Counterfeit banknote identification based on outliers detection methods

Counterfeit banknotes in circulation in the economy are a long-standing and recurring problem. Technological improvements are applied to produce increasingly convincing counterfeit banknotes. In contrast, sophisticated strategies to identify counterfeit notes remain an up-to-date and fascinating research topic of great interest. The literature presents several techniques for detecting counterfeit banknotes. In this study, outlier detection methodologies are presented as adapted mechanisms to assist in the fight against counterfeit banknotes. This study addresses and tests multivariate outlier detection techniques' efficiency in identifying counterfeit banknotes. A comprehensive computational experiment proves the great adaptability of the methods to multivariate outliers in the identification of counterfeit banknotes. In particular, the Data-driven Cluster Analysis Method (DDCAM) technique presented promising and optimistic results through several quality and performance measurement metrics, suggesting a hopeful future for detecting counterfeit banknotes.