Financial statement fraud detection using supervised learning methods.

Abstract

A large number of potential indicators (explanatory variables) of financial statement fraud are investigated in order to study which are the most useful to detection models. These include financial information, non-financial information and comparisons of the two.Empirical support has been found for both financial and non-financial explanatory variables,including new variables. A new framework, the Fraud Detection Triangle, is also developed to assist in the selection of explanatory variables for financial statement fraud detection models. Empirical evidence is provided to support the use of this new framework. Using models developed in this research, financial statements can be automatically classified as either fraudulent or legitimate, as well as being ranked according to their likelihood of being fraudulent. This information can be used to improve early detection,which would mitigate the costs of fraud and help deter it from occurring by increasing the probability of being detected. Beneficiaries of this information include auditors, investors,financiers, employees, customers, suppliers, regulators, company directors and the financial markets as a whole through improved integrity and allocation of resources.