 Serhat Guven is the Global Proposition Lead for P&C Product, Pricing, Claims and Underwriting (PPCU) in WTW’s Insurance Consulting and Technology business. Neil Chapman is Senior Director, Insurance Consultancy and Technology Global Leadership, Pricing, Product, Claims and Underwriting, at WTW.
Streamlining is essentially simplifying an existing process, typically by removing some unnecessary workflows from the larger effort.
By comparison, we like a quote from Haresh Sippy, Chief Founder of Tema India, who described automation as “cost cutting by tightening the corners and not cutting them”.
In the context of insurance pricing, that will typically mean connecting disparate systems and data flows more seamlessly, bringing some overall structure and governance to the workflow, enabling scheduling and triggering of activity, and creating reports to monitor progress.
Automation shouldn’t just be a matter of saving time – important as that often is – it should pave the way to bring new sources of value to pricing.
Automation done responsibly
Another frequently used phrase in relation to automation is “to do more with less”. That is often the case but, equally, automation applied to an inefficient operation will simply magnify the inefficiency.
If we look at traditional machine learning models, for example, they have to effectively ‘fail’ to learn. But, will they learn fast enough for certain pricing applications? In other words, 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 insurance pricing cycle of Analyse – Decide – Deploy in a multitude of ways, so no two automation projects are going to be the same.
For example, companies working with traditional generalised linear models could make significant improvements (up to 40% resource savings in our experience) by automating the process of 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 on 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.
It’s also worth remembering that 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.
Which customers will be most impacted?
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 impacted, 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 influencing factors that drive pricing decisions.
Key challenges are often cultural
Automation doesn’t necessarily always sit easily with established pricing practices. So, 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 and bed in 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.
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