While predictive modeling is still in its early stages for many insurers, it can be an incredibly powerful tool drawing on techniques from BI, machine learning, and AI in order to generate a “score” in specific, individual cases that can be interpreted by a human or automated process to determine risk. These models are distinctive from actuarial models that use demographic data in aggregate to assess risk at a general level across a book of business. For those insurers leveraging the technology, the most common predictive model areas include underwriting risk, product profitability, and financial projection models. But is investing in predictive analytics worth it?
AI has the potential to bypass more traditional predictive modeling and make real-time predictive decisions without previous human modeling. Some see AI technology as the next step in the evolution of using data analytics to make predictive decisions. It’s important to recognize, however, that when that does happen, it will require vast amounts of data to train AI to that level of risk knowledge, and the training input will naturally include the scores from previous predictive models. As such, the potential value of both technologies will only increase as they mature, and so insurers will likely benefit from employing both tools.
Interest in predictive modeling has increased across all industries, insurance included, and insurers now have a growing vendor market from which to choose when considering providers of predictive analytics solutions. As with all contracted services, insurers would do well to thoroughly vet their options, as the offerings of various vendors vary widely. For more on predictive analytics software solutions, see our latest Novarica Market Navigator at https://dev-novarica.pantheonsite.io/predictive-analytics-solutions-for-insurers/.
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