The Other Dismal Science

(Image from the balance)

I never gave a thought to actuarial science until I went to business school. One of the smartest guys in the class had been an actuary, which I came to understand was too tough a career choice if I had to be as smart as he was and pass difficult, technical exams. (BTW, he's now CFO of a well-known company.)

Digression: so I became a CPA, which meant sitting for two days of exams and working for two years in the audit department of a CPA firm. The latter path was easier, and besides, I've never had to pay anybody to do my taxes.

Apparently, becoming an actuary is even tougher these days: [bold added]

Among people taking at least one exam from the Society of Actuaries—the field’s biggest U.S. credentialing body—15% eventually pass the multiple tests required to become an Associate, one of two designations allowing them to practice. Just 10% pass those and additional tests to become a Fellow, the group’s higher designation, which affords bigger responsibilities and salaries.

It’s such an arduous process that the number of test-takers has been declining in recent years, and the society is making changes to keep candidates from dropping out of the gantlet. It is also adding new “predictive analytics” tests to adjust to the massive amounts of data insurers now have...

There is no limit to how many times a candidate can take the tests. It took one man 50 years to become a Fellow, says Stuart Klugman, an official at the society. The society says a candidate typically takes seven to 10 years to become a Fellow. They must pass 10 exams plus other coursework and requirements.

Every profession is being inundated with oceans of data, and being familiar with data science has become essential to being an actuary. Knowing how to filter, analyze, and model the data is crucial to success. (We noted the importance of predictive analytics three years ago.)

If one is not thorough, knowledgeable about statistics, and honest, one can easily promulgate misinformation by biasing the data sets, selecting or not selecting variables to be analyzed, finding causation in correlation, and not "showing the work" so that results can be replicated.

Why must an actuary be honest? Because as the analyses become more complex, and as the demand for risk assessment (not only for insurance purposes) explodes, there are fewer people who are available to check the work; actuaries must be trusted not to force through results that please the powers that be.

I was glad that I didn't try to become an actuary.

Just for fun: the article presents a sample question involving rudimentary statistics and algebra. Without using a calculator, I could still solve it. (My high school self could have figured it out much more speedily but would also have snickered throughout at the phrase "blue balls.")

“An urn contains 10 balls: 4 red and 6 blue. A second urn contains 16 red balls and an unknown number of blue balls. A single ball is drawn from each urn. The probability that both balls are the same color is 0.44. Calculate the number of blue balls in the second urn.”

B = blue balls in second urn.

Probablility of drawing two red balls is .4 x (16/(16+B))
Probablility of drawing two blue balls is .6 x (B/(16+B))

.4 x (16/(16+B)) + .6 x (B/(16+B)) = .44
6.4 + .6B = .44(16+B)
.16B = .64
B = 4

Multiple choice would have been easier because you could just plug in the various alternatives until the combined probability of two same-color balls was .44, but that's not how the problem was presented.