Biblio

Criminals do money laundry to hide the illegitimate sources of money to show as if their money is of a legitimate source. Money laundry has many stages that money flow has to go through to finally look as if it is of a legitimate source, rule-based systems are implemented across different banks to detect structuring which is one technique of the layering stage which sophisticated criminals can evade by unsatisfying the check rules. In this work, graph database and graph data mining are to be used to overcome this limitation, the proposed technique does this by plotting the whole transactional monetary flow of entities doing money transfers between each other as one large graph database and then detecting clusters of entities interacting with each other, afterwards detection of the most influential node (intended destination) which we consider the destination to which huge amounts of money is intended to flow to (criminal`s account) using PageRank algorithm and eventually detecting all members (Smurfs) of participated in the paths leading to that destination, a technique that would be hard to implement using traditional RDBMS in contrary to Graph DB, our results have proven correct detection of clusters as well as the final destination of the monetary flow (criminal`s account).

Security and making trust is the first step toward development in both real and virtual societies. Internet-based development is inevitable. Increasing penetration of technology in the internet banking and its effectiveness in contributing to banking profitability and prosperity requires that satisfied customers turn into loyal customers. Currently, a large number of cyber attacks have been focused on online banking systems, and these attacks are considered as a significant security threat. Banks or customers might become the victim of the most complicated financial crime, namely internet fraud. This study has developed an intelligent system that enables detecting the user's abnormal behavior in online banking. Since the user's behavior is associated with uncertainty, the system has been developed based on the fuzzy theory, This enables it to identify user behaviors and categorize suspicious behaviors with various levels of intensity. The performance of the fuzzy expert system has been evaluated using an receiver operating characteristic curve, which provides the accuracy of 94%. This expert system is optimistic to be used for improving e-banking services security and quality.