Refactoring Insurance/Reinsurance Catastrophe Modeling using Big Data

NewimageThe Catastrophe Modeling ecosystem, used in insurance and reinsurance, is a good example of the types of traditional computational platforms that are undergoing an assault from the exponential changes seen in data. Not only are commercially available simulation and modeling tools incapable of closing the forecasting capabilities gaps in the near future, but most organizations are not addressing the needed changes in the human factor (data scientists and functional behavioral analysts). The net for those insurance/reinsurance companies that rely on these old school techniques is 1) reduced accuracy in understanding physical effects of catastrophic events, 2) reduced precision in quantifying the direct and indirect cost of a catastrophe, and 3) increased blind spots for new and emergent catastrophic events, coming from combinations and permutations of existing events, as well as the creation of new ones.

NewImage

The quadrafication of big data (infrastructure, tools, exploratory methods, and people) is having a positive impact on these kinds of ecosystems. I believe we can use the big data reference architecture as the basis for refactoring the traditional catastrophic simulation, modeling, and financial analysis activities. Using platforms like Pneuron, we can help them more effectively map computationally complex MDMI (multi-data mult-instruction) workstreams into disaggregated process maps functioning in a MapReduce format, potentially using some of the existing simulation models. They could get the benefit of their a prior knowledge (models, tools), while dealing with the growth in data sets. Just a few thoughts.

One last note – this is an exercise in science and not engineering, or even systems integration. The practices that make for excellent enterprise architectures, requirements development, or even software engineering are of very little use here (those beyond critical thinking). To solve this problem, one must be willing to fail, fail early, an fail often. It is only through these failures that the true realization of Big Data Cat Modeling capabilities will be found.



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