Qualitative vs Quantitative Risk Analysis: Which One Should You Choose for Finserv?

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Risk analysis.

These two little words pack a whole lot of meaning.

It’s a staple of financial services (Finserv) and one that’s at the root of several different fields — from insurance and stock trading to quant and many more. In short, it allows firms to identify, measure, and mitigate potential threats, promising financial stability and informed decision-making.

But quantitative risk analysis isn’t the only type of analysis that exists.

Sometimes you need to get more qualitative with your analyses.

So which one do you choose?

(Plot twist: It’s both.)

In this article, we’ll look at the differences between quantitative and qualitative analysis, how you can use each effectively, and which scenarios need which type of analysis.

What is quantitative risk analysis?

Quantitative risk analysis involves using collected and processed data to calculate risk and model trends.

This approach focuses exclusively on hard numbers and quantifiable factors to specify how different events are likely to impact businesses and financial processes, making it an essential tool for making data-driven decisions.

Why use quantitative risk analysis?

As quantitative risk analysis is based on hard data, we’re not just making assumptions. Instead, we’re analyzing information and creating models to help explain financial realities and patterns.

Quantitative risk assessment also uses statistical models and techniques to give precise, objective risk measurements. These tools help quantify the probability of different outcomes and their potential impacts, making it easier to make informed decisions.

Here are some of the common tools you might use in your quantitative risk analysis:

• Expected monetary value (EMV) calculations
• Monte Carlo simulations
• Decision trees
• Time series analysis

When to perform quantitative analysis

The benefits of quantitative risk analysis include providing the solid, data-backed foundation Finserv companies need to navigate complex decisions, reducing uncertainty and enhancing the accuracy of predictions.

Quantitative risk management and analysis will come in handy in several scenarios, such as:

Focusing on financial risks

You can use quantitative analysis to understand the potential benefits and pitfalls of risky decisions like making a particularly daring trade, or deciding whether to invest in a project.

For example, a financial institution can use quantitative analysis to assess the risk of a new investment portfolio by examining historical performance data. That past data can help a company understand the probability of the portfolio failing — and, if it does fail, how big the losses are likely to be.

Observing market trends

When there’s a shift in market trends, quantitative analysis helps to determine if this is a short-term fluctuation, an abnormal event, or a developing trend.

It’s crucial for traders and investors, who need to understand whether a sudden market movement is an opportunity or a big red flag.

Preparing for new launches or strategies

Needless to say, investing in new products or trading strategies comes with a whole slew of risks. Quantitative analysis helps you understand and mitigate those risks.

For example, before launching their new product, a company might analyze market data to predict potential sales and spot how competitors, the economy, or consumer trends might threaten their profits.

Weighing up alternatives

When companies want to shift gears, pivot, or try something new, they’ve got to understand whether the numbers back up the move, or if the risk is too high.

For example, a company considering a major strategic move might analyze financial projections and market data to check that they’re not making a major mistake.

Embedded finance and quantitative risk analysis

Embedded finance, which integrates financial services into non-financial platforms, generates mountains of transactional and behavioral data.

The good news is that you can use this data to improve your quantitative risk analysis by:

• Improving credit scoring — real-time transaction data from embedded finance solutions can improve traditional credit-scoring models, providing more accurate risk assessments.
• Enhancing fraud detection — by using advanced analytics on data from embedded finance platforms to identify unusual patterns and flag potential fraud in real time.

What is qualitative risk analysis?

On the flip side, qualitative risk analysis looks beyond the numbers. It uses experience, expertise, and previous judgments to shape decisions.

This method is particularly useful when there isn’t enough data, or when data analysis doesn’t reveal a clear trend.

Qualitative analysis includes creating risk matrices, conducting interviews with experts, and consulting experienced stakeholders.

Why use qualitative risk analysis?

Qualitative risk analysis is vital for finding potential challenges in new markets, understanding competition, and performing deep SWOT analysis — that’s Strengths, Weaknesses, Opportunities, and Threats.

Qualitative analysis also helps create a broader view of a company’s ecosystem. Why? Because some of the most important factors aren’t easily quantifiable.

Say you’re a company entering a new market. You might conduct interviews with local experts to understand cultural nuances — something data alone can’t show.

When to perform qualitative risk analysis

You might want to carry out qualitative analysis in several scenarios, such as:

When it’s too hard to quantify risks

Some risks — like reputational or legal risks — can’t be fully captured through data alone.

For example, if a public relations crisis could damage a brand’s reputation, it’s difficult to quantify exactly how that damage will translate into financial loss. But it can be assessed qualitatively, either by analyzing past incidents or seeking expert opinions.

When data isn’t available

Qualitative analysis gives a good framework for risk assessments when you don’t have much historical data to hand — maybe a company is expanding into new markets, or adopting strategies that haven’t been tried before.

Or maybe a company’s launching a new but unprecedented product on the market. In this situation, qualitative methods can help you gauge potential risks based on expert judgments and analogies to similar products.

When unexpected risks arise

In today’s world, anything can happen — the COVID-19 pandemic is a prime example. Emerging risks, especially those never witnessed before, mean you need qualitative judgment to understand their potential impact. That’s why qualitative analysis is essential, especially in the early stages, to understand the broad implications.

When risks are more complex

Let’s say you’ve got risks that involve multiple factors that aren’t just financial — think political, environmental, or social factors. Most of these things can’t be easily measured in hard numbers.

Qualitative analysis lets you look at the full scope of risk across a large-scale project to make sure you’re never left unprepared.

Embedded finance and qualitative risk analysis

Embedded finance doesn’t just need quantitative risk analysis. Qualitative risk analysis can also be essential when it comes to analyzing two types of risk:

• Regulatory risks — integrating financial services into non-financial platforms means you’re much more likely to come up against regulations that have never affected your business before. Qualitative analysis helps you to understand these complexities and prepare you for potential regulatory changes.
• Operational risks — moving into embedded finance can mean companies have to deal with operational risks that they’ve never encountered before — specifically technical risks like system failures or cybersecurity threats. Qualitative assessments can evaluate these risks and suggest strategies to help combat them.