What an Actuary Actuarily Does: The Scientific Foundations of Risk Management

In the upper echelons of corporate leadership, executives are tasked with making decisions that impact not only the trajectory of their companies but also the financial security of employees, shareholders, and clients. In this landscape, the need for precision in risk management has never been greater. Actuaries are the professionals who provide the scientific backbone for those decisions, quantifying uncertainty and applying rigorous methodologies to mitigate risk.

While actuaries may not often be visible in day-to-day operations, their work is integral to the financial and strategic planning of industries such as insurance, healthcare, finance, and beyond. Understanding the depth of their expertise is key for any executive looking to make informed, forward-thinking decisions.

Actuaries: The Architects of Predictive Modeling

At its core, actuarial science is about managing risk through statistical modeling and financial theory. Actuaries build models that project potential outcomes for future events that carry financial risk—whether it’s determining the most effective way to price insurance premiums, managing the long-term solvency of pension plans, or analyzing catastrophic risks such as pandemics or natural disasters.

According to the Society of Actuaries (SOA), the profession exists to help businesses and governments make data-driven decisions to “safeguard the financial health” of their organizations. This is more than just managing today’s numbers; actuaries focus on long-term projections and incorporate a wide variety of variables—demographic shifts, market volatility, regulatory changes, and environmental factors—into their models. It is their ability to quantify uncertainty that makes their insights invaluable.

Anna Rappaport, an actuary specializing in retirement and healthcare risk, has noted, “Actuaries take on complex financial and societal challenges, leveraging data and models to provide clarity in uncertain environments.” This clarity allows executives to strategize with greater confidence, knowing that decisions are grounded in scientific rigor rather than intuition.

Real-World Applications: Managing Risk in an Unpredictable Era

The actuary’s role became particularly visible during the COVID-19 pandemic, which introduced new and volatile variables into financial forecasting. Actuaries were crucial in recalibrating models for mortality, healthcare costs, and the long-term impacts of a post-pandemic world. As the American Academy of Actuaries reported, actuaries played an essential role in revising assumptions that underpinned insurance pricing, pension funding, and broader economic models during the pandemic’s early stages.

For example, the pandemic led to a rethinking of traditional mortality tables and life expectancy models, as actuarial forecasts had to quickly integrate emerging data on infection rates, public health measures, and their impact on healthcare systems. The agility with which actuaries adapted these models enabled insurance companies, healthcare providers, and even government agencies to navigate an unprecedented crisis.

“COVID-19 challenged our long-held assumptions about risk and uncertainty,” said one actuary involved in pandemic response efforts. “We had to continuously refine our models to ensure they reflected the fast-changing reality. This is where actuarial expertise truly shines—bridging the gap between data and actionable insights.”

The Science of Risk: A Deeper Look into Actuarial Methodology

At the heart of actuarial science is predictive modeling, a technique that executives may be familiar with in other business contexts, but with added layers of complexity when applied to risk. Actuaries use stochastic models to account for the randomness inherent in financial forecasting. Unlike deterministic models, which assume a fixed set of outcomes, stochastic models allow for a range of possibilities based on probability distributions, making them especially suited for unpredictable events such as natural disasters or economic shocks.

For example, consider how actuaries model the risks of extreme weather events. Actuaries use Monte Carlo simulations to generate thousands of potential outcomes, helping companies and governments understand the probability of losses under various scenarios. This method enables them to price insurance, structure reinsurance contracts, and plan reserves with high precision.

In the healthcare sector, actuaries rely heavily on survival analysis and life table models to assess risk. These models, refined with updated data on life expectancy, morbidity, and treatment outcomes, are critical for determining the premiums and costs associated with health and life insurance products. For executives in the healthcare industry, actuarial insights provide the financial foundation to offer competitive, sustainable products in a volatile market.

According to the U.S. Bureau of Labor Statistics, the demand for actuaries is expected to grow by 21% from 2021 to 2031—a testament to the increasing need for sophisticated risk management in a rapidly changing world. For companies that seek stability and precision, actuaries are an essential part of the strategic puzzle.

Where Actuarial Expertise Meets Executive Decision-Making

Actuaries’ contributions extend far beyond the confines of insurance. They are increasingly present in industries such as investment banking, pension fund management, and corporate finance, where their expertise helps mitigate complex financial risks.

In investment banking, actuaries help assess the risk of structured financial products, such as derivatives, ensuring that institutions can minimize exposure to volatility in the markets. Their ability to model the potential outcomes of highly leveraged investments has become a critical skill in managing the balance between profitability and risk.

In the pension fund sector, actuarial analysis is indispensable for managing long-term liabilities. As retirement trends shift and life expectancy increases, pension plans face mounting pressures. Actuaries calculate the contributions needed today to fulfill promises made to beneficiaries decades from now, ensuring that companies can meet their obligations without jeopardizing financial stability.

Bob Conger, former president of the Casualty Actuarial Society, summarized it well: “Actuaries are risk managers at their core. They’re helping companies solve problems before they become crises, ensuring that decisions made today can sustain the future.”

Quantifying the Unknowable: The Actuarial Edge

In the executive suite, decisions are made under uncertainty—whether you’re determining how much capital to allocate, how to price a new product, or when to expand into a new market. Actuaries provide the rigorous, data-driven insights that can turn uncertainty into manageable risk. Their work transforms ambiguous data into actionable forecasts, giving leaders the clarity to make strategic decisions with confidence.

According to the Casualty Actuarial Society, actuaries are highly compensated for their expertise, with median salaries exceeding $100,000 and more experienced professionals earning well over $200,000. The financial commitment companies make to employ actuaries reflects the enormous value they bring in safeguarding long-term financial health.

The Strategic Value of Actuarial Science

For executives, understanding what actuaries actuarily do is more than just appreciating their number-crunching abilities—it’s about recognizing their strategic importance. In an increasingly uncertain world, actuaries offer the scientific tools to navigate risk with foresight and precision. They don’t just solve today’s problems; they help companies prepare for tomorrow’s challenges, with data and analytics as their guide.

As your organization faces an ever-evolving landscape of financial, regulatory, and operational risks, leveraging actuarial expertise will be essential. Their insights aren’t just numbers on a spreadsheet; they are the foundations upon which sound, sustainable decisions are built.

In short, actuaries enable executives to plan with confidence, knowing that every decision is backed by rigorous analysis and scientific precision. As global risks grow in complexity, actuaries are the key to ensuring that businesses not only survive but thrive in an uncertain world.

Measuring the Immeasurable: Applying Empirical Evidence to Areas Typically Governed by Intuition and Faith

We live in an age where we’re obsessed with numbers—steps walked, hours slept, calories burned. It’s like we’re all starring in our own little Truman Show, and data is the all-seeing eye. But what happens when you try to measure things that don’t quite fit in a spreadsheet? How do you put numbers to something as elusive as love, faith, or happiness?

Turns out, it’s not as impossible as it seems.

The challenge of measuring the immeasurable is one that transcends academic disciplines, from psychology to business strategy. In fact, many of the most important decisions we make in business and life rely on these abstract concepts. So, let’s explore how science is bringing structure to areas we typically navigate by instinct.

The Economics of Intuition: Trusting Your Gut with Data

Business leaders often describe their decision-making process as a blend of data analysis and intuition. Steve Jobs famously said, “Have the courage to follow your heart and intuition. They somehow already know what you truly want to become.” But is intuition really just a gut feeling, or can we quantify it?

In fact, neuroscience offers insight into what we call “gut feelings.” Researchers argue that intuition is a form of rapid cognition, where the brain processes large amounts of information based on prior experiences. Malcolm Gladwell explored this in Blink, showing that experts can make high-quality decisions in split seconds, relying on pattern recognition developed over years of experience. This isn’t magic—it’s cognitive processing occurring below the surface, making intuition a measurable, albeit complex, cognitive phenomenon.

Research published in the journal Neuroscience of Decision Making found that intuitive decision-making activates the brain’s insula and ventromedial prefrontal cortex, both areas linked to emotional processing and complex decision-making. Intuition, therefore, can be understood as a cognitive shortcut that allows for rapid decision-making in high-stakes environments, whether you’re leading a company or navigating a life event.

Happiness: Quantifying an Abstract Concept

Happiness is often considered deeply personal and, by nature, subjective. Yet, psychologists and economists have made significant strides in quantifying it. Martin Seligman, the founder of Positive Psychology, proposed a well-known “happiness formula”:

H = S + C + V

In this equation, H represents happiness, S is a biological “set point” (your baseline level of happiness), C reflects life circumstances (such as career, relationships, and health), and V covers voluntary activities (what you do with your time).

While this may seem overly simplistic, the data behind it is compelling. Studies consistently show that around 50% of happiness is attributed to the set point (genetic predispositions), 10% to circumstances, and a surprising 40% to voluntary activities. In business terms, this implies that investing time and resources in intentional actions—like building meaningful relationships or pursuing hobbies—can significantly boost long-term happiness. It shifts the notion of happiness from being something abstract to a factor that leaders and individuals can actively manage.

Even in the corporate world, companies like Google and Zappos have invested in “happiness engineering,” recognizing that employee satisfaction has measurable impacts on productivity and retention. Surveys and behavioral data serve as proxies to measure overall job satisfaction, well-being, and the effectiveness of company policies.

The Science of Serendipity: Creating Your Own Luck

Luck may feel random, but science suggests that it can be influenced, if not controlled. Richard Wiseman, a psychologist at the University of Hertfordshire, conducted extensive research on luck and found that people who consider themselves “lucky” tend to exhibit certain behaviors that increase their chances of positive outcomes. In his book The Luck Factor, Wiseman outlines key traits such as openness to new experiences, resilience in the face of adversity, and the ability to notice opportunities others might miss.

Wiseman’s experiments showed that people who self-identify as lucky often create their own “luck” by cultivating these traits. His research suggests that being open to the unexpected and maintaining a positive outlook are measurable factors that contribute to what we commonly call “luck.” Businesses can take note of this when fostering innovation—promoting a culture of curiosity and risk-taking can, in essence, increase the likelihood of serendipitous discoveries.

Statistically speaking, lucky people often make their own breaks by interacting with a wider range of people and exposing themselves to more diverse experiences. In organizational terms, this might translate to encouraging cross-department collaboration or networking outside traditional industry boundaries.

Measuring Faith: When Pascal’s Wager Meets Business Strategy

Faith, whether in a religious or secular context, is another area traditionally considered beyond the reach of empirical study. However, Blaise Pascal, a 17th-century mathematician, introduced a compelling argument for belief that has practical applications in decision theory. Pascal’s Wager argues that, in the absence of certainty, it’s more rational to bet on the existence of God because the potential rewards outweigh the risks.

This line of thinking isn’t limited to theology—it extends to modern business strategy, particularly when leaders must make decisions under conditions of uncertainty. In these cases, risk management is key. Taking a calculated risk, or even a “leap of faith,” is often backed by data, forecasts, and contingency plans.

For example, Amazon’s decision to launch AWS was initially considered a risk. But by betting on the future of cloud computing, Jeff Bezos used empirical data to guide what was, at the time, an unconventional move. He wasn’t betting on faith alone; he was calculating the odds based on trends in technological adoption and business needs.

The Fibonacci Sequence: Finding Order in Chaos

Even nature seems to have a way of revealing patterns in what initially appears random. Take the Fibonacci sequence, a series of numbers where each one is the sum of the two preceding ones: 0, 1, 1, 2, 3, 5, 8, and so on. This sequence appears in everything from sunflower spirals to stock market fluctuations, suggesting that even seemingly random phenomena follow underlying mathematical patterns.

From an empirical standpoint, this is significant. The natural world operates in ways we might not fully understand yet, but by studying these patterns, we gain valuable insights. The business world has similarly sought to apply pattern recognition and predictive algorithms, whether through machine learning or financial modeling. Algorithms, much like Fibonacci sequences, attempt to bring order to chaos, finding trends in what might otherwise seem random.

Conclusion: Bridging Data and Intuition

The ability to measure the immeasurable has practical implications for both personal and professional development. From happiness to luck, faith to intuition, there are empirical methods for understanding these abstract concepts that go far beyond traditional metrics. For business leaders and graduate students alike, the challenge is to blend the best of both worlds: data-driven decision-making with the intangible insights that come from experience and intuition.

As Albert Einstein famously said, “Not everything that can be counted counts, and not everything that counts can be counted.” Yet, with the right approach, we can inch closer to quantifying even the most elusive aspects of life, business, and the human experience.

Now, how can you apply these principles to measure the immeasurable in your own field? Whether it’s innovating in business, pursuing personal happiness, or embracing new opportunities, finding the balance between faith and facts might just be the key to success.

Data Cleaning: The Spin Cycle Your Dataset Needs

Ah, data. It’s the lifeblood of your analytics and the glittering gold of machine learning. But before you can extract that precious insight, there’s one not-so-fun step to conquer: data cleaning. Yes, this is the laundry day of data science, where you scrub, de-dupe, and declutter your dataset until it’s fit for predictive modeling. Let’s make it fun (or at least tolerable) with some wit and wisdom on the essential art of data cleaning and preprocessing.

1. Missing Data: The Ghosts of Data Past

Ever opened a spreadsheet and found holes so big you could drive a truck through them? Yeah, that’s missing data for you. Whether it’s because someone forgot to input values or your system hiccuped mid-collection, missing data is inevitable. The key is to know how to deal with it.

• Option 1: Drop the NaNs (missing values), like ghosting someone after a bad date. If the data isn’t important or the missing percentage is tiny, just hit “delete” and move on.

• Option 2: Fill them in (imputation), because hey, sometimes we need to play detective. You can fill in missing values with averages, medians, or if you’re feeling spicy, by predicting what should be there based on other data points.

2. Outliers: The Drama Queens of Your Dataset

Outliers are those bizarre data points that stick out like a sore thumb. Maybe it’s a customer who bought 10,000 units of your product while everyone else bought 10. They can throw off your models if left unchecked.

• Ignore Them: Maybe that 10,000-unit purchase really happened (hello, bulk buyer!). Sometimes the outliers are telling a true story, and you don’t want to ignore valuable data.

• Cap or Transform: If you suspect they’re anomalies, cap them at a reasonable limit or transform the data to reduce their impact. You’re basically telling them, “Hey, calm down.”

3. Duplicate Data: The Overenthusiastic Clones

Duplicates are the overeager interns of your dataset—helpful, but when there’s too many, you’re going to have a mess on your hands. Identifying and removing duplicate rows is crucial because duplicates artificially inflate your metrics.

• Pandas .drop_duplicates(): If you’re in the Python world, Pandas offers this handy function. One click and poof! Your clones are vaporized, and your dataset looks much sleeker.

4. Inconsistent Data: The Frenemies

Nothing is more frustrating than inconsistent data. One column might say “New York City,” while another says “NYC.” Are they the same? Absolutely. Do machines know that? Not a chance.

• Standardization: Like herding cats, but doable. Make sure your dates, text values, and categorical data all follow the same format. This might involve converting “January 1st, 2024” to “2024-01-01” (because let’s face it, machines don’t appreciate pretty formatting).

5. Data Type Issues: The Wrong-Sized Puzzle Pieces

Have you ever tried to do math with a string? Neither has your model, because that’s just not how it works. Data type errors are sneaky little devils. One second you’re trying to compute the average age, and the next, you find someone entered “twenty-five” instead of “25.”

• Conversion: Convert data types as needed. Use type coercion in Python (e.g., int() for integers, float() for decimal numbers) to make sure everything’s playing nicely in your numerical sandbox.

6. Feature Scaling: Making Everyone Play Fair

When one feature is in the range of thousands and another is between 0 and 1, you’ve got a scale problem. You wouldn’t judge a fish by how well it climbs a tree, right? Feature scaling brings everyone down to the same level.

• Standardization: Scale your features so they have a mean of 0 and a standard deviation of 1. This is great for distance-based algorithms like k-means clustering.

• Normalization: Alternatively, you can normalize your data to a 0-1 range, perfect for algorithms that don’t assume normal distributions.

7. Encoding Categorical Data: Translating for the Machines

If your dataset has text labels (like “Yes” or “No”), you’ll need to translate them into something machines can understand. It’s like getting everyone in the room to speak the same language.

• One-Hot Encoding: This transforms your categorical columns into binary columns (i.e., “Yes” becomes [1, 0] and “No” becomes [0, 1]). Think of it as the universal translator of machine learning.

8. Splitting Data: Train, Test, Repeat

Before you pat yourself on the back for a clean dataset, don’t forget to split it. A portion of your data will be used to train your model, while the rest will be used to test it. This ensures your model can handle new, unseen data.

• Train-Test Split: A typical ratio is 80/20 (80% training, 20% testing), but you can adjust based on your needs. Don’t cheat—testing your model on the same data it was trained on is like grading your own homework.

Wrapping Up: Shine Bright Like a Clean Dataset

Data cleaning isn’t glamorous, but it’s the foundation of any successful analytics project. Remember, a dirty dataset leads to misleading insights and bad predictions, and no one wants that. Treat your data like a car—regular maintenance keeps it running smoothly, and the cleaner it is, the better it performs.

Now, go forth and clean with confidence. Your future models will thank you! Or at least your business partners will.

About the author: Curt Jones is the founding partner at Proklamate, a fractional business intelligence firm in Boise, Idaho.