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Abstract Pluvial floods pose a significant threat to properties, yet comprehensive impact analysis is hindered by data limitations on pluvial inundation. To assess pluvial flood impacts, we leveraged U.S. flood insurance claims and policy records for a subset of properties outside 100-year floodplains, streamflow records, and nationwide precipitation data, enabling us to distinguish damage claims caused by pluvial floods over 1978–2021. Strikingly, 87.1% of the claims analyzed from this subset were due to pluvial floods. Utilizing these pluvial flood claims unveiled distinct regional patterns of pluvial impacts across the contiguous U.S. These patterns are informed by the relationship between claim frequency and precipitation within each region. Remarkably, despite the pervasiveness of impacts, many states are seeing declining uptake in pluvial flood insurance coverage. Our study highlights regions facing heightened pluvial flood risks and underscores the critical need for enhanced consideration of pluvial inundation within risk management frameworks.
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This content will become publicly available on December 1, 2025
A copula model for marked point process with a terminal event: An application in dynamic prediction of insurance claims
Accurate prediction of an insurer’s outstanding liabilities is crucial for maintaining the financial health of the insurance sector. We aim to develop a statistical model for insurers to dynamically forecast unpaid losses by leveraging the granular transaction data on individual claims. The liability cash flow from a single insurance claim is determined by an event process that describes the recurrences of payments, a payment process that generates a sequence of payment amounts, and a settlement process that terminates both the event and payment processes. More importantly, the three components are dependent on one another, which enables the dynamic prediction of an insurer’s outstanding liability. We introduce a copula-based point process framework to model the recurrent events of payment transactions from an insurance claim, where the longitudinal payment amounts and the time-to-settlement outcome are formulated as the marks and the terminal event of the counting process, respectively. The dependencies among the three components are characterized using the method of pair copula constructions. We further develop a stagewise strategy for parameter estimation and illustrate its desirable properties with numerical experiments. In the application we consider a portfolio of property insurance claims for building and contents coverage obtained from a commercial property insurance provider, where we find intriguing dependence patterns among the three components. The superior dynamic prediction performance of the proposed joint model enhances the insurer’s decision-making in claims reserving and risk financing operations.
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- Award ID(s):
- 2210712
- PAR ID:
- 10553286
- Publisher / Repository:
- Institute of Mathematical Statistics
- Date Published:
- Journal Name:
- The Annals of Applied Statistics
- Volume:
- 18
- Issue:
- 4
- ISSN:
- 1932-6157
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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