Roberto, thank you for being here today. Let’s start with a bit of context. Could you explain
your role at Guy Carpenter and how your professional background prepared you for it?
Thank you, Raffaele, for the invitation. It’s truly a pleasure to be here today. At Guy Carpenter, I
serve as Vice President in Risk Management. My work focuses on developing parametric
solutions alongside my team, with the goal of providing innovative and efficient risk
management tools to our clients.
My academic and professional journey has played a significant role in shaping my expertise in
this field. I have a background in civil engineering, which provided me with a strong
foundation for understanding risks at specific locations. During my studies, I explored the
entire chain of risk assessment—starting with hazard identification, followed by fragility
analysis, and finally, evaluating the potential damage to an asset.
This combination of technical knowledge and practical application forms the backbone of my
role at Guy Carpenter. Here, we work on connecting hazard exposure to economic outcomes,
ensuring that our parametric solutions are not only robust but also highly relevant to the risks
our clients face.
That’s fascinating. Parametric solutions seem to be an innovative way to manage risk. Could
you summarize their main advantages and how analytical solutions contribute to their
effectiveness?
Absolutely. Parametric solutions are indeed innovative, particularly because they address a
key challenge in traditional insurance models—what we call “basis risk.” This is the potential
discrepancy between the financial payout provided by the parametric solution and the actual
losses incurred by the insured party.
Let me explain. Parametric solutions are designed to trigger payouts when an event occurs
that meets predefined parameters, such as a specific wind speed, magnitude of an
earthquake, or rainfall level. While this simplifies the claims process and speeds up payouts, it
doesn’t always perfectly align with the actual damage sustained by the insured.
Our job is to minimize this basis risk as much as possible. This is where analytical solutions
play a crucial role. At Guy Carpenter, we collaborate with universities and other institutions to
implement optimization algorithms that refine our models. These algorithms help us adjust
the parameters to ensure that payouts are more closely aligned with the real-world losses
clients experience.
By enhancing the precision of our parametric solutions, we not only reduce financial
discrepancies but also build trust and confidence in these products.
You mentioned optimization algorithms and minimizing basis risk. Could you elaborate on
how these algorithms are applied in practice?
Certainly. The implementation of optimization algorithms is at the heart of our work.
Essentially, these algorithms allow us to analyze historical data, understand hazard
probabilities, and model potential loss scenarios.
For example, let’s consider a portfolio exposed to hurricanes. Using historical weather data
and damage reports, we can simulate various scenarios to identify the parameters that should
trigger payouts. This includes determining the threshold wind speed or rainfall level that
correlates most closely with actual losses.
By refining these parameters, we minimize the gap between the losses incurred and the
compensation provided. This is what we refer to as reducing basis risk. It’s a dynamic process
that requires constant evaluation and adjustment, but the result is a solution that’s both
efficient and reliable.
Collaboration is also key here. Working with universities and research institutions allows us to
stay at the forefront of analytical advancements, ensuring that our models incorporate the
latest methodologies and data sources.
It’s impressive how much technology and data analysis are involved in these solutions. That
said, with so much automation, where do decision-makers fit into the process? What role do
they still play?
That’s an important question, Raffaele, because the human element remains critical despite the advancements in automation. Let me break it down into two key areas where decision-
makers play a central role.
The first is recognizing the need for risk transfer. In risk management, there are several
strategies available—investing in risk reduction, retaining the risk, or transferring it via
insurance. Deciding to transfer risk through a parametric solution is a strategic choice that
requires careful evaluation of the organization’s vulnerabilities and goals.
The second area involves defining the scope of coverage. Decision-makers must determine
which risks or events to insure against and what level of protection is required. For example, they might decide to focus on high-frequency, low-impact events or low-frequency, high-
impact disasters. While analytics provide insights and recommendations, it’s ultimately up to the decision-maker to align these with the organization’s overall strategy.
Automation supports these decisions by providing data-driven insights, but the judgment and
foresight of decision-makers remain irreplaceable.
That’s a great perspective. Could you share an example where parametric solutions made a
tangible difference for a client?
Absolutely. One example that comes to mind involves a client with significant exposure to
hurricanes. In this case, traditional insurance models often resulted in delays due to the time
required for damage assessments and claim processing.
With a parametric solution, we defined clear triggers based on measurable criteria, such as
wind speed and storm location. This allowed payouts to be made almost immediately after a
qualifying event, giving the client access to funds when they needed them most.
This not only minimized financial disruptions but also enabled the client to resume operations
more quickly. It’s a powerful demonstration of how parametric solutions can provide both
financial security and operational resilience.
Looking ahead, what do you see as the biggest challenges and opportunities for parametric
solutions in risk management?
The opportunities are immense, particularly as more industries recognize the value of
parametric solutions. These products can be adapted to a wide range of risks, from natural
disasters to supply chain disruptions, making them highly versatile.
However, there are challenges as well. One major hurdle is education—helping stakeholders
understand how parametric solutions work and why they are beneficial. Many organizations
are still accustomed to traditional insurance models, so there’s a learning curve involved.
Another challenge is data availability. Parametric solutions rely heavily on accurate and timely
data to define triggers and calculate payouts. Ensuring access to reliable data sources will be
critical for the continued success of these solutions.
Despite these challenges, I’m optimistic about the future. With ongoing advancements in
analytics and technology, I believe parametric solutions will become an essential component
of modern risk management.
