Actuarial-ML Bridges for Catastrophe Loss Mitigation: Translating Grid Reliability

The proposed research suggests a hybrid actuarial/ML model that will expedite the utility grid reliability variables and property insurance pricing and claims triage to a parallel level. The rising intensity and number of the power outages as a result of aging of the infrastructure, overgrowth of vegetation, and global warming create correlated loss risks that cannot be effectively handled through a conventional actuarial modeling methodology. The framework approximates the reduction in reliability (depending on the projected SAIDI and SAIFI deltas accumulated over the geographies of an insurance to the projected severity of claims). The trade-off between interpretability and performance is made through GLM and GBDM, and fairness and stability checks are made to ensure compliance with the regulations. The possible efficiency increase in the operation is shown in terms of an experimental protocol of claims triage, which minimizes the losses in the second stage in the case of a cluster of outages. These restrictions are data confidentiality, geographic generalizability, and adversarial machine learning threats. The future projects predict the system of monitoring outages based on the IoT, digital transformation between the two industries, and the collaboration of the utilities and the insurers. This will offer an efficient means of incorporating predictive reliability knowledge into the contemporary catastrophe risk management.