Building a Scale for Internet Fraud Detection Using Machine Learning

Abstract

The widespread digitalization of the modern society and the development of information technology have increased the number of ways in which financial institutions and potential consumers of financial services can interact. At the same time, the advent of new financial products inevitably leads to a rise in threats, and the use of information technology facilitates the constant “improvement” of fraud schemes and unfair business practices, which negatively affect both the financial market as a whole and its individual participants such as financial institutions and their clients. With the development of the modern society, most financial transactions, including the fraudulent ones, have moved to the Internet. When services are provided remotely, it is more difficult to trace and prosecute the beneficiary. However, there are still ways to stop fraudulent activity, but they are associated with high costs of monitoring and analysis of huge amounts of unstructured information (BigData) available on the Internet, which takes a great amount of time and effort. A solution to illegal activity detection in financial markets is based on open data intelligence, machine learning, and systems analysis. This paper examines certain types of financial services provided on the Internet among which fraudulent activities are most common. In order to identify illegal financial services, some criteria are developed and grouped based on their contribution to the decision-making process. The main result of this study is the construction of a scale for a complex indicator, which is used to build a mathematical model based on the developed criteria and machine learning methods for determining the degree of illegality of online financial services.