Blockchain-enabled Risk Scoring for P&C Insurance Underwriting

Thesis Abstract

The rapid digitization of property and casualty (P&C) insurance underwriting has intensified the need for transparent, verifiable, and efficient risk assessment mechanisms. This study investigates the viability and impact of blockchain-enabled risk scoring to augment underwriting decisions in P&C markets, addressing the problem of information asymmetry, data tampering risk, and prolonged underwriting cycles. The aim is to develop and validate an integrated blockchain-based risk scoring framework that aggregates disparate data sources, ensures immutability of underwriting inputs, and provides real-time verifiable risk scores to underwriters. Specific objectives are (1) to design a multi-layer risk scoring model that fuses insured asset data, historical claims, external data feeds, and sensor-derived information within a permissioned blockchain; (2) to evaluate the impact of blockchain-enabled risk scoring on underwriting turnaround time, pricing accuracy, and loss ratio over a 24-month period; (3) to assess governance, privacy, and interoperability challenges across insurers, brokers, and third-party data providers; (4) to test the robustness of the framework under regulatory compliance constraints such as GDPR and local privacy laws; and (5) to derive policy and practice recommendations for scaling blockchain-based underwriting across product lines. A mixed-methods approach is employed. The research adopts a pragmatic paradigm, drawing on the Technology Acceptance Model (TAM) and the Information Asymmetry Theory to frame adoption and risk signaling dynamics, supplemented by principal-agent theory to interpret incentives. The quantitative component uses a quasi-experimental design with a 12-month pilot across two regional insurers, involving a sample of 1,200 commercial property policies and 800 personal lines policies, matched through propensity scoring. Data collection combines structured underwriting records, claims histories, sensor telemetry (where applicable), and immutable blockchain audit trails, totaling an estimated 2.5 million data points. Instrumentation includes a bespoke blockchain-enabled risk scoring dashboard, standardized underwriting checklists, and a data quality assessment protocol. The qualitative component comprises 20 semi-structured interviews with underwriters, data engineers, and compliance officers, analyzed through thematic analysis to elucidate operational and governance insights. Analytical methods consist of descriptive statistics to profile baseline underwriting performance, multivariate regression to quantify the impact of blockchain-scored variables on pricing accuracy (mean absolute percentage error and prediction intervals), and difference-in-differences analysis to isolate the effect of the blockchain framework on underwriting cycle times and loss ratios. Time-series analyses will examine trend stability in risk scores across policy vintages, while network analysis will map data provenance, trust anchors, and permissioned access relationships within the blockchain ecosystem. Data integrity and model validation will employ cross-validation, out-of-sample testing, and sensitivity analyses to ensure robustness against data sparsity and sensor noise. The risk scoring model integrates actuarial indicators with real-time data feeds, utilizing gradient boosting and LSTM components to capture nonlinear relationships and temporal dynamics, with explainability techniques (SHAP values) to support underwriter decision-making. Expected findings include a significant reduction in underwriting cycle time, improved pricing accuracy evidenced by lower predictive error metrics, and enhanced risk signal fidelity due to tamper-evident data provenance. The framework is anticipated to demonstrate resilience to data integrity breaches and demonstrate regulatory compliance through auditable, immutable transaction logs. The study contributes to knowledge by articulating a practical, scalable blueprint for blockchain-enabled risk scoring in P&C underwriting, delineating the interplay between technological infrastructure, data governance, and underwriting disciplines. It also advances theoretical understanding of information asymmetry mitigation via distributed ledgers in insurance contexts and extends the empirical literature on technology-driven underwriting transformations. The conclusions will emphasize the operational viability and strategic value of integrating blockchain-based risk scores with traditional actuarial models, while acknowledging limitations related to data interoperability, regulatory variation across jurisdictions, and the need for standardized data schemas. Recommendations include developing industry-wide data governance standards, fostering interoperable APIs for external data sources, and establishing regulatory sandboxes to accelerate adoption. Future research directions propose cross-line expansions (commercial and personal lines), exploration of parametric risk transfer mechanisms, and longitudinal assessment of customer outcomes under blockchain-augmented underwriting.