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The new era of insurance analytics – Willis Tower Watson

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Article Synopsis :

While new customer acquisition is a widely tracked metric in insurance  the specific measurement of retention rates, lifetime value creation, up sell/cross sell, and fraud remain a challenge.

 The Digital Insurer reviews Willis Towers Watson ’s Report on The new era of insurance analytics

Are big data analytics poised to deliver big returns to insurers?  

To address this, what analytics strategies and/or packages are worthy of your investment? Claudine Modlin and Graham Wright of Willis Tower Watson suggest answers as they discuss the topic in this Q&A article “The new era of insurance analytics”. Specific areas of conversation  include:

  • Increased attention and high expectations for advanced analytics in insurance
  • Domain expertise in this new era of advanced analytics
  • Analytics applications for insurance pricing
  • Choice of models in insurance analytics
  • Why insurers should invest in advanced analytics

One of the key themes is the value of people. Advancements in machine learning and AI make it seem like everything will eventually be automated but the machines operate at the behest of people, and the talent of those people, more than the machines themselves, will determine success (or failure).

Link to Full Article:: click here

Digital Insurer's Comments

If you’re relatively new to insurance analytics and prefer information presented in a Q&A format then this is the report for you.

With computing power per dollar increasing by a factor of ten roughly every four years analysts are handling larger data sets at lower cost  bringing powerful analytics within reach of smaller organizations, creating potential risk for larger players.

While large carriers hold an advantage in terms of customer base and capital position, smaller players, unburdened by legacy investments in core systems, are free to pursue fresh insights from scratch, for less, in the public cloud. Some large organizations are hiving off analytics teams to do same. Might this make sense for you?

Link to Source:: click here

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