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Complex decision-making and business rules

Lars Toomre posted a comment that made me think about complex decision-making.

I would think that this solution would be great for any bread-and-butter insurance underwriting situation, but would break down with the some of larger, more complex deals for certain corporate P&C lines. How does one get more information on what Blaze Advisor can do with complex rules?

So let's take a couple of specific examples. Automobile insurance is relatively repetitive and so companies can get very high rates of automation - Auto Club Group, for instance, automates 99% of their decisions. What about the other 1%? Well those still get referred to underwriters for review but, in those circumstances, the rules can and do execute both to add additional information likely to be relevant (MVR or Motor Vehicle Reports for instance) and to inform an underwriter as to why it is being referred ("Number of crashes is relevant and was too high for automatic risk calculation" or whatever). In addition, rules can be used to refer the policy to a particular underwriter e.g. one who specializes in the particular kind of corner-case identified. This kind of 1% referral is one of the reasons BPMSs complement BRMSs - the rules-based decision process can be injected into the underwriting process and leverage the work queuing etc for referrals while pushing the auto-adjudicated into the fulfillment part of the process.

Commercial P&C policies are a different case - there is almost always something unique to each policy. This is one of the reasons why fewer commercial insurers use rules. That said, there is still much that could be done with rules:

  • A rules-driven interactive dialog can be used to collect the data likely to be needed. SmartForms for Blaze Advisor, for instance, would allow you to use rules to control valid answers and to display/hide/change follow-up questions based on answers. This helps make sure the data is correct and complete before an underwriter sees it.
  • Rules could be used to identify those that automatically fail for some reason and to handle this processing. This would reduce the noise in the system for underwriters.
  • Renewals could likely be processed at a fairly high rate using rules that really compared the renewal to the old policy and checked to see if there was anything that required review.
  • Rules could be run to find additional information if it seemed relevant e.g. using location information to calculate flood risk etc.
  • Even if rules could not define the whole policy and underwrite it they would likely allow many of the terms and conditions and prices for parts of the policy to be auto-generated make the underwriting process simpler and quicker
  • and so on...

Now adding risk models based on predictive analytics might also help the underwriting percentage but, clearly, these less repeatable transactions are less likely to be completely automated and more likely to be partially automated and largely supported by business rules.

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