Progress in Computing Applications Koodichimma Chinazom Ibe-Ariwa 15 2 2026 https://doi.org/10.6025/pca/2026/15/2/49-65 https://www.dline.info/pca/fulltext/v15n2/pcav15n2_1.pdf As digital payment ecosystems expand globally, financial institutions increasingly rely on advanced machine learning algorithms to detect cyber-enabled fraud. However, optimizing feature engineering to minimize false positives remains a critical operational challenge. This study empirically evaluates the predictive efficacy of static device security signals and absolute transaction amounts in identifying fraudulent financial transactions. Utilizing a quantitative, cross-sectional design, we analyzed the Financial Transaction Intelligence Dataset 2026, comprising 10,000 transactions across 1,500 devices and 773 customers. The methodological approach incorporated descriptive statistics, cross-tabulation, Chi-square tests, and nonparametric distribution comparisons to rigorously assess device telemetry and monetary variables across multiple analytical domains. The findings reveal that classic device-security flags including trusted status, rooted or jailbroken states, and biometric enablement exhibit statistically insignificant associations with actual fraud outcomes in this population. Furthermore, transaction amounts demonstrate a near-uniform distribution lacking discriminative power, with fraud rates remaining remarkably flat across all monetary bins and showing near-zero correlation with critical behavioral risk covariates. These results provide actionable insights for risk analysts developing cloud-hosted analytics platforms. Ultimately, this research establishes that absolute transaction amounts and static device attributes are weak, orthogonal predictors of fraud. To enhance operational efficiency and detection accuracy, financial institutions must pivot away from static, rule based thresholds and instead prioritize dynamic behavioral and contextual features within advanced multivariate machine learning models.