Looking back on my career path, it may appear fairly conventional at first glance: I started in the insurance industry as an actuary, later pursued a PhD in Mathematics, and eventually moved into banking to work in credit risk management. Along the way, I gained exposure to theory, modeling, and practical business applications across different areas of finance and risk.

However, once I became deeply involved in credit risk, I gradually realized something important: many times, we are “doing analysis,” but not necessarily fully understanding the underlying problem itself.

It was at this stage that I decided to pursue the CQF.

For me, this was never about adding another credential. Instead, it was about solving a more practical challenge: whether it was possible to connect risk, data, and modeling within a more unified framework.

From Actuarial Science to Credit Risk

I spent more than five years at Liberty Mutual Insurance working in actuarial roles and eventually became a credentialed actuary. During that time, I received rigorous training centered around risk modeling, pricing, reserving, and long-term liability assessment. The actuarial framework is exceptionally strong in terms of structure, discipline, and stability, and it provides a solid foundation for risk pricing and capital management.

Later, pursuing a PhD in Mathematics deepened my understanding of stochastic processes, statistical modeling, and mathematical analysis. It strengthened my ability to think abstractly about uncertainty and complex systems. At the same time, however, I also realized that understanding theory and systematically applying it to real financial problems are two very different challenges.

When I transitioned into banking and credit risk management, I encountered an entirely different type of environment: large-scale datasets, dynamic borrower behavior, macroeconomic uncertainty, and increasingly nonlinear risk patterns. At that point, several things became clear to me:
 

• Actuarial methodologies are powerful but often rely on relatively fixed frameworks.
• Mathematical tools are rigorous, but not always directly actionable in business settings.
• Business problems are increasingly complex yet often lack a unified analytical structure.

Over time, I began to feel that although these capabilities existed individually, they were not fully connected.
 

Why CQF: Not About Learning More, but Connecting Everything Together

My decision to pursue the CQF was actually a very rational one.

It was not simply because of the program’s reputation, but because it addressed exactly the gap I was trying to bridge. The CQF uses probability and stochastic processes as a common language. It integrates derivatives, numerical methods, and machine learning within a single framework, and more importantly, it emphasizes how models are applied in practice rather than focusing solely on theoretical derivations. That distinction mattered a great deal to me.

Before the CQF, I had already learned many individual concepts and techniques, but they often existed separately. The CQF helped connect them into a coherent system.

The Biggest Change: Not What I Learned, But How I Think

If I had to summarize the most valuable part of the CQF experience, it would not simply be the technical skills themselves, but rather the shift in the way I approach problems. Previously, analysis was often centered around historical trends, segmentation, and judgment-based conclusions. Now, I find myself asking different questions:
 

• How is this risk generated?
• What distribution or structure sits behind it?
• How would the outcome change under different environments or assumptions?

In other words, the mindset gradually shifted from being result-oriented to mechanism-oriented. This may sound abstract, but in practice the difference is significant. Many problems that once seemed explainable only through experience can now be described, modeled, and stress-tested in a more systematic way.

During the CQF program, I worked on an independent project that had a meaningful impact on how I think about risk. Using yfinance, I collected U.S. equity market data and built a simple backtesting and VaR (Value-at-Risk) framework to analyze downside risk across different market environments.

Initially, it started as a technical exercise. But over time, I began asking a broader question: Could information embedded in market volatility and tail risk also be useful in credit risk management?

I then experimented with mapping these market risk measures into concepts such as asset dissipation and discount/haircut assumptions. The project itself was relatively straightforward, but the insight behind it was important to me. Different areas of risk management can borrow methodologies from one another. The key is not the individual tool itself, but whether there is a unified framework capable of connecting them.

That, in many ways, is what CQF gave me.

So, is the CQF worth it?

If I were to answer that question directly, I would say this: For professionals already working in finance, risk, or data-related fields, the value of the CQF is not simply that it teaches another technical skill. Its real value lies in helping transform fragmented capabilities into a system that works together coherently.

The CQF will not magically turn someone into a quantitative expert overnight, but it can fundamentally change the way you think about problems, models, and uncertainty. And in my experience, that shift in thinking is far more valuable in the long run than learning any individual tool.

Looking back, CQF was not just another learning experience for me. It became a way to gradually connect actuarial science, mathematics, and credit risk into a more unified quantitative framework.

In today’s environment where uncertainty continues to increase and data continues to expand, the ability to truly “understand the problem” is becoming increasingly important. For me, CQF was not an endpoint, but rather a new starting point, one that continues to shape how I approach risk, decision-making, and complex financial problems going forward.