AI- hot water for insurance incumbents, or a relaxing spa?

Frog-in-boiling-water

The parable of the frog in the boiling water is well known- you know, if you put a frog into boiling water it will immediately jump out, but if you put the frog into tepid water and gradually increase the temperature of the water it will slowly boil to death.  It’s not true but it is a clever lede into the artificial intelligence evolution within insurance.  Are there insurance ‘frogs’ in danger of tepid water turning hot, and are there frogs suffering from FOHW (fear of hot water?)

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Patrick Kelahan is a CX, engineering & insurance consultant, working with Insurers, Attorneys & Owners in his day job. He also serves the insurance and Fintech world as the ‘Insurance Elephant’.

The frog and boiling water example is intuitive- stark change is noticed, gradual change not so much.  It’s like Ernest Hemmingway’s quotation in “The Sun Also Rises”- “How did you go bankrupt?  Gradually, and then suddenly!”  In each of the examples the message is similar- adverse change is not always abrupt, but failure to notice or react to changing conditions can lead to a worst-case scenario.  As such with insurance innovation.

A recent interview in The Telegraph by Michael Dwyer of Peter Cullum, non-executive Director of Global Risk Partners (and certainly one with a CV that qualifies him as a knowing authority), provided this view:

“Insurance is one business that is all about data. It’s about numbers. It’s about the algorithms. Quite frankly, in 10 years’ time, I predict that 70pc or 80pc of all underwriters will be redundant because it will be machine driven.

“We don’t need smart people to make what I’d regard as judgmental decisions because the data will make the decision for you.”

A clever insurance innovation colleague, Craig Polley, recently posed Peter’s insurance scenario for discussion and the topic generated lively debate- will underwriting become machine driven, or is there an overarching need for human intuition?  I’m not brave enough to serve as arbiter of the discussion, but the chord Craig’s question struck leads to the broader point- is the insurance industry sitting in that tepid water now, and are the flames of AI potentially leading to par boiling?

I offered a thought recently to an AI advocate looking for some insight into how the concept is embraced by insurance organizations.  In considering the fundamentals of insurance, I recounted that insurance as a product thrives best in environments where risk can be understood, predicted, and priced across populations with widely varied individual risk exposures as best determined by risk experience within the population or application of risk indicators.  Blah, blah, blah. Insurance is a long-standing principle of sharing of the ultimate cost of risk where no one participant is unduly at a disadvantage, and no one party is at a financial advantage- it is a balance of cost and probability.

Underwriting has been built on a model of proxy information, on the law of large numbers, of historical performance, of significant populations and statistical sampling.  There is not much new in that description, but what if the dynamic is changed, to an environment where the understanding of risk factors is not retrospective, but prospective?

Take commercial motor insurance for example.  Reasonably expensive, plenty of human involvement in underwriting, high maximum loss outcomes for occurrences.  Internal data are the primary source of rating the book of business.  There are, however,  new approaches being made in the industry that supplant traditional internal or proxy data with robust analysis of external data.  Luminant Analytics is an example of a firm that leverages AI in providing not only provide predictive models for motor line loss frequency and severity trends, but also analytics that help companies expanding into new markets, where historical loss data is unavailable.  Traditional underwriting has remained a solid approach, but is it now akin to turning the heat up on the industry frog?

The COVID-19 environment has by default prompted a dramatic increase in virtual claim handling techniques, changing what was not too long ago verboten- waiver of inspection on higher value claims, or acceptance of third party estimates in lieu of measure by the inch adjuster work.  Yes, there will be severity hangovers and spikes in supplements, but carriers will find expediency trumps detail- as long as the customer is accepting of the change in methods.  If we consider the recent announcement by US P&C carrier Allstate of significant staff layoffs as an indicator of the inroads of virtual efforts then there seemingly is hope for that figurative frog.

Elsewhere it was announced that the All England Club has not had its Wimbledon event cancellation cover renewed for 2021 (please recall that the Club was prescient in having cancellation cover in force that included pandemic benefits).  The prior policy’s underwriters are apparently reluctant to shell out another potential $140 million with a recurrence of a pandemic, but are there other approaches to pandemic cover?  The consortium of underwriting firms devised the cover seventeen years ago; can the cover for a marquee event benefit from AI methodology that simply didn’t exist in 2003?  It’s apparent the ask for cover for the 2021 event attracted knowledgeable frogs that knew to jump out of hot water, but what if the exposure burner is turned down through better understanding of the breadth of data affecting the risk, that there is involvement of capital markets in diversifying the risk perhaps across many unique events’ outcomes and alternative risk financing, and leveraging of underwriting tools that are supported by AI and machine learning?  Will it be found in due time that the written rule that pandemics cannot be underwritten as a peril will have less validity because well placed application of data analysis has wrangled the risk exposure to a reasonable bet by an ILS fund?

There are more examples of AI’s promise but let us not forget that AI is not the magic solution to all insurance tasks.  Companies that invest in AI without a fitting use case simply are moving their frog to a different but jest as threatening a pot.  Companies that invest in innovation that cannot bridge their legacy system to meaningful outcomes because there is no API functionality are turning the heat up themselves.  Large scale innovation options that are coming to a twenty-year anniversary (think post Y2K) may have compounding legacy issues- old legacy and new legacy.

The insurance industry needs to consider not just individual instances of the gradual heat of change being applied.

What prevents the capital markets from applying AI methods (through design or purchase) in predicting or betting on risk outcomes?  The more comprehensive and accurate risk prediction methods become the more direct the path between customer and risk financing partner also becomes.  Insurance frogs need not fear the heat if there are fewer pots to work from, but no pots, no business.

The risk sharing/risk financing industry has evolved through application of available technology and tools, what’s to say AI does not become a double-edged sword for the insurance industry- a clever tool in the hands of insurers, or a clever tool in the hands of alternative financing that serves to cut away some of the insurers’ business?  If asked, Peter Cullum might opine that it’s not just underwriting that AI will affect, but any other aspect of insurance that AI can effectively influence.  Frogs beware.

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