, a very general class of functions is allowed to represent firms, constraining them by peer effects between firms and presenting an error-distribution-based financial fraud firm detection approach. Experimental results have shown great performance of the proposed approach.