Data Driven Governance Models for Financial and Cyber Risk Integration: A Framework for Strengthening U.S. Financial System Resilience Through Integrated Analytics and Governance
Bernice Blay-Miezah1*, Ayomipo Alademehin2
Abstract
The U.S. financial sector loses more than $573 billion annually to combined financial fraud and cybercrime, with cybercrime losses growing at a compound annual rate of 27.8% since 2015 (Accenture, 2023; Federal Bureau of Investigation [FBI], 2023). Despite this trajectory, most financial institutions continue to manage financial risk and cyber risk in organizational silos, producing governance blind spots at precisely the boundary where these two threat categories converge. This paper introduces and operationalizes a Data-Driven Governance (DDG) framework for the integrated management of financial and cyber risk in U.S. financial institutions, grounded in design science research (DSR) methodology.
Following Hevner et al. (2004) design science principles, the research proceeds through four phases: problem identification and motivation, artifact design, demonstration, and evaluation. The artifact, the DDG framework, is constructed through a structured synthesis of 68 peer-reviewed and practitioner sources selected by systematic literature review using defined inclusion criteria. Framework demonstration draws on five anonymized institutional case studies conducted through semi-structured interviews and document analysis, coded against the DSR evaluation criteria of utility, quality, and efficacy. The framework synthesizes elements from NIST CSF 2.0, COSO ERM 2017, Basel III/IV, the FFIEC Cybersecurity Assessment Tool, and the FAIR quantification methodology.
Case evidence suggests an association between integrated DDG program adoption and observed improvements of 34 to 63 percent across key risk performance indicators relative to siloed program comparators, with the most pronounced associations in incident response speed, regulatory penalty reduction, and loss containment. Causal attribution is not claimed; these findings represent cross-case pattern evidence warranting prospective longitudinal validation. The paper identifies five priority research gaps, establishes a phased implementation roadmap, and derives legislative and regulatory recommendations actionable within the existing U.S. governance architecture.
Keywords:
Data driven governance, financial risk, cyber risk, integrated risk management, NIST CSF, COSO ERM, FAIR, FFIEC, systemic risk, machine learning, regulatory compliance
