Application of Metalearning to Detect Financial Statements Fraud in Organisations

Abstract

Financial statements fraud detection techniques have been classified into various categories and this study will focus on one of them: artificial and computational intelligence techniques. One of the major challenges facing financial statements fraud detection is that financial data needed to train detection models, is hugely unavailable due to regulations that prohibit the transmission and distribution of the highly confidential data. The aim of this research is to come up with a fraud detection technique that overcomes the challenge faced in fraud detection of unavailability of financial data. This research was conducted through first finding out which features of financial statements are key to financial statements fraud detection. An experiment was also done to find out which hybrid algorithm performs best at detecting fraud in financial statements. The features standing out from the first experiment would form the feature set, and a model built on the algorithm that performs best. The results showed that 3 of the top features were related to the assets of the business and out of all the 20 features identified, ones dealing with assets were 7 in total. Ensemble methods showed great accuracy when it comes to classification tasks that have a high dimensionality as all the methods scored 80% and the best performing being Reptile at 87.86%. The model built on reptile algorithm and trained using the identified feature set had an accuracy of 86.33%. The key limitation of this research is the inaccessibility of financial statements data in the public domain. It is even harder to find these statements where fraud has occurred as efforts will have been put into place to conceal the presence of such fraud. The project concludes that metalearning algorithms are performing better than other algorithms where the data to train is limited as is in this case, and that feature selection is important to increasing the accuracy of the model. This study contributes to the knowledge of how to accurately detect fraud occurrence in financial statements by providing an insight as to which features of these statements are more key in indicating the possibility of fraud. The research also shows how key metalearning algorithms are in this new technological era, this being backed up by the accuracy of the two metalearning algorithms as they emerged the top two from all the hybrid algorithms identified. Key words: Reptile, financial statements fraud, fraud detection, metalearning, neural networks.