Integrating Cyber Risk Metrics into Fintech Product Lifecycle Management

Authors

  • Arooj Hassan Department of Project Management and Supply Chain Management, Bahria University Islamabad
  • Muhammad Ahsan Khan Syed Babar Ali School of Science and Engineering (SBASSE), Lahore University of Management Sciences (LUMS)
  • Malik Arfat Hassan Department of Computer Science, Comsats University Islamabad, Attock

Keywords:

Fintech, Cyber Risk Metrics, Product Lifecycle Management, Cybersecurity Governance, Risk Analytics, Predictive Security

Abstract

The proliferation of financial technologies (Fintech) has revolutionized financial services through enhanced accessibility, automation, and innovation. However, the increasing reliance on interconnected digital infrastructures has also amplified exposure to cyber risks. This study explores the integration of cyber risk metrics into the Fintech Product Lifecycle Management (PLM) framework to create a proactive, security-driven development paradigm. Traditional PLM approaches in Fintech primarily emphasize product innovation, regulatory compliance, and customer-centricity, often neglecting cybersecurity until post-deployment phases. This paper proposes a comprehensive model that embeds quantifiable cyber risk indicators across all stages of the product lifecycle—conceptualization, design, development, deployment, and maintenance—ensuring continuous threat visibility and resilience enhancement. By synthesizing methodologies from cybersecurity analytics, risk management standards (ISO/IEC 27005, NIST), and agile Fintech operations, the study formulates a set of dynamic risk metrics such as vulnerability exposure index, data integrity deviation ratio, and threat surface evolution rate. These metrics are contextualized within PLM workflows to support decision-making, resource prioritization, and regulatory alignment. Empirical evaluation using Fintech case studies demonstrates that risk-integrated PLM enhances product robustness, reduces incident recovery time by approximately 30%, and improves compliance efficiency. Furthermore, incorporating predictive analytics enables early detection of potential breaches and systemic vulnerabilities. The proposed model not only bridges the gap between product innovation and cybersecurity governance but also establishes a measurable framework for continuous improvement and assurance. The study concludes that integrating cyber risk metrics into Fintech PLM transforms cybersecurity from a reactive safeguard into a strategic asset, fostering user trust, operational stability, and regulatory adherence in an increasingly volatile digital ecosystem.

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Published

2024-06-30

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Section

Articles