Monetary transaction fraud detection system based on machine learning strategies

Abstract

Fraud is a costly business problem which causes every organization to face huge loss. Fraud may lead to risk of financial loss and loss of the confidence of customers and stakeholders of the company. Cyber security teams and internal audit departments of most of the organizations try to monitor such fraudulent activities using traditional rule-based fraud detection systems. However, with the rapid adaptation of online financial transactions, it is more difficult to identify fraudulent activities by static methods and via data analysis. Further, as traditional rule-based fraud detection systems cannot dynamically adjust the rule set based on the behavioral changes of the fraudsters, there is a high possibility of detecting false positive alerts. The aim of this paper is to review selected machine learning techniques where it can be used to develop a fraud detection system which identifies fraudulent activities in financial transactions.