Device Security Signals and Transaction Amounts as Weak Predictors of Fraud: An Empirical Analysis of a Financial Transaction Dataset
Koodichimma Chinazom Ibe-Ariwa
Progress in Computing Applications, Vol. 15, No. 2, 2026.
DOI: https://doi.org/10.6025/pca/2026/15/2/49-65
Link: https://www.dline.info/pca/fulltext/v15n2/pcav15n2_1.pdf
Abstract:
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.