The energy sector depends on high-value, safety-critical assets where failures can result in severe human, environmental, and financial consequences. Although insurance provides financial risk transfer, current underwriting practices largely rely on historical loss data and regional benchmarks, often overlooking asset-specific integrity conditions and management effectiveness. Risk-Based Inspection (RBI) offers a structured engineering approach to quantify probability and consequence of failure at the asset level, yet its outputs are rarely integrated into insurance premium determination.
This study introduces a Risk-Based Premium Model (RBPM) framework that embeds RBI outputs and integrity management data directly into insurance pricing. The framework prioritizes inspection confidence and documented integrity management practices as primary premium drivers, with remaining asset life applied as a secondary modifier. Aboveground Storage Tank datasets from prior integrity studies, including inspection histories, condition assessments, and management plans, were used to test the model and compare premium outcomes across differing risk profiles.
Application of RBPM demonstrated clear differentiation between well-managed and poorly managed assets. Tanks with low remaining life but high inspection confidence and active integrity management plans were rated comparably to lower-risk assets, reflecting reduced uncertainty. Conversely, assets with unknown condition, limited inspection coverage, or absent management strategies attracted significant premium loadings. The model replaced generic actuarial assumptions with transparent, evidence-based engineering risk indicators.
RBPM establishes a direct link between integrity risk reduction and financial reward, promoting proactive asset management while reducing insurer uncertainty. By aligning engineering reliability with insurance economics, the framework enables fairer, condition-based premiums and incentivizes continuous integrity improvement. Future work includes integration with digital twins, real-time analytics, and standardized RBI–insurance protocols to further enhance risk-responsive underwriting.
