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Understanding and managing model risk for reinsurance and ILS
Artemis, September 25, 2014
by Jessica Weinkle
In recent years, insurers and regulators have sought improved transparency of risk modeling practices. For example, recent changes to insurer capital requirements outlined by Solvency II explicitly promote goals of transparency and risk model assessment.
Catastrophe model vendors supply clients and regulators with extensive documentation on methodology. Yet, risk managers are increasingly interested in the uncertainty inherent in model methodology.
This type of uncertainty comes from the theoretical underpinnings of the model construction. In the sciences this type of uncertainty often goes by the term epistemic uncertainty. In insurance, it is often regarded as model risk and generally defined as, “the uncertainty that arises from having the wrong models to start with.”
In response to client demand, catastrophe modelers are offering improved access to model components and ease of model blending, morphing, fusing, etc. Most notable of efforts are those of RMS, Karen Clark and Company (KCC) and Lloyd’s. RMS(one) promises to provide users with access to over 300 probabilistic models, whereas KCC’s RiskInsight enables users access to internal assumptions. Lloyd’s Oasis offers users choice in “a set of plug-and-play components.”
These efforts are aimed at resolving concerns about model risk but do not actually help to reduce or control model risk. Improved ability to manipulate vendors’ models may buffer companies from volatility produced by model updates. But that volatility is produced by changes in the decision making by the model vendors and their judgments about how best to create a model.
The ability to create one’s own theory on how best to estimate a given risk does not make that theory an accurate representation of reality.
By chance alone, some views of risk will demonstrate better skill than others. However, model risk remains persistent because, as Karen Clark long ago explained, “[m]odel validation is also problematic.” Read more …
