Multi-instance Learning for Predicting Fraudulent Financial Statements

This paper explores the effectiveness of multi-instance learning techniques in detecting firms that issue fraudulent financial statements (FFS). For this reason, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms. The results show that MIBoost algorithm with decision stump as base learner had the best accuracy.