Cybersecurity Talent Gap Prediction Models for Financial Infrastructure Protection

Abstract

Cybersecurity staffing in financial market infrastructures (FMIs) rarely keeps pace with the threats those systems face. Payment rails, trading platforms, and digital banking services need defenders who can respond faster than risk accumulates, yet most institutions still plan their workforce reactively, a vacancy opens only after an incident, an audit finding, or a technology migration has already exposed the gap. We propose a data-driven approach to forecasting cybersecurity talent shortages in financial infrastructure. The model draws on threat intelligence, vulnerability signals, incident telemetry, human resources information systems (HRIS), vacancy aging, training capacity, and the NIST NICE cybersecurity workforce taxonomy; it scores shortage criticality role by role over a rolling horizon and converts those scores into concrete hiring, reskilling, and sourcing decisions. The pipeline is reproducible and combines time-series baselines, gradient-boosted trees, and sequence models in an interpretable ensemble, and we widen the evaluation with an ablation study, ROC analysis, a feature-correlation study, and a role-level error decomposition. Because direct institutional data could not be shared, testing relies on an anonymized synthetic dataset built to mirror an FMI environment. On a 6-month holdout, the ensemble cut shortage-forecasting error by 23.8% against a seasonal naive baseline and classified critical shortages with an F1-score of 0.842 and an ROC-AUC of 0.93. Feature attribution points to vacancy aging, critical vulnerability volume, incident severity, and NICE task density as the strongest predictors of future shortage. The paper closes with a reference architecture, an expanded survey instrument, a feature schema, an evaluation protocol, governance controls, a NICE role crosswalk, and a deployment roadmap for institutions that want to plan cyber workforce resilience before the gap appears, not after.