Using active learning methods for predicting fraudulent financial statements

Abstract

Detection of Fraudulent Financial Statements (FFS), or simpler fraud detection problem, refers to the falsification of financial statements with the aim either to demonstrate larger positive rates, such as assets and profit, or to conceal negative factors, such as expenses and losses. Since the expansion of contemporary markets and multinational trade are real phenomena, production of large volumes of data under which the operation of the current firms is facilitated constitutes a resulting consequence. Thus, analog upgrade of the antifraud mechanisms should be adopted, enabling the introduction of Machine Learning tools in the related field. However, because of the inability to collect trustworthy datasets that describe the corresponding ratios of a firm that has conducted fraud actions, strategies that exploit the existence of a few labeled instances for discovering useful patterns from a pool of unlabeled data could be proved really efficient. In this work, comparisons of algorithms that operate under Active Learning theory against their supervised variants are being conducted, using data extracted from Greek firms. To the best of our knowledge, this is the first study that uses Active Learning for predicting FFS. The obtained results prove the superior performance of the corresponding active learners.