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Abstract

Zero Trust architectures strengthen continuous verification and least-privilege enforcement, but general-purpose implementations do not explicitly treat financial exposure and transaction-level operational cost as first-class decision inputs. This paper presents SecureBank, a financially-aware Zero Trust architecture that combines identity and device trust, contextual consistency, financial risk, business-aligned segmentation, and policy actions such as allow, step-up verification, and block. The empirical study validates the architectures financial-risk decision component rather than claiming end-to-end production validation of the complete architecture. A public credit-card fraud dataset containing 284,807 transactions was divided into independent training, validation, and test partitions using a 60/20/20 stratified design. A class-weighted logistic regression model produced a risk score. The operational threshold was selected only on the validation partition using an asymmetric cost function in which a false negative was assigned a cost 50 times greater than a false positive, and the frozen threshold was then evaluated once on the independent test partition. The final test set contained 56,962 transactions and 99 fraud cases. At the validation-selected risk-score threshold of 0.9991, the model achieved ROC-AUC 0.9664, average precision 0.7119, precision 71.03%, recall 76.77%, F1-score 0.7379, and a falsepositive rate of 0.0545%. These results provide reproducible component-level evidence that financially asymmetric operating costs can be integrated into a Zero Trust decision process while keeping the scope of the empirical claims explicit.