Detecting Financial Statements’ Fraud Using Relational Trend Analysis (RTA)

Abstract

Severe criticisms trail the horizontal and vertical analysis methods of detecting financial statements fraud (FSF) for general lack of precision. This paper presents the relational trend analysis (RTA) technique to correct this defect. The study adopted the desk research method using a rehashed five-year financial statement data. The study employed tables and simple MS Excel commands to perform periodic relational analysis comparing the probabilities of the current period occurrence of an item in a group with similar probabilities of same in the base period. The results showed that RTA produced indices which highlighted not only the problem area of the financial statements but also the source(s) of the problem(s). The findings also indicated that RTA overcame the deficiencies of its forerunners with greater precision eliminating the need for advanced mathematical modeling.