Μοντέλα φυσικών καταστροφών μετεωρολογικών συμβάντων και εφαρμογές στον ασφαλιστικό κλάδο

Frequency and severity of weather-related natural catastrophe events are expected to increase due to climate change. Improved climate projections provide evidence that future climate-related extremes will be increased in many European regions having a significant social and economic impact and affecting the insurance industry. Moreover, weather related events are often not fully recorded therefore it is challenging to model the relation between climate events and claim frequencies. Additionally, there is a difficulty in recognizing the proximate cause of a relevant claim. Motivated by the above issues, a representative supervised learning approach, the decision tree-based ensemble method, called “Gradient Boosting” is implemented for classifying the number of insurance claims caused by storms in Greece. Furthermore, a new class of compound frequency models for joint modeling of storm and storm-triggered claim frequencies observed is introduced. The proposed models demonstrate the joint distribution of the actual storm and claim processes. Geospatial covariates are included to assess their impacts on the storm and claim frequencies. Finally, a Solvency Capital Requirement methodological approach is proposed in the context with Greek insurance portfolio, risk, and weather particularities.Η συχνότητα και η σφοδρότητα των φυσικών καταστροφών σχετικά με τον μετεωρολογικό κίνδυνο αναμένεται να αυξηθούν λόγω της κλιματικής αλλαγής. Οι νέες κλιματικές προβλέψεις αναδεικνύουν ότι στο μέλλον οι ακραίες συνθήκες που σχετίζονται με το κλίμα θα αυξηθούν σε πολλές ευρωπαϊκές περιοχές με σημαντικό κοινωνικό και οικονομικό αντίκτυπο, επηρεάζοντας επίσης τον ασφαλιστικό κλάδο. Επιπλέον, τα συμβάντα που σχετίζονται με τις καιρικές συνθήκες συχνά δεν καταγράφονται πλήρως, επομένως είναι δύσκολο να μοντελοποιήσουμε τη σχέση μεταξύ κλιματικών γεγονότων και της συχνότητας των ασφαλιστικών απαιτήσεων. Παράλληλα, πολλές φορές, υπάρχει δυσκολία στην αναγνώριση της πρωτογενούς αιτίας των σχετικών ασφαλιστικών ζημιών. Με κίνητρο τα παραπάνω ζητήματα, εφαρμόσαμε μια από τις πιο αντιπροσωπευτικές προσεγγίσεις μοντέλου «εποπτευόμενης εκμάθησης», μέθοδο βασισμένη σε δενδροδιαγράμματα αποφάσεων, την «Gradient Boosting», για την ταξινόμηση του πλήθους των ασφαλιστικών απαιτήσεων που προκαλούνται από καταιγίδες στην Ελλάδα και μια νέα κατηγορία μοντέλων σύνθετων κατανομών συχνότητας για την από κοινού μοντελοποίηση της συχνότητας των ασφαλιστικών αποζημιώσεων και του πλήθους των ακραίων μετεωρολογικών φαινομένων. Τα προτεινόμενα μοντέλα καταδεικνύουν την από κοινού κατανομή του πλήθους των μετεωρολογικών συμβάντων και των ασφαλιστικών αποζημιώσεων περιλαμβάνοντας γεωχωρικές μεταβλητές για την αξιολόγηση των επιπτώσεών τους στη συχνότητα εμφάνισης των συμβάντων και των σχετικών ασφαλιστικών απαιτήσεων. Τέλος, προτείνεται μια μεθοδολογική προσέγγιση στον υπολογισμό Κεφαλαιακών Απαιτήσεων Φερεγγυότητας στα πλαίσια των ιδιαιτεροτήτων σχετικά με τα χαρτοφυλάκια ασφαλιστικών εταιρειών, των κινδύνων και των μετεωρολογικών χαρακτηριστικών στην Ελλάδα

Identification of Rainfall Thresholds Likely to Trigger Flood Damages across a Mediterranean Region, Based on Insurance Data and Rainfall Observations

Flood-producing rainfall amounts have a significant cumulative economic impact. Despite the advance in flood risk mitigation measures, the cost of rehabilitation and compensation of citizens by the state and insurance companies is increasing worldwide. A continuing challenge is the flood risk assessment based on reliable hazard and impact measures. The present study addresses this challenge by identifying rainfall thresholds likely to trigger economic losses due to flood damages to properties across the Athens Metropolitan Area of Greece. The analysis uses eight-year rainfall observations from 66 meteorological stations and high spatial resolution insurance claims on the postal code segmentation. Threshold selection techniques were applied based on the ROC curves widely used to assess the performance of binary response models. The model evaluates the probability of flood damages in terms of insurance claims in this case. Thresholds of 24-h rainfall were identified at the municipal level, as municipalities are the first administration level where decision making to address the local risks for the citizens is needed. The rainfall thresholds were further classified to estimate and map the local risk of flood damages. Practical implications regarding the applicability of the detected thresholds in early-warning systems are also discussed

Identification of Rainfall Thresholds Likely to Trigger Flood Damages across a Mediterranean Region, Based on Insurance Data and Rainfall Observations

Flood-producing rainfall amounts have a significant cumulative economic impact. Despite the advance in flood risk mitigation measures, the cost of rehabilitation and compensation of citizens by the state and insurance companies is increasing worldwide. A continuing challenge is the flood risk assessment based on reliable hazard and impact measures. The present study addresses this challenge by identifying rainfall thresholds likely to trigger economic losses due to flood damages to properties across the Athens Metropolitan Area of Greece. The analysis uses eight-year rainfall observations from 66 meteorological stations and high spatial resolution insurance claims on the postal code segmentation. Threshold selection techniques were applied based on the ROC curves widely used to assess the performance of binary response models. The model evaluates the probability of flood damages in terms of insurance claims in this case. Thresholds of 24-h rainfall were identified at the municipal level, as municipalities are the first administration level where decision making to address the local risks for the citizens is needed. The rainfall thresholds were further classified to estimate and map the local risk of flood damages. Practical implications regarding the applicability of the detected thresholds in early-warning systems are also discussed