Adaptive cyber insurance: pricing, underwriting, and evaluation framework

Thesis Abstract

This study addresses the imperative for adaptive cyber insurance in the face of rapidly evolving threat landscapes, enterprise risk management demands, and the inadequacies of static pricing and underwriting models. The problem centers on the misalignment between dynamic cyber risk profiles and traditional actuarial frameworks, which often rely on coarse indicators and fail to reflect evolving threat intelligence, organization-specific controls, and responders’ behavior. The aim is to develop an integrated pricing, underwriting, and evaluation framework that adaptively updates risk assessment as new information emerges, and to evaluate its performance against conventional methods. Specific objectives are (1) to model cyber risk using a dynamic Bayesian network that incorporates threat intelligence, control maturity, and organizational exposure; (2) to design an adaptive pricing mechanism using a multivariate time-series regime-switching model that adjusts premiums in real time as risk factors evolve; (3) to formulate underwriting rules that integrate continuous monitoring results, incident response capability, and decision rules for portfolio diversification; (4) to implement an evaluation framework employing back-testing, out-of-sample forecasting, and simulation-based stress testing to assess resilience and profitability; and (5) to validate the framework with a mixed-methods assessment combining quantitative performance metrics and qualitative expert evaluations. The methodology adopts a mixed-methods research design, anchored in risk theory, decision science, and information economics. The population comprises cyber insurance portfolios from large enterprises across financial services, healthcare, and critical infrastructure sectors. A purposive sample of 60 firms will be recruited, with 40 providing detailed incident histories and control maturity data, and 20 serving as a control group for out-of-sample validation. Data collection instruments include (i) structured firm surveys capturing security controls, governance practices, and incident response capabilities; (ii) threat intelligence feeds and MITRE ATT&CK-based mappings; (iii) insurer-acquired claims and premium data over a 36-month window; and (iv) semi-structured interviews with underwriters and risk managers to elucidate underwriting decision rationales. Validity and reliability will be ensured through pilot testing (n=6 firms), triangulation of incident records with insurer claims, and intercoder reliability checks for qualitative data. Analytical procedures comprise (a) construction of a dynamic Bayesian network to quantify evolving cyber risk states and their impact on loss distributions; (b) estimation of a regime-switching pricing model (Markov-switching GARCH) to produce time-varying premium surfaces conditional on risk state and exposure; (c) development of underwriting decision rules using a deterministic-optimization framework augmented by probabilistic risk measures (Value at Risk and Tail Value at Risk) and a portfolio-level diversification constraint; (d) evaluation through back-testing across the 24-month holdout period, assessing predictive accuracy (MAE, RMSE for losses; Brier score for state predictions) and profitability metrics (expected profit, risk-adjusted return); and (e) scenario analysis and Monte Carlo simulations to examine performance under elevated threat levels and varying control effectiveness. The study will employ regression analyses to identify factors driving premium adjustments, ANOVA to test differences across sectors, and thematic analysis of interview transcripts to integrate practitioner insights with quantitative results. Theoretical grounding draws on expected utility theory, regime-switching models in finance, and the dynamic risk management framework, augmented by insights from information economics and behavioral decision theory. Expected findings include (i) improved predictive accuracy for cyber losses using dynamic risk states that incorporate threat intelligence and control maturity; (ii) superior calibration and discrimination of premiums under adaptive pricing, reducing mispricing and adverse selection; (iii) underwriting rules that better align coverage terms with actual security posture and incident response capability, reducing claim frequency and severity variances; and (iv) robust performance under stress scenarios, with higher risk-adjusted returns and reduced tail risk relative to static frameworks. The study contributes to knowledge by integrating dynamic risk modeling with adaptive pricing and underwriting in cyber insurance, offering a replicable framework that insurers can implement with real-time data feeds and enterprise monitoring outputs. It advances practice by providing a validated methodology for continuous improvement of policy terms, pricing granularity, and portfolio risk management in the cyber insurance domain. The main conclusion anticipates that adaptive frameworks outperform static models in accuracy and resilience, and recommendations emphasize investment in threat intelligence integration, standardized control maturity metrics, and governance processes to operationalize ongoing model recalibration and governance oversight.