A multicriteria approach in detecting falsfield financial statements: Evidence from small and medium UK companies
2005
Afrokh, M.
The purpose of this paper is to present a multicriteria classification approach for the purpose of detecting Falsified Financial Statements (FFS) emitted by UK companies. The study relies on a sample of 1146 small and medium manufacturing firms (232 with (FFS) and 914 with non-FFS) over the period 1998-2003. The model is developed using the UTADIS (UTilites Additives DISscriminantes) method, which is based on preference disaggregation analysis. The application of other multivariate techniques, such as Logit and Discriminant Analysis were deemed important for comparison purposes. The results indicate that the model developed by the multicriteria method is as much as 73,20 per cent accurate in detecting FFS, which outperforms those developed with the traditional multivariate techniques. Moreover, this finding can be a crucial tool for auditors and assessors to identify the factors that are most likely associated with FFS.
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