The top challenges among insurers investing in and employing predictive analytics are a lack of sufficient data and limited numbers of skilled modelers, according to a survey released by Earnix, a provider of pricing and customer analytics software for banks and insurers, and ISO, a source of information about property/casualty insurance risk, titled “2013 Insurance Predictive Modeling Survey.”

Data quality is an issue especially among large insurers (more than $1 billion in gross written premium), with 57 percent pointing to it as their most significant challenge. Among smaller insurers (less than $1 billion in gross written premium), data quality (30 percent) was second to a lack of observations/skills (42 percent).

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