Published Paper Details:

All traditional economic control has been nullified due to the existence of counterfeit currency, everywhere across the globe. Counterfeit detection using Decision Tree, Random Forest, SVM, Naives Bayes, and other approaches have been used by researchers in the recent past to test fake banknotes. A survey of UCI repository showed that some features were missing and a certain amount of data was pre-processed before model training. A number of splits in the training and testing samples were created and the algorithms were assessed and trained on parameters such as accuracy, precision, recall and F1 score. The outcomes have shown the strength of specific models such as Random Forest that almost achieved satisfactory classification accuracy and can be used in real-life applications within banking kiosks and ATMs. The research further highlights with facts, the need for automated systems which can curb financial fraud and the methodologies required to put in place an effective counterfeit detection machine learning system.