AI Summary & Key Takeaways
AI InsightThe proposed methodology enhances actuarial fairness, transparency, and policy efficiency, providing a scalable foundation for modernizing state-supported agricultural insurance systems and advancing climate-resilient risk governance.
📌 3 Key Takeaways
Abstract
Agriculture is increasingly exposed to systemic risks driven by climate change, including extreme weather events, yield instability, and market fluctuations. Effective risk zoning is therefore crucial for actuarially fair and sustainable agricultural insurance. However, in multi-branch systems such as Türkiye’s Agricultural Insurance Pool (TARSIM), different insurance lines such as Crop, Greenhouse, Livestock, and Income Protection, employ distinct criteria and coding schemes, often resulting in inconsistent regional classifications. This study introduces a novel Machine Learning-Based Cross-Branch Consistency Framework to evaluate and harmonize agricultural risk zoning across these branches. Using official 2025 TARSIM datasets covering 71,902 localities, ordinal risk codes (A–Z) were encoded and analyzed through correlation statistics, clustering, and supervised learning. The results revealed strong interdependencies among high-intensity perils such as hail and storm across open-field and greenhouse insurance lines (r > 0.98), and high spatial synchrony among staple crop risks (r ≈ 0.99), confirming the existence of shared systemic exposure. K-Means clustering identified five statistically robust regional risk archetypes that transcend administrative boundaries and branch definitions. A novel Cross-Branch Consistency Index (CBCI) was developed to quantify inter-branch alignment, highlighting regions of both coherence and discrepancy in current zoning logic. Supervised learning validation using Random Forest achieved over 95% classification accuracy, demonstrating the reliability and reproducibility of the ML-derived zones. The findings support a transition from fragmented, expert-driven zoning to an integrated, data-driven risk management framework. The proposed methodology enhances actuarial fairness, transparency, and policy efficiency, providing a scalable foundation for modernizing state-supported agricultural insurance systems and advancing climate-resilient risk governance.
Özet
Agriculture is increasingly exposed to systemic risks driven by climate change, including extreme weather events, yield instability, and market fluctuations. Effective risk zoning is therefore crucial for actuarially fair and sustainable agricultural insurance. However, in multi-branch systems such as Türkiye’s Agricultural Insurance Pool (TARSIM), different insurance lines such as Crop, Greenhouse, Livestock, and Income Protection, employ distinct criteria and coding schemes, often resulting in inconsistent regional classifications. This study introduces a novel Machine Learning-Based Cross-Branch Consistency Framework to evaluate and harmonize agricultural risk zoning across these branches. Using official 2025 TARSIM datasets covering 71,902 localities, ordinal risk codes (A–Z) were encoded and analyzed through correlation statistics, clustering, and supervised learning. The results revealed strong interdependencies among high-intensity perils such as hail and storm across open-field and greenhouse insurance lines (r > 0.98), and high spatial synchrony among staple crop risks (r ≈ 0.99), confirming the existence of shared systemic exposure. K-Means clustering identified five statistically robust regional risk archetypes that transcend administrative boundaries and branch definitions. A novel Cross-Branch Consistency Index (CBCI) was developed to quantify inter-branch alignment, highlighting regions of both coherence and discrepancy in current zoning logic. Supervised learning validation using Random Forest achieved over 95% classification accuracy, demonstrating the reliability and reproducibility of the ML-derived zones. The findings support a transition from fragmented, expert-driven zoning to an integrated, data-driven risk management framework. The proposed methodology enhances actuarial fairness, transparency, and policy efficiency, providing a scalable foundation for modernizing state-supported agricultural insurance systems and advancing climate-resilient risk governance.