What the examples I give above have in common is this.
Let’s create an imagined metric, X. It is the ratio of quality and abundance of data to the difficulty of its analysis and deriving conclusions therefrom.
The higher X is, the more relevant is ADS. Both ADS and traditional actuarial work follow the DATA IN → CRUNCH → CONCLUSION framework. The less you need to intervene in these three steps the more weight you can give ADS. Good data with low modelling complexity is the environment where automated algorithms thrive.
In the field of actuarial or statistical analysis in insurance, there are some areas where ADS may bring great insight. An obvious one is a single high-volume line of business (e.g. UK motor) where price optimisation needs large quantities of real time data and crunching, and quick response times in a highly competitive market. It’s the closest we have to algorithmic stock trading.
But this is rare. Insurance rarely needs such lightning fast responses, and very rarely will managers have enough confidence in the data to let a computer do their thinking.
In a nutshell, it is far easier for Amazon to predict what book I’d like to read than it is for me to tell you what your Commercial Property loss ratio is going to be next year.
The real insurance world weighs on X. It weighs on all of its components, decreasing the top line of the fraction and increasing the bottom line. Data is almost never abundant and immaculate. The choices of modelling approach, model parameters and of data-cleaning are laden with judgment that requires both experience and clear and transparent communication. The conclusions must be carefully considered – discarded when not sound, well-communicated in all cases. The quality of all findings must be drawn out and made clear to the end-user, and actions or other commercial ends suggested by the findings should be presented.
So whenever data is compromised in quality or volume (low DQ or DA) or whenever the analysis is challenging or its outcomes nuanced (high DiffA or DiffC), you can see we have low X.
Low X is very, very common. It means actuaries are needed for their experience and judgment. Those tough exams and years of commercial experience, for which actuaries get paid the big bucks, are enduringly valuable.
Don’t get me wrong: it is important to build processes and software tools to create control cycles of actuarial insight. This is the essence of efficiency and consistency. It is what Calibrant does. And I recognise that some circumstances do lend themselves to the algorithmic approach, including as contributions to an overall process still guided by an actuary.
But anyone who thinks that the actuaries can be replaced with a machine is either fooling themselves or (worried about those big bucks) guilty of wishful thinking. The insurance world can accommodate ADS and actuaries both, but if it had to choose it couldn’t lose the actuaries.
There has been a lot of speculation recently around the use of AI in insurance and how this signals the death knell for Underwriters. Having mopped up the coffee that I spat out and checked my life insurance provisions, this got me to thinking. Surely not all underwriters? But if not, then who?
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