Risk Analysis: Using Quantitative Analysis To Identify and Mitigate Business Risks

While there’s no crystal ball businesses can use to predict the future, a robust risk assessment strategy can help them plan for unexpected events. A risk management strategy can help business leaders anticipate and mitigate risks that have the potential to cause severe financial or reputational damage or even destroy their companies.¹

The collapse of Silicon Valley Bank (SVB) is an example of a business disaster caused by a system failure in its risk management process. Although SVB had a risk committee and a process to perform risk assessments, the bank didn't use the assessments to inform risk management oversight. Without this oversight, SVB engaged in practices such as over-leveraging treasuries and mortgage-backed securities that eventually led to a liquidity crisis.²

This article will cover risk assessment and management through quantitative risk analysis, including its methods and use cases, and will touch briefly on qualitative analysis.

Quantitative risk analysis (QRA) calculates the risks involved in various activities or projects. Using mathematical models, statistical methods, and numerical data, analysts can evaluate the probability and impact of different risks. Unlike qualitative risk analysis, which provides a descriptive assessment, QRA assigns numerical values to the likelihood of a risk occurring and its potential impact. This numerical value is the basis for a more precise and data-driven understanding of risks.¹

Key components of QRA include probability distributions, which model the uncertainty associated with risk factors, and metrics like Expected Monetary Value (EMV) to provide a dollar-figure risk estimation. Tools such as a Monte Carlo simulation-which uses repeated random sampling to obtain results-are often used to simulate various outcomes based on the assigned probabilities and impacts, giving decision-makers a comprehensive view of potential risks.³

Business leaders can use QRA to make informed decisions, create contingency plans, and allocate resources more effectively. Industries such as finance, healthcare, construction, and energy rely on quantitative risk analysis for precise risk assessment to prevent losses and increase revenue.⁴

A company's risk management strategy seeks to protect the company from the various risks that can threaten it without hindering growth and profitability. The risk management process is iterative and can be divided into risk assessment, management, and communication. As the SVB example shows, the results of risk assessments must be communicated through the proper channels so that others understand the risk landscape and use the information to inform operations management.⁵

The basic steps in qualitative and quantitative risk analysis are similar, with the key differences found in the types of data collected and the evaluation tools. The next few paragraphs outline the process of quantitative risk analysis.

First, analysts will identify the risk factors that could affect the project or business activity. The risk identification process should be comprehensive and often emerges from prior qualitative risk analysis. Quantitative risk analysis is appropriate for risks that can be expressed numerically, such as market volatility, interest rates, or production delays.⁶

The next step involves collecting data related to the identified risk factors. This data can come from historical records, industry reports, or expert opinions. For accurate analysis, the data should be as current and specific as possible.⁷

Once they have the data they need, analysts will select statistical models and analytical tools. Commonly used methods include Monte Carlo simulations, decision trees, and sensitivity analysis. The choice depends on the complexity of the project and the data available.³

For each risk factor, analysts choose a probability distribution that models a range of possible outcomes. They then evaluate the likelihood and severity of the risk occurring within the context of that probability distribution. For example, a normal distribution might be used for factors like project cost overruns. In contrast, a Poisson distribution⸺a model that describes the distribution of a specific event in a time period⸺might be appropriate for modeling the occurrence of a specific type of system failure.⁸

The selected models or simulations will generate a range of potential outcomes and their associated probabilities. This often involves running thousands or even millions of simulations to capture a full spectrum of possibilities.⁶

The results should show the likelihood and impact of different outcomes. Analysts and business leaders use this information to evaluate various risks and decide on appropriate responses.⁶

QRA can be used in many industries to drive data-based decisions and strategies. For a closer look at quantitative analysis methods, check out this Seattle U blog post. Some of the most common use cases for quantitative risk assessments are discussed below.

In the finance and investment sector, QRA plays a critical role in portfolio management and market risk assessment. Investors use QRA to analyze the risks and returns of different investment options and optimize asset allocation. This numerical insight allows for a more data-driven approach to balancing a portfolio. Similarly, financial institutions implement QRA to evaluate the impact of market fluctuations on their portfolios. By doing so, they can develop hedging strategies to mitigate potential losses, making financial planning more precise and reliable.⁹

QRA is a valuable tool in many areas of healthcare, including clinical trials and hospital resource management. Pharmaceutical companies use QRA to quantify the likelihood of success for new drugs or treatments so they can allocate their resources more effectively and decide whether to move to the next phase of testing.¹⁰ Additionally, healthcare providers utilize QRA to assess the risks associated with patient care, such as the probability of adverse events. This information aids in optimizing staffing levels, medical supply requirements, and even the implementation of preventative measures.¹¹

In manufacturing, QRA is instrumental in supply chain management and quality control. Manufacturers can assess and quantify the risks related to supplier reliability, production delays, and distribution challenges. By doing so, they can prioritize vendor relationships and build more resilient supply chains. Additionally, quantitative risk analysis aids in quality control by modeling the probability and impact of defects or failures in manufacturing processes. This leads to more robust quality assurance protocols and helps minimize the risks of product recalls.¹²

Qualitative analyses are valuable when there is limited available quantitative data, when risks are complex and hard to quantify, and when exploring relationships between events. As noted above, a qualitative risk analysis can be a good first step in a QRA.

Qualitative assessment has applications across industries and business functions, and is particularly useful for project managers, helping them deliver quality projects on time and on budget. That's why project risk management processes begin with the earliest planning phases of a well-managed business project.¹³

Examples of qualitative risk models include SWOT analysis, game theory, and decision matrices.⁶ Analytic tools for qualitative risk assessment include machine learning (ML) algorithms, natural language processing (NLP) software, qualitative data analysis software (QDAS) tool packages, spreadsheets and statistical software.

When the appropriate qualitative and quantitative risk assessment processes are complete, the next steps in a risk management strategy are to communicate the results and adopt risk responses. Based on the risk assessment results and in the context of larger corporate strategy, business leaders will devise responses that can include reallocating resources, buying insurance, or implementing contingency plans. In some cases, management will decide that risk acceptance or risk monitoring is the best strategy.⁶

Seattle University’s Albers School of Business and Economics Online Master’s in Business Analytics will give you the solid foundation to bring strategic data-driven insights to today’s organizations. The world-class MSBA curriculum teaches you to drive sustainable innovation and manage business risks. Contact an admissions outreach advisor to learn more.