Individuals have a different kind of relationship with insurance than what they have with any other product or service. Though being the most effective risk mitigation tool, it still requires a hard push from insurers and regulators to make people purchase. The thought of insurance could evoke every other emotion except joy in an individual. The main reason for this is that insurance is a futuristic promise that assures compensation when a covered risk event happens. This operates exactly opposite to the strong impulse of scarcity and immediacy bias.
As in any other industry, the persuadable events in insurance could be based on reactive or proactive triggers to encourage positive or discourage negative events. Depending on the intelligence ingrained in the back-end systems and the extent customer data is consolidated, the proactive persuasion events could be personalized to a customer and not just limited to generalized promotion of a new product or program. It could be performed for other persuadable events of the same policy for which the chat is in progress or expand to include policy events from other policies of the customer.
An indicative list of the persuadable events in an insurance policy could be categorized as given in Table 2.
My advice for actuaries looking to succeed outside the traditional function:
Push yourself to speak up more. Actuarial leaders know how to get the best out of you. It may not be the same experience with leaders in different areas of the business. So take advantage and speak up.
Have an opinion. So often I’ve worked with young actuaries who can perform incredible analysis but will stop short of providing an opinion and often defer to their manager or a senior actuary. As you move up the corporate ladder, you’re going to have to get more comfortable with providing your input with less and less data to work with.
If you are still toying with the idea of moving outside of a traditional actuarial role, it never hurts to widen your circle. Get to know the people in different areas of your company. Sit with an underwriter while they process a policy, shadow a claims adjuster while they work to help a customer during a stressful time of their life or connect with a member of your broker distribution team to find out what brokers or customers are saying. It will give you a whole new perspective on where the data you work with comes from.
There has been significant disruption in how organisations conduct business and the way we work over the past year and a half. However, financial modellers and developers have had to continue to build, refine and test their models throughout these unprecedented times. Figure 1 below summarises the areas we have covered in the blog series and how they fit together to form the practical guidance of how to follow and implement the Financial Modelling Code.
Looking at other great tools like R and Python, it can be difficult to summarize a single reason to motivate a switch to Julia, but hopefully this article piqued an interest to try it for your next project.
That said, Julia shouldn’t be the only tool in your tool-kit. SQL will remain an important way to interact with databases. R and Python aren’t going anywhere in the short term and will always offer a different perspective on things!
In an earlier article, I talked about becoming a 10x Actuary which meant being proficient in the language of computers so that you could build and implement great things. In a large way, the choice of tools and paradigms shape your focus. Productivity is one aspect, expressiveness is another, speed one more. There are many reasons to think about what tools you use and trying out different ones is probably the best way to find what works best for you.
It is said that you cannot fully conceptualize something unless your language has a word for it. Similar to spoken language, you may find that breaking out of spreadsheet coordinates (and even a dataframe-centric view of the world) reveals different questions to ask and enables innovated ways to solve problems. In this way, you reward your intellect while building more meaningful and relevant models and analysis.
This is an interesting one because it illustrates a version of “Littlewood’s Law of Miracles”: in a world with ~8 billion people, one which is increasingly networked and mobile and wealthy at that, a one-in-billion event will happen 8 times a month. Littlewood’s law is itself a special-case of Diaconis & Mosteller 1989’s “the Law of Truly Large Numbers”:
Because weirdness, however weird or often reported, increasingly tells us nothing about the world at large. If you lived in a small village of 100 people and you heard 10 anecdotes about bad behavior, the extremes are not that extreme, and you can learn from them (they may even give a good idea of what humans in general are like); if you live in a ‘global village’ of 10 billion people and hear 10 anecdotes, you learn… nothing, really, because those few extreme anecdotes represent extraordinary flukes which are the confluence of countless individual flukes, which will never happen again in precisely that way (an expat Iranian fitness instructor is never going to shoot up YouTube HQ again, we can safely say), and offer no lessons applicable to the billions of other people. One could live a thousand lifetimes without encountering such extremes first-hand, rather than vicariously.
More immediately, you should keep your eye on the ball: ask yourself regularly how useful news consumption has really been, and if you justify it as entertainment, how it makes you feel (do you feel entertained or refreshed afterwards?), and if you should spend as much time on it as you do; take Dobelli’s advice try to cut back or ignore recent news (perhaps replace a daily newspaper subscription with a weekly periodical like The Economist and especially stop watching cable news!); shift focus to topics of long-term importance rather than high-frequency noise (eg scientific rather than polling or stock market articles); don’t rely on self-selected convenience samples of news/opinions/responses/anecdotes brought to you by other people, but make your own convenience sample which will at least have different biases and be less extreme (ie don’t go off 10 comments online, ask 10 of your followers instead, or read 10 random stories instead of the top 10 trending stories); don’t have an opinion until you have a fulltext—insist on following back & getting fulltext sources (if you don’t have time to trace something back to its source, then your followers collectively don’t have time to spend reading it)7; read articles to the end (many newspapers, like the New York Times, have a nasty habit of including critical caveats—at the end, where most readers won’t bother to read to); discount things which are “too good to be true”; focus on immediate utility; try to reduce reliance on anecdotes & stories; consider epistemological analogues of robust statistics like simply throwing out the top and bottom percentiles of data; and pay attention to the trends, the big picture, the central tendency, not outliers.
The world is only getting bigger.
Publication Date: 18 February 2019 (last edited, visited 19 August 2021)
Do not put your career on hold. Continue to take (and hopefully pass) exams during the transition period.
Remember why you started taking actuarial exams in the first place. It was probably because you wanted to become an actuary or open doors to a variety of rewarding careers that combine business and the mathematical sciences. Unless your goals have changed, you should continue to take exams during the transition period. The SOA’s transition rules are usually very generous, so unless you repeatedly fail an exam that is being discontinued, you should not worry that the time spent studying for exams will be wasted.
Not all 10% increases are created equal. And by that we mean, assumption effects are often more impactful in one direction than in the other. Especially when it comes to truncation models or those which use a CTE measure (conditional tail expectation).
Principles-based reserves, for example, use a CTE70 measure. [Take the average of the (100% – 70% = 30%) of the scenarios.] If your model increases expense 3% across the board, sure, on average, your asset funding need might increase by exactly that amount. However, because your final measurement isn’t the average across all the scenarios, but only the worst ones, it’s likely that your reserve amounts are going to increase by significantly more than the average. You might need to run a few different tests, at various magnitudes of change, to determine how your various outputs change as a function of the volatility of your inputs.
Beginning in January 2022, pre-ASA candidates will also be able to begin work to earn new micro-credentials that recognize and demonstrate to employers their knowledge and skills gained along the pathway to ASA. These “milestone markers” will remain with candidates if they decide to leave the ASA pathway and are also applicable for those choosing to enter the pathway to only earn one or more micro-credential. All elements required to earn these micro-credentials are part of the ASA pathway and count in full toward earning the ASA and FSA designations.
These micro-credentials group together pathway components that represent distinct knowledge and skills to demonstrate the level of achievement candidates earn to employers, co-workers and their professional network. AQ/EQ and data science skills are driving changes to the ASA curriculum and will be incorporated into requirements for each micro-credential, allowing candidates the ability to demonstrate and build on those skills for their resume and jobs.
These micro-credentials do not make candidates qualified or “signing” actuaries; that work is reserved for those who earn the ASA and FSA designations. However, they do provide critical marks of candidates’ progress through the system and signal to employers the knowledge they’ve gained. We will be conducting an outreach program to employers to build awareness and support for the micro-credentials over the coming months.
In the pre-computer days, people used these approximations due to having to do all calculations by hand or with the help of tables. Of course, many approximations are done by computers themselves — the way computers calculate functions such as sine() and exp() involves approaches like Taylor series expansions.
The specific approximation techniques I try (1 “exact” and 6 different approximation… including the final ones where I put approximations within approximations just because I can) are not important. But the concept that you should know how to try out and test approximation approaches in case you need them is important for those doing numerical computing.
Author(s): Mary Pat Campbell
Publication Date: 3 February 2016 (updated for links 2021)
Publication Site: LinkedIn, CompAct, Society of Actuaries
When spreadsheets are created ad-hoc, the usage of time steps tends to be inconsistent: advancing by rows in one sheet, columns in another, and even a mix of the two in the same sheet. Sometimes steps will be weeks, other times months, quarters, or years. This is confusing for users and reviewers, leads to low trust, increases the time for updates and audits, and adds to the risks of the spreadsheet.
A better way is to make all calculations follow a consistent layout, either across rows or columns, and use that layout for all calculations, regardless if it requires a few more rows or columns. For example, one way to make calculations consistent is with time steps going across the columns and each individual calculation going down the rows:
Author(s): Stephan Mathys
Publication Date: June 2021
Publication Site: Small Talk at the Society of Actuaries
Working from home was a significant change for most actuaries. While some are looking forward to returning to work in the office, few would like to return to working in the office most or all of the time. After COVID-19 restrictions are fully lifted, approximately 65% of full-time respondents would prefer to work from home at least 3 days per week: 28% would prefer to work from home three days per week, 23% would like to work from home every day, and 14% would prefer to work from home 4 days per week.
In general, respondents who identify as women have a slight preference to work from home more frequently than do respondents who identify as men.
Examination of aggregate data on graduate admissions to the University of California, Berkeley, for fall 1973 shows a clear but misleading pattern of bias against female applicants. Examination of the disaggregated data reveals few decision-making units that show statistically significant departures from expected frequencies of female admissions, and about as many units appear to favor women as to favor men. If the data are properly pooled, taking into account the autonomy of departmental decision making, thus correcting for the tendency of women to apply to graduate departments that are more difficult for applicants of either sex to enter, there is a small but statistically significant bias in favor of women. The graduate departments that are easier to enter tend to be those that require more mathematics in the undergraduate preparatory curriculum. The bias in the aggregated data stems not from any pattern of discrimination on the part of admissions committees, which seem quite fair on the whole, but apparently from prior screening at earlier levels of the educational system. Women are shunted by their socialization and education toward fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects.
Science 07 Feb 1975: Vol. 187, Issue 4175, pp. 398-404 DOI: 10.1126/science.187.4175.398
Author(s): P. J. Bickel, E. A. Hammel, J. W. O’Connell