Cyber-Financial Risk Analytics : Integrating Business Intelligence with Threat Modeling

Cyber-financial risk has become a strategic concern for modern financial institutions such as digital banking, fintech platforms, cloud ecosystems, and interconnected payment infrastructures expand the attack surface for fraud, disruption, and data compromise. This study proposes an integrated cyber-financial risk analytics framework that combines Business Intelligence (BI) with structured threat modeling to improve risk visibility, prioritization, and decision support. The framework unifies transaction data, cybersecurity telemetry, vulnerability information, and threat intelligence within a single analytical pipeline. BI components support descriptive, diagnostic, and predictive analysis through anomaly detection, trend analysis, and risk scoring, while threat modeling components map assets, trust boundaries, attack paths, and control weaknesses using structured methodologies such as STRIDE and DREAD. Their integration enables a more contextualized assessment of cyber exposure by linking technical threat scenarios to measurable financial consequences, operational disruption, and governance requirements. The paper further introduces a composite cyber-financial risk formulation that incorporates threat likelihood, vulnerability severity, financial impact, and detection latency. A PRISMA-guided literature synthesis and simulated validation tables are used to demonstrate how the proposed approach strengthens monitoring, improves mitigation prioritization, and supports executive-level risk reporting. The findings indicate that integrating BI and threat modeling can shift financial institutions from reactive security operations toward predictive, intelligence-driven cyber resilience. The framework also supports compliance mapping, audit readiness, and continuous improvement.