Relational Trend Analysis: A Simple and Effective Way to Detect Financial Statements Fraud

Horizontal and vertical analysis are among the numerous financial statements fraud (FSF) detection methods which have been severely criticized for their apparent imprecision. This paper intends to reverse this through a new technique which combines the two to produce a joint relational trend analysis (RTA). The study adopted the desk research method using a rehashed five-year financial statement data and employed tables and simple MS Excel commands to perform periodic relational analysis by comparing the probabilities of the occurrence of a percentage of an item in a group for the current period with similar probabilities of the same item and group for the base period. The new technique produced indices which highlighted not only the problem area of the financial statements but also their source(s). The findings proved that RTA overcame the deficiencies of its fore-runners by offering promising results with greater precision. RTA’s easy to apply method makes it possible to timely detect FSFs without the need for advanced mathematical modeling.