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The number of fraud occurrences in electronic banking is rising each year. Experts in the field of cybercrime are continuously monitoring and verifying network infrastructure and transaction systems. Dedicated threat response teams (CSIRTs) are used by organizations to ensure security and stop cyber attacks. Financial institutions are well aware of this and have increased funding for CSIRTs and antifraud software. If the company has a rule-based antifraud system, the CSIRT can examine fraud cases and create rules to counter the threat. If not, they can attempt to analyze Internet traffic down to the packet level and look for anomalies before adding network rules to proxy or firewall servers to mitigate the threat. However, this does not always solve the issues, because transactions occasionally receive a “gray” rating. Nevertheless, the bank is unable to approve every gray transaction because the number of call center employees is insufficient to make this possible. In this study, we designed a machine-learning-based rating system that provides early warnings against financial fraud. We present the system architecture together with the new ML-based scoring extension, which examines customer logins from the banking transaction system. The suggested method enhances the organization’s rule-based fraud prevention system. Because they occur immediately after the client identification and authorization process, the system can quickly identify gray operations. The suggested method reduces the amount of successful fraud and improves call center queue administration.

The adsorption and substitution of transition metal atoms (Fe and Co) on Au-supported planar silicene have been studied by means of first-principles density functional theory calculations. The structural, energetic and magnetic properties have been analyzed. Both dopants favor the same atomic configurations with rather strong binding energies and noticeable charge transfer. The adsorption of Fe and Co atoms do not alter the magnetic properties of Au-supported planar silicene, unless a full layer of adsorbate is completed. In the case of substituted system only Fe is able to produce magnetic ground state. The Fe-doped Au-supported planar silicene is a ferromagnetic structure with local antiferromagnetic ordering. The present study is the very first and promising attempt towards ferromagnetic epitaxial planar silicene and points to the importance of the substrate in structural and magnetic properties of silicene.