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Monte Carlo simulations employed for the analysis of portfolios ofcatastrophic risk process large volumes of data. Often times these simulationsare not performed in real-time scenarios as they are slow and consume largedata. Such simulations can benefit from a framework that exploits parallelismfor addressing the computational challenge and facilitates a distributed filesystem for addressing the data challenge. To this end, the Apache Hadoopframework is chosen for the simulation reported in this paper so that thecomputational challenge can be tackled using the MapReduce model and the datachallenge can be addressed using the Hadoop Distributed File System. A parallelalgorithm for the analysis of aggregate risk is proposed and implemented usingthe MapReduce model in this paper. An evaluation of the performance of thealgorithm indicates that the Hadoop MapReduce model offers a framework forprocessing large data in aggregate risk analysis. A simulation of aggregaterisk employing 100,000 trials with 1000 catastrophic events per trial on atypical exposure set and contract structure is performed on multiple workernodes in less than 6 minutes. The result indicates the scope and feasibility ofMapReduce for tackling the computational and data challenge in the analysis ofaggregate risk for real-time use.

High Performance Risk Aggregation: Addressing the Data Processing Challenge the Hadoop MapReduce Way