sigma 5/2020 – Machine intelligence in insurance

The raison d'être for machine intelligence (MI) is higher profits through revenue creation and cost savings. In insurance, MI has yielded returns in areas such as customer analytics and claims processing.

Early adopters of more advanced MI approaches are seeing positive results in areas like faster claims settlement and targeted cross- and up-selling, and with correct deployment, we believe there are more benefits to be had. However, enterprise-wide scale transformation of insurance through MI is still ambition rather than reality. We found that only 10% of firms are able to implement transformative end-to-end machine intelligence systems.

A main reason is data quality. For example, our research found that 80% of companies lack data that's clean and curated enough to implement the most productive machine intelligence. 

The quality of data and data curation capabilities is a main determinant of effective deployment of MI-enabled systems
Jeffrey Bohn, Chief Research and Innovation Officer, Swiss Re Institute

Read our latest sigma to find out more about the opportunities to be had from, and the challenges that insurers face in deploying MI-enabled systems.

Facts & Figures

Figure 4 (RHS):
Progress in AI implementation (RHS)

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Table 1:
Criteria for successful implementation of enterprise-wide MI for the long term

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Figure 7:
MI-data strategy schematic

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Figure 9:
MI-related mentions in insurer investment reports (LHS), and trend in Insurtechs using AL/ML technology

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Risk knowledge


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sigma 5 Machine intelligence in insurance

​Why data and machine intelligence will become the new normal in insurance

Now, more than ever, there is a focus on digitisation and sustainability. This is true across sectors but is particularly relevant for the insurance industry, who are viewed by many as being particularly slow to embrace digitalisation. In this article we consider trends like access to data enabling increased personalisation for consumers, and we look at business models that are emerging as the industry evolves into the next phase.

Machine learning is essential for a resilient future



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