Intelligent business applications will “cross the chasm” for early majority adoption within the next one to three years in insurance. This is due to widespread embedding of AI technologies within both business applications (ERP, CRM) and, more prominently, insurance-specific solutions such as P&C core platforms. These offerings drive customer loyalty and application dependence, rather than specific revenue lines for vendors, with AI enhancing the usefulness of the whole application. Intelligence can make the difference between a compelling business application and something that is “nice to have” but could be supported by manual processes or handcrafted in productivity tools like spreadsheets and documents. For example, core platform providers are building events-handling constructs and automation into claims management modules to expedite claims processing. Insurance-specific use cases for intelligent applications typically sit on a spectrum due to the complexity of risk. Simple underwriting cases for commoditized personal lines can be written on an STP basis, taking an exception-based underwriting approach for cases on the edge of the risk appetite. Simple property damage claims can be automated using CV and analytics for damage estimation, while serving up insights to support claims adjusters with more complex multiparty claims that require knowledge and high touch.
Mass: High
The vast majority of applications will ultimately become intelligent in some way via the use of AI technologies. Applications will incorporate one or more of the capabilities listed in the following bullet points, making the mass for intelligent applications very high. Intelligent applications that deliver better performance (namely, accuracy), user productivity and faster inferencing will provide a strong business case for users across business functions, geographies and industries.
Intelligent applications use AI in insurance in the following ways:
Data capture and response: AI technologies such as NLP, text analytics, deep neural networks and image recognition can be used for extraction of terms and conditions from policy wordings, or analysis of images in support of risk improvements or claims estimation. ■
Process augmentation: AI technologies like machine learning, decision intelligence, knowledge graphs and explainable AI can provide more intelligent actions for an application. In the future, process augmentation can be extended further to orchestrate interactions in the supply chain, automatically dispatching risk engineers, loss adjusters or other experts.
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Gartner, Inc. | G00786204
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