  Monte Carlo simulations employed for the analysis of portfolios of
catastrophic risk process large volumes of data. Often times these simulations
are not performed in real-time scenarios as they are slow and consume large
data. Such simulations can benefit from a framework that exploits parallelism
for addressing the computational challenge and facilitates a distributed file
system for addressing the data challenge. To this end, the Apache Hadoop
framework is chosen for the simulation reported in this paper so that the
computational challenge can be tackled using the MapReduce model and the data
challenge can be addressed using the Hadoop Distributed File System. A parallel
algorithm for the analysis of aggregate risk is proposed and implemented using
the MapReduce model in this paper. An evaluation of the performance of the
algorithm indicates that the Hadoop MapReduce model offers a framework for
processing large data in aggregate risk analysis. A simulation of aggregate
risk employing 100,000 trials with 1000 catastrophic events per trial on a
typical exposure set and contract structure is performed on multiple worker
nodes in less than 6 minutes. The result indicates the scope and feasibility of
MapReduce for tackling the computational and data challenge in the analysis of
aggregate risk for real-time use.
