In the week since the release of the Celent report “Machine Intelligence in Insurance: Designing the Aware Machine,” I have been involved in several fascinating discussions around a new level of personalization in insurance. An insurer called me to ask if there are any vendors providing intelligent machine services that can analyze social posts of a person and slot them into one of several pre-described personas. It was fascinating to contact some of the vendors involved in the report and find out just how far along they are in using intelligent machines to personalize down to the unit of the individual! At the same time, my colleague, Zil Bareisis on the Celent Banking team, blogged about a new type of personality test: Personality Insights powered by IBM’s Watson. According to the description of the system, the test “uses linguistic analytics to extract a spectrum of cognitive and social characteristics from the text data that a person generates through blogs, tweets, forum posts, and more.” Interestingly, it claims to be able to reach conclusions just from a text of 100 words. (Zil’s blog is here: Don’t be surprised if your bank knows not just who but also what you are in the future.) Following Zil’s lead, I copied an extract from the Aware Machine report into the system to find out what Personality Insights said about me. The results: “You are inner-directed, skeptical and can be perceived as insensitive. You are imaginative you have a wild imagination. You are philosophical: you are open to and intrigued by new ideas and love to explore them. And you are independent. You are relatively unconcerned with taking pleasure in life: you prefer activities with a purpose greater than just personal enjoyment. You consider achieving success to guide a large part of what you do: you seek out opportunities to improve yourself and demonstrate that you are a capable person.” After I got over my initial reaction -- which was to shout “No! That’s not me!” especially about the “insensitive” part -- my analyst instincts observed that my result contained a great deal of overlap with Zil’s profile. This indicates how broad the analysis is based on such a limited sample. The experience made me want to load a lot of additional data about myself into the system to see how personalized the results could get. And this is the main take-away for me about these systems; that they are trying to reach areas for which we have not generally applied automation, understanding the personality of our selves/our customers, using unstructured data. More experimentation and refinement will increase the value of both the results and our understanding of how to use them.

This blog entry has been republished with permission.

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