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Modern insurance and reinsurance companies use stochastic simulation techniques for portfolio risk analysis. Their risk portfolios may consist of thousands of reinsurance contracts covering millions of individually insured locations. To quantify risk and to help ensure capital adequacy, each portfolio must be evaluated in up to a million simulation trials, each capturing a different possible sequence of catastrophic events (e.g., earthquakes, hurricanes, etc.) over the course of a contractual year. We present a flexible framework for portfolio risk analysis that can answer a rich variety of catastrophic risk queries. Rather than aggregating simulation data in order to produce a small set of high-level risk metrics efficiently (as done in production risk management systems), our focus is on queries on unaggregated or partially aggregated data. The goal is to allow analysts to obtain answers to a wide variety of unanticipated but natural ad hoc queries, which can help actuaries or underwriters to better understand the multiple dimensions (e.g., spatial correlation, seasonality, peril features, construction features, financial terms, etc.) that can impact portfolio risk and thus company solvency. We implemented a prototype system, called QuPARA, using Apache's Hadoop implementation of the MapReduce paradigm. This allows the user to utilize large parallel compute servers in order to answer ad hoc queries efficiently even on very large data sets typically encountered in practice. We describe the design and implementation of QuPARA and present experimental results that demonstrate its feasibility.

QuPARA: Query-driven large-scale portfolio aggregate risk analysis on MapReduce