According to recent news, Solvency II may be introduced in 2015 instead of 2013. However, this does not mean that insurers should delay work to get their ‘house’ in order.
Many technology firms are promoting point-level Solvency II solutions, however, in my view insurance companies may be better off enhancing their core capabilities, especially in the areas of data quality, geocoding and data enrichment. In addition predictive analytics can help companies master the complex relationships between customer demographics, market trends, buying patterns and location. In this way, insurers can not only comply with Solvency II requirements in the most cost-efficient manner—they can actually improve their overall business operations and impact on the bottom line.
There is no question that the ability to accurately measure assets and calculate risk can have significant impact on cash flow and investment capital. If insurers over estimate they could be at a serious competitive disadvantage. This is why insurers appear to be taking advantage of the EU Directive’s option for them to develop and certify their own internal models to calculate solvency capital.
While adopting an internal modelling approach will no doubt help insurers get their capital calculations right, in my view this investment will not really pay off until the calculations are underpinned by accurate, complete and appropriate data.
Insurers can't delay work to get their house in order
Poor data quality can impact the modelling process in a number of ways such as calculation failures, punitive default values, increased manual intervention, and delays in model updating. This can ultimately lead to an increase in the level of capital to be held as the regulator places a capital charge on top of the firm’s own assessment.
Unfortunately, in my experience, many insurance companies are not satisfied with their data quality, citing incorrect information, missing or misfiled data, duplicated records and inconsistent standards that lead to significant costs, delays and an incomplete understanding of the truth. Considering that one needs to aggregate and account for market risk, operational risk, credit risk and insurance risk across every country and multiple lines of business, it is easy to see why data quality is so important.
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