KEY TAKEAWAYS:
--Automation applied to an inefficient operation can simply magnify the inefficiency.
--You will surely be asked which customers will be affected most by automation, so be prepared.
--Key changes will be cultural, so understand at the outset how much change will be needed -- and tolerated.
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Streamlining and automation often get talked about in the same breath, but there’s a big difference.
Streamlining is essentially simplifying an existing process. That is typically done by removing some unnecessary workflows from the larger effort.
By comparison, automation is “cost cutting by tightening the corners and not cutting them,” as Haresh Sippy, chief founder of Tema India, has put it. In the context of insurance pricing, automation typically means connecting disparate systems and data flows more seamlessly. Rather than just simplifying existing processes, these connections bring some overall structure and governance to the workflow, enable scheduling and triggering of activity and allow for reports to monitor progress.
Automation shouldn’t just be a matter of saving time – important as that often is – it should bring new sources of value to pricing.
Automation done responsibly
Automation allows for doing more with less -- but automation applied to an inefficient operation can simply magnify the inefficiency.
Traditional machine learning models, for example, have to effectively "fail" to learn. But will they learn fast enough for certain pricing applications? Automation has to be appropriate to the pricing circumstances for which it is intended.
When looking at how to apply automation responsibly, the six standards recommended by Microsoft are a good starting point: accountability; transparency; fairness; reliability and safety; privacy and security; and inclusiveness.
Improvement must be relative to something relevant
Insurers approach the pricing cycle of Analyze – Decide – Deploy in a multitude of ways, so no two automation projects are going to be the same.
For example, companies working with traditional, generalized, linear models could make significant improvements (up to 40% resource savings in our experience) by automating the simplifying, grouping and curve-fitting factors that could lead to more competitive or segmented pricing. A next step could be the automated tuning of factor parameters and interactions, leading to applications that assist the interpretation of results.
The key is to identify where automation can improve your pricing process and deliver the most value.
Automation may do more than just replace what previously would have been done manually. Machines may reveal pricing insights that wouldn’t typically have been uncovered. Often, automation can serve to triage the value of making rating updates, as we have seen recently with some companies automating the tracking of potential inflation effects on their books of business.
See also: Insurers Turn to Automation
Which customers will be most affected?
In just about every pricing automation project we’ve worked on where companies are, for example, using technology to integrate and update data from multiple systems to adjust their pricing and are aiming to get new pricing to market quicker, the question arises: “Which customers are going to be most affected, and by how much?”
In the fairly safe knowledge that the question is coming, automate the response, particularly as impact analysis can be extremely time-consuming if done manually.
Another reason for being ready for the question is increasing interest from regulators in understanding how machine learning and automation are driving pricing decisions.
Key challenges are often cultural
Automation doesn’t necessarily always sit easily with established pricing practices. It pays to determine what those most involved are prepared to let go and the acceptable levels of scrutiny and review of automated processes at the outset.
There is likely to be a need to introduce new working practices, because breaks or barriers in an automation-enhanced workflow can limit the benefits of automation. For example, a company that aspires to automated delivery of pricing updates can face real problems if the hand-off from pricing/product teams to IT/rate deployment teams is overly manual and complex.
