A rule-based machine learning model for financial fraud detection

Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively

Land Treatment of Wastewater

© 2012 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. Land treatment of wastewater is increasing considerably worldwide to achieve both environmental and treatment reuse benefits. This chapter includes brief descriptionabout land treatment systems: specifically, slow-rate (SR) systems, overland (OF) systems, and soil aquifer treatment (SAT) systems. Moreover, discussed about wastewater constituents and removal mechanism and pretreatment and storage design, requirements, monitoring, management as well as in-depth calculation process are discussed