Development of Comparative Fake Transactions Alert Detection Models Using Machine Learning

Abstract

Fraudulent payment evidences are the current techniques criminals employ to defraud businesses and small scale enterprises. Researchers have processed transactions textual data however, there is still the challenge in the ability to differentiate between legitimate and fake transaction alerts. In Nigeria, fake transaction alerts pose significant challenges for financial institutions and individuals losing on their hard earned assets, citizens are sceptical on electronic transactions and several Point of Sale (PoS) businesses have fallen victims. Hence, this study was aimed at the development of a better fake transaction alert detection model to distinguish fake transaction alerts. Artificial Commercial Data for Fraudulence Discovery was collected from Kaggle website. The collected data was pre-processed. Data imbalances were handled. Support Vector Machine (SVM) and Random Forest (RF) algorithms were ensembled to simulate fake transactions alert detection models using MATLAB programming. They were trained and tested with 70% training and 30% testing datasets, respectively. Performance evaluation was done on RF and SVM classifiers using exactness, precision, recollection, F-measure as benchmarks. The data record employed for this study had 1,048,575 transactions alerts. At performance evaluation, RF model had exactness, precision, recollection and F-measure values, 97.6, 97.48, 97.54 and 97.51%, respectively. Its RMSE was 0.02376. Moreover, SVM model had exactness, precision, recollection and F-measure values of 96.1, 96.88, 98.38, and 97.63%, respectively, it has RMSE of 0.03911. Random Forest algorithm was more suitable for the development of the fake transactions alert detection because it had higher performance. This model could be adopted by financial related institutions.