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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.
