Data mining of financial crimes in cyberspace using neural network algorithms and decision tree

Undoubtedly, human societies have always faced the phenomenon of financial crimes as uninvited guests and are always exposed to harm. This research has been done using data mining tools and with the aim of increasing the efficiency and accuracy of financial crimes, with neural network and decision tree algorithms. Statement of Problem: The forthcoming issue indicates that the spread of cybercrime and, in parallel, the increase of recorded crime data, has confronted us with a large amount of data, each of which contains a large number of messages and information. If analyzed correctly, this data can help police organizations identify and track, as well as predict and prevent crime.

This study improves the efficiency and accuracy of financial crimes by data mining more than 4500 records of cyber financial crimes using data mining tools. The type of data mining method in this article is predictive. This study, by analyzing the data and crime variables taken from the FATA police database, using decision tree algorithms and neural networks, has created a new method to increase efficiency and accuracy in financial crimes, so that efficiency using From the neural network algorithm in the proposed method is 70. 11% and in the decision tree algorithm is 87.78%.

Using the existing tools in the field of data mining, it is possible to detect crimes or prevent their occurrence by using appropriate and optimal human resources of the police. It is hoped that police organizations can be assisted in identifying criminals and, more importantly, in predicting and preventing cybercrime, especially financial crime.