Understanding Risk: A Basic Introduction

“They made risky investments.”

“He’s a risk-taker.”

“It’s a calculated risk.”

“That’s too much risk for me.”

Every day we hear people talking about risk, especially lately. But what really is “risk”, and how do we deal with it?

Up until a couple months ago, I had treated the word risk like we treat the world love or happiness. An abstract concept that described an amorphous feeling, rather than a concrete concept. Risk was intuitive; you knew it when you saw it, but you didn’t go around quantifying it.

Studying risk in the financial services industry has opened my eyes to the science of risk. How we decompose, quantify and handle risk. And how to develop principles of risk management that have practical application throughout life. This article gives an introduction to the basic concepts surrounding risk using the example of driving a car. Future articles will delve deeper into specific areas, and address how risk applies in entrepreneurship, angel investing and daily life.

What Is Risk?

Snow beats down against the windows of your house. The road is covered with ice. You watch as a car slides over into the oncoming lane. You make an intuitive risk assessment: too risky, you’re staying home today.

What was going through your mind? That the chance of an accident was too high. That if you did get into an accident, you might get killed. That there were too many variables, like bad drivers, iced roads, and poor visibility that you couldn’t control.

Risk is a combination of chance and severity. The chance, or probability, that an incident will occur plus the severity, or impact, if that incident does occur. High risks either occur often, or occur infrequently, but have devastating results.

Assessing Risk

To assess risk, we figure out what the chance of a given outcome happening is, and what the impact that outcome will have. We usually measure the chance of occurrence using a probability, such as 20% or 1 in 5. Impact can be defined in numerous ways. For today’s discussion, we’ll use the amount of money lost if the incident occurs, since this is a common metric used to measure risk.

Let’s go back to our driving example. The difficulty in assessing risk lies in measuring the probability and the impact. And while accident statistics for driving are well-known, the probability of having an accident during a single trip on a specific day is difficult to calculate. To make the math easy, we’ll say the probability is 10%. If we assume the average crash during a snowstorm causes $5,000 worth of damage, then the calculated risk equals $5,000 * 10%, or $500.


So what does a risk of $500 actually mean. It means, on average, I lose $500 every time I drive in the snow. In reality, though, I lose nothing unless I have an accident, and if I do, then I lose $5,000. So risk measurements, by themselves, aren’t very useful. Risk measurements help when evaluating different alternatives, or when taking a risk multiple times.

Let’s consider a different scenario. Instead of a snowstorm, it’s a bright and sunny day. But because there are more people on the road, let’s assume the probability of an accident is higher, at 20%, but the average accident cost is lower, at $3,000. Our calculated risk now equals $3,000 * 20% = $600.

The net result: in our example, driving during a sunny day is 20% riskier than driving during a snowstorm. While a purposely absurd example, this demonstrates how risk can be calculated and compared to evaluate the relative risks of taking an action.

Components of Risk

Up until now we’ve been talking about risk as a single idea or number. And while you can view it as such, it is often useful to break risk into components and look at each component separately.

Going back to our driving example, the chance of getting into an accident is dependent on multiple factors: the weather, the mechanical state of your car, the mental state of the other drivers on the road, and the traffic congestion, to name a few. The risk of getting into an accident can therefore be broken down into “Weather Risk”, “Mechanical Risk”, “Other Driver Risk” and “Congestion Risk”, respectively. Treating each of these components of risk separately enables us to analyze and deal with those risks more effectively.

For instance, some risks, like mechanical risk, you’ll know before you get out on the road. While other risks like congestion risk, you may not know until you get into traffic. Either way, knowing the types of risks involved gives you the knowledge to deal with those risks.

Dealing with Risk

When faced with a decision involving risk, what do you do? Maybe you calculate the risk, like we did above, or break the risk into its components. But ultimately you need to do one of three things: accept the risk, mitigate the risk or reject the risk.

Accepting the risk means being willing to take the chance that the loss will occur, and hopefully, that if the loss does occur, being able to survive the loss. If we don’t think we can survive the loss, or feel uncomfortable with our chances, we reject the risk, deciding to take a conservative approach with less risk. Alternatively, we could mitigate the risk.

Mitigating a risk involves taking steps to lower either the chance the negative event will occur, or the impact of that event. Understanding the components of risk helps us determine how to mitigate the risk, by mitigating one or more of its components.

In our car accident example, we mitigate the risk of an accident by doing proper maintenance on our car (mechanical risk), or by only driving during good weather (weather risk). This lowers the chance we’ll have an accident. Maintaining insurance on your car is another form of mitigation. In this case, you’re mitigating the amount of loss you’ll have if you do have an accident.

Finally, when dealing with risk, it’s useful to determine which components of risk are within your control and which are not. The mechanical risk of your car’s brakes failing is controllable by doing proper maintenance, while the state of mind of other drivers can’t be controlled.

In The Future: Combinations, Correlations and Confidences

In this article, I aimed for a brief introduction to three concepts of risk: how to measure it, how to componentized it, and how to deal with it. In the future, I’ll address how the risks of individual components can be measured and combined into the risk of an entire scenario; how the components of risk might be correlated with each other and the consequences of that; and how the confidence level of the probability and impact associated with a risk affects how you evaluate and compare risks. I’ll also delve into building a risk model to evaluate angel investments and improve their outcome.

Until then, remember that all risk analysis relies on models of how we see the world, and the model is not reality. Cheers.

Sidebar: Calculating Auto Accident Risk

For the mathematically inclined, I went through the exercise of calculating the risk associated with a single one-way trip. Since I didn’t have the data for losses for snowy versus normal conditions, this is across all accidents. Here’s how it worked.

First, I took my data from the Car-Accidents.com, and the FHA Highway Statistics web sites. According to these sites, the base data indicates 2006 had:

  • 6,420,000 accidents
  • 202,810,438 licensed drivers
  • $230,000,000,000 in losses

From this, I assumed:

  • Drivers drive an average of 4 one-way trips per day, 365 days per year


  • Total trips per driver per year = 4 one-way trips * 365 days = 1,460 trips
  • Total trips = 202,810,438 licensed drivers * 1,460 trips/drivers = 296,103,239,480 trips
  • Total accidents = 6,420,000 accidents
  • Chance of accident on across all trips = 6,420,000 accidents / 296,103,239,480 trips = 1 in 46,121 chance
  • Loss per accident = $230B / 6,420,000 accidents = $35,825
  • Risk of accident for single trip = $35,825 * 1 in 46,121 chance = $0.78

So the calculated risk of driving one-way is $0.78, based on a chance of getting in an accident of 1 in 46,121 and an average loss of around $35,825, which likely includes medical and other costs.

So how is $0.78 per trip relevant. Well, given our assumption of 1,460 trips per year, this means the average loss for an insurance company per driver is $0.78 * 1,460 = $1,138.80. That’s before accounting for any of the insurance companies costs for sales, marketing or administration. Which explains why insurance rates can be so high. Of course, my assumptions are just guesses and I didn’t include other risk factors like average trip length, but you get the point.

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