July 30, 2025 • 26 mins
Climate-related disasters have caused $18.5 trillion in damages since 2000, and the risks keep growing. In this episode of ESG Currents, Bloomberg Intelligence’s director of ESG research Eric Kane speaks with Ron Dembo, founder and CEO of Riskthinking.AI, about the radical uncertainty of climate change, how his firm models physical climate risk, how investors can use the data and why the biggest signals might be hiding in the tails.
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Speaker 1 (00:09):
ESG is constantly evolving. Over the years, it has shifted
from socially responsible investing to impact to sustainable finance. While
the terminology continues to change, what hasn't changed are the
underlying science, market pressures, and tangible physical and financial impacts
of the climate crisis, increasing regulatory scrutiny and rising consumer expectations.
(00:35):
We aim to filter out the noise by speaking with
industry experts to identify what is really driving value. Welcome
to ESG Currents. I'm Eric Caine, director of ESG Research
for Bloomberg Intelligence. Today we're talking about the ever present
topic of physical risk. Based on our Climate Damages tracker,
(00:58):
which tracks the costs associated with climate events across fifty countries,
we've seen eighteen and a half trillion dollars in climate
damages since the year two thousand. In the US, climate
costs over the last year alone have approached one trillion.
So the question many investors, corporates and policymakers are asking
(01:21):
is how can we assess or prepare for these climate
impacts going forward? And to help answer that question, I'm
joined by Ron Dembo, who is the CEO and founder
of Risk Thinking dot AI and the author of Risk
Thinking in an Uncertain World. Risk dot AI seeks to
(01:41):
model the impact of climate change on the future of
financial institutions, corporations, and even countries. And just a quick
disclaimer for this episode, Bloomberg LP, the parent of Bloomberg Intelligence,
is an investor in one or more of the companies
identified in this podcast. Any views in this podcast are
(02:02):
those of the author and Bloomberg Intelligence and don't necessarily
reflect those of Bloomberg LP. With that, Ron, welcome to
the program and thank you so much for taking the
time to join us.
Speaker 2 (02:14):
Well, thank you for having me Eric, It's a pleasure
to be here.
Speaker 1 (02:18):
Great, So we can jump right in with the first question. So,
as I mentioned in the beginning, physical risk and adaptation
have really become key areas of focus for the estree
market and investors more broadly, can you tell us a
little bit about Riskinking AI, where you fit in the
conversation and ultimately what you're trying to solve for?
Speaker 2 (02:39):
Thank you, Eric. We've seen that the discussion around ESG
has changed considerably recently. So for years, the big E
and ESG mainly focused on emissions and transcision risk. Physical
risks such as floods, fires, storms impacting tenable assets was
(03:01):
perceived as much too complex and too distant to be priced.
This is a key gap that riskinking was designed to address,
and the main issue we're addressing is that climate change
has introduced significant uncertainty into our economic system. So the
past is no longer dependable as a guide to the future,
which means that transition risk model traditional risk models are
(03:25):
increasingly effective ineffective. This results in trillions of dollars worth
of physical assets, as you mentioned, factories, power gds, buildings
and infrastructure being dangerously mispriced. Our role is to act
as the architects of this new paradigm. We are more
than just a climate provider. We are a financial risk
(03:48):
analytics firm that has developed a climate digital twin that
is a future model of the entire planet under climate
uncertainty right to twenty one hundred. We translate all of
this complexity of climate science, mathematics, and risk management into
a language the market understands financial risk. We provide tools
(04:12):
to accurately assess physical risk, enabling companies, investors, and regulators
to transition from a vague climate awareness to making informed,
data driven decisions on adaptation. Investment and financial resilience. I
hope that sort of summarizes it for you.
Speaker 1 (04:31):
Yeah, No, that's a great summary. And I think you
know the idea of ultimately translating everything into the language
of financial risk as one of the key components. Certainly
as the ESG conversation kind of continues to evolve and
really hone in on the idea of, you know, financial
materiality and financial risk. So we know, of course that,
(04:53):
as you said, the future is uncertain. I'm curious to
hear a little bit more about how you ultimately help
your clients understand the future when the only data that
we actually have certainty in is of course backward looking.
Speaker 2 (05:10):
I think that's the key question. Eric. You start with history,
but you can't rely on it alone. Relying only on
past data is kind of like driving a wind along
a winding mountain road by just looking solely into the
rear view mirror. So our method is twofold. First, we
(05:30):
base our models on the most reliable forward looking science available.
This changes from time to time, and we update them monthly.
We include as many projections as we possibly can that
are science based, the same ones used by the IPCC
and covering various scenarios and timelines up to twenty one hundred.
(05:52):
So this gives us a comprehensive view of the potential futures,
not just a simple extension of past trends. Second, and
this is vital, we don't just pick one of these features. Instead,
we embrace the uncertainty by developing what we call a
sarchastic view, that is, a view that captures full uncertainty.
(06:13):
Our Climate Digital Twin includes over one hundred and forty
billion forward looking projections which we digest and store worldwide
for over more than fifty hazards. So we verified this
probability data by comparing it to real world extreme events.
And that's where we use some history. So we take
(06:35):
black swan events, events that have never been observed before
in a specific location. For example, our twenty twenty two
distributions accurately captured ninety six percent of the record breaking
extreme heat events worldwide a year later. That is, all
the blacks ones that occurred actually appeared in our distributions.
(06:59):
In short, we rely on the past to calibrate and
validate them models, but we use the full range of
future focused climate science to guide us forward.
Speaker 1 (07:08):
Really interesting, and you mentioned you know, the black Swan
events being captured in the distribution. So I think that's
a good segue into the next question, which is you
know you mentioned also that you use all the science
based models that are out there, right, we know these
models are extremely complex and uncertain, and what you've ultimately done,
(07:30):
as you suggest it is kind of created a distribution
model of potential risks. So I guess the question is
where do you see the most value in these distributions.
Is it in the areas of consensus or perhaps the
tails of the distribution that maybe better capture those black
Swan events that you alluded to.
Speaker 2 (07:52):
So the value lies in the entire distribution, but most urgent,
actionable signals come from the tails consensus view, that is
where most of the most people really are believe the
world is going, or the mean of the distribution we
call the expected impact. It's important, but it shows our
(08:14):
consensus view. So in the stable world that's pretty good. However,
complex systems, whether it's the climate or any financial system,
don't fall apart because of averages. They failed during extreme events.
The tails of the distribution represent these events. They're serious
(08:34):
and scientifically plausible scenarios that cause devastating losses or potentially
could call devastating losses are the triggers. So as a
recent research shows, you know, a moderate climate chuck might
trigger a recession, but a tail risk event can permanently
(08:56):
break an economy, leading to a lusting down to and
we've seen this happen in many you know, butterfly flappings, swings,
and you know, the whole of two thousand and eight happens.
So that's why analytics focus so heavily on metrics like
tail risk or conditional value at risk as it's called
in the jargon, which messes the average loss in the
(09:19):
worst five percent of outcomes or one in twenty outcomes.
It quantifies the severity of these events that truly threaten
solvency and stability. The consensus tells you the direction of travel,
the tails tell you what you need to do to
survive in the journey.
Speaker 1 (09:38):
Interesting, so if the signal is really in the tail,
how ultimately can different entities use the data. Maybe we
can start with corporates and then talk about investors and
ultimately how the two can use this information.
Speaker 2 (09:56):
So this is where the data becomes truly powerful, and
data driven solutions are pretty interesting. For corporations, tail risk
data transforms adaptation from some vague nice to have a
the idea into a strategic, finance driven necessity. Instead of
merely knowing they face flood risk, a company can pinpoint
(10:19):
that a specific critical factory has a tail risk representing
a large portion of its asset value, requiring urgent action.
It is in prioritizing adaptation funds where they'll have the
greatest impact. So for investors, tail risk signals hidden vulnerabilities.
A company might seem financially sound on its balance sheet,
(10:41):
but that balance sheet doesn't price in critical physical risk today,
and our data show that its key assets are highly
vulnerable to physical risks they haven't been priced in. So
investors can use this data to assess resilience in their portfolios,
engaged with corporate management on the need for adaptation strategies
(11:02):
or conduct due diligence for m and a private credit
ensuring the underlying collateral isn't dangerously mispriced. Think of it.
Private credit is the fastest growing part of finance, and
yet the actual collateral for that credit is not being priced.
That's pretty systemic. Adaptation acts as a bridge between these
(11:25):
two ideas. For corporations, it's the measure that they take
to mitigate detail risk for investors, A company's credible adaptation
plan demonstrates active risk management, making it more resilient and
more appealing for investors.
Speaker 1 (11:43):
So maybe let's drill into a specific example. We've here
at bi on the HG team recently written about HCA, which,
for those who don't know, is a healthcare facilities company
that has a majority of its hospital bitals in places
like Texas and Florida that have higher hurricane frequencies. Last year,
(12:07):
the company reported about two hundred and fifty million dollars
in losses in the fourth quarter and negative impacts to
volume up between twenty and forty basis points due to
Hurricanes Milton and Helene. So we know the company has
been exposed historically and will likely continue to face risk
associated with climate change. So in advance of our conversation here,
(12:30):
I asked your tool Risk Thinking dot AI the simple
question what is hda's exposure to physical risk? And your
service returned quite a bit of data. For example, it
suggested that for the year twenty thirty and under a
stochastic view scenario, the company has a total value at
risk or VAR of seventeen percent of tangible capital asset value,
(12:56):
suggesting medium risk level. The model also suggests a downside
likelihood of eighty five percent and an expected impact of
eight percent of tangible capital asset value. So it's a
lot of information, and there's actually quite a bit more
on the website, which is impressive. But maybe let's start
with the top with value at risk or VAR as
(13:19):
we call it, and maybe you can walk us through
kind of what informs each of those data points that
I mentioned.
Speaker 2 (13:25):
This is a great practical example that you found, Eric,
I mean, it's really right on target. So let's just
break down those metrics that we actually discuss. First, downside
likelihood of eighty five percent is exactly the same metric
as the probability of rain next Tuesday is eighty five percent,
(13:45):
So it's the likelihood of a bad event, not the impact.
This is the most intuitive starting point. It indicates the
probability that climate conditions impacting HCAs assets in twenty thirty
between now and twenty thirty will be worse than historical average.
An eighty five percent chance strongly suggests that climate risks
(14:07):
are heavily tilted against them. The operating environment is fundamentally
shifting towards greater risk. Second, the expected impact of eight
percent of tangible capital value lost. This is a probability
weighted average of all possible outcomes from minor damage to
severe loss, and you can think of it as the
(14:29):
annualized cost of climate change. It informs HCA that on average,
they can expect future replacement or repair costs due to
all physical hazards to be approximately eight percent of the
asset value, So you can think of a balance sheet
hit they can expect. It's a crucial number for long
(14:50):
term financial planning and budgeting. Then, the third total value
at risk is seventeen percent. This is a tail risk metric.
The seventy percent it's called at a ninety five percent
confidence level, indicates a five percent or one in twenty
chance between now and twenty thirty, the total damage from
(15:11):
all possible climate events two ahta's capital assets will exceed
seventeen percent of their value the value of that asset.
This doesn't represent an average loss. It's the threshold that's
especially interesting for severe years. It's a figure that a CFO,
(15:34):
for example, a risk officer must consider when stress testing
their capital adequacy or their insurance.
Speaker 1 (15:41):
Interesting and as I mentioned, you know, when I queried
your service, there's a lot more data that comes up,
and in fact too much for us to cover on
this program. But one of the other things that the
model does is, you know, provide looks at var an
expected impact for specific perils including cyclone, sea level risk,
(16:07):
coastal floods, fires, et cetera. Curious if you could walk
us through how your model ultimately kind of aggregates these
individual perils up into an overall value at risk.
Speaker 2 (16:20):
This is a critical question and actually sets us apart
from many of the available solutions out there. We don't
look at one peril at a time. We look at
combinations of perils, multi factor stresses, you could think of
it at and we don't just simply add them up.
(16:42):
A simple sum would be a fundamental error. You can't
add two distributions for example. He ignores the complexity of
the non linear interactions between hazards. Patented method for multi
factor scenario generation provides a solution, and yes, how it works.
Only given asset we generate a full probability distribution of
(17:03):
that hazard the cyclone, flood and heat and so on.
At that time in that location. Then the algorithm combines
these individual distributions to generate literally thousands or even millions
of new, comprehensive multi factor scenarios. It's generated algorithmically because
humans can't do this, it's just too complex. It explores
(17:27):
every possible permutation, including unintuitive but high impact combinations like
severe drought followed by extreme rainfall in the same climatology
tour period. It makes a big difference if there's been
a drought before we have extreme rainfall. So for each
of these integrated scenarios, we calculate a single financial impact.
(17:49):
So it's a combination of scenarios hitting that asset and
its impact islimeasured, and as a result, we get a
distribution of impacts because we're doing many of these, and
the overall value at risk is then calculated from the
tail of that distribution. So this is a very complex distribution,
it's a multi factor distribution, and it's generated totally algorithmically.
(18:16):
So the overall value risk is not a sum of
these individual perils, it's the value risk of the sum
of the impacts across all of the integrated scenarios. So
just a very complex reality of how these hazards interact.
It's probably the main differentiator we have. I don't know
(18:38):
others that do this.
Speaker 1 (18:39):
So another thing that you ultimately provide is that RTAI
was thinking AI vulnerability ranking where we can ultimately see
the percentage of assets that may be stranded, stressed, or
at risk in twenty thirty for example, you can also
see that relative to the S and P or other benchmark.
(19:01):
And what we see for the company that we were
just discussing HDA is that the company is in the
lower third relative to the S and P or those
that are kind of least exposed. Curious as to how
you calculate the level of asset risks and whether you're
able to account for factors that we would associate with
(19:22):
those assets, like economic activity associated with facilities, or in
the case of a hospital, maybe the number of hospital
beds there, how well easily they could be moved or replaced.
Maybe could walk us through that. So thanks for raising this.
We recently published a worldwide vulnerability ranking that covers thirteen
(19:44):
thousand major companies and eighty thousand of the subsidiaries, and
it's available free on the net. You can just go
and type in a company name and get its vulnerability ranking.
So the ranking is based on our tail risk asset
damage function. The expected financial loss is a percentage of
(20:05):
the tangible capital asset costs in the worse five percent
of scenarios, so one in twenty chance you could see
this happen. We then set thresholds to classify assets. For example,
we look at the different assets of the company and
organize them by their tail risk. How big that a
loss they could assume. Some of these could lose as
(20:29):
much as all of their value over the next five years.
In a sense, they're stranded. You know, they're worth less
than the impacts on them. Others might be close to that,
and we could classify them as stressed assets. They're not
ensurable because or the insurance would be outrageously priced because
(20:50):
they have very high risk physical risk. And then the
ones with less and less tail risk, so we actually
organize them who have the biggest tail risk. That helps
us say, you know who should we go after first?
The stranded assets are absolutely they need to be the
(21:11):
dealt with immediately, so we list these are the assets
go after them. You need to either move them into
new assets fix their physical nature now.
Speaker 2 (21:25):
But you need to do that now the stressed assets
I think you need to do pretty soon after that,
and assets at are risk could become stress so you
might want to worry about them, but you've got a
bit of time. So we do that by calculating these
scores and actually looking at the tail risk and breaking
(21:46):
it down into these categories. And we've done that for
every company listed in the forty large economies.
Speaker 1 (21:53):
Impressive, So maybe taking a step back to more of
a macro view to end the com You mentioned at
the beginning that the ESG conversation, the climate conversation has
really shifted in recent years from a focus on transition
risk to physical risk and ultimately adaptation. Curious to hear
(22:18):
kind of your take on how the climate conversation is
evolving kind of beyond that and ultimately what's needed to
get investors, corporates and policy makers to really start incorporating
the type of analytics that you're providing.
Speaker 2 (22:37):
This follows the general theme of this discussion. You know,
we believe in data driven risk and the reason for
that is you can't really pick a distribution, some mathematical
distribution and then assume that you can measure risk with that.
You have to really do this based on what the
data is because it varies so much from company to company.
(23:00):
Conversations is advancing very quickly. We're moving beyond debating the
why of climate change and are now focused on figuring
out the how, how to manage risk, how to finance adaptation,
how to build resilience. It's shifting from an environmental or
corporate social responsibility issue to a matter of financial stability
(23:21):
and macroeconomic importance, as shown by our work with central
banks and regulators like us FREE, the Canadian major regulator,
and a partner of House. But to accelerate action, three
things are needed. Quantification, we must continue to transition from
narrative to numerical data. A business case is required for action.
(23:43):
By pricing physical risk and enabling a clear cost benefit
analysis of adaptation, we're providing that business case. The second
point is integration. Climate risk cannot be a niche topic
handled solely by the sustainability department. It must be incorporated
into the core processes of the financial system, such as
(24:05):
an enterprise risk management m and a due diligencence capital allocation.
Our collaboration with Bloomberg to embed our physical risk data
directly into the terminal and other platforms is a crucial
step in achieving this, and thirdly leadership. Ultimately, the tools
are now available. The final piece is leadership. We need
(24:27):
leaders across business and government willing to abandon outdated models,
embrace this new, highly uncertain reality, and make the strategic
forward looking decisions to cry you know, based on data,
and that way is the way in which we'll get
to a resilient future.
Speaker 1 (24:47):
Very interesting, So I really like that as kind of
a three point answer there. So again the idea of
shifting from narrative information to numerical data, into grading this
type of information into core processes. Then of course leadership
and the need for folks across businesses and governments who
(25:10):
actually kind of as you suggested, abandon the old models
and start to use this information and planning for the future.
So I think that's a really great way to end Ron.
Thank you so much for taking the time to join
us today.
Speaker 2 (25:23):
Well, thanks, Eric, I mean this is a real pleasure.
I appreciate the opportunity.
Speaker 1 (25:28):
Wonderful. So for our listeners out there, you can of
course get more information on all things climate, including our
climate damage is tracker by going to our dashboard on
the Bloomberg terminal, which is BI Space ESG GO, and
if you have questions or topics you'd like us to
cover in future episodes, please reach out to us at
(25:50):
esg currents at Bloomberg dot net. Thanks for listening, and
we'll see you next time.
ESG is constantly evolving. Over the years, it has shifted
from socially responsible investing to impact to sustainable finance. While
the terminology continues to change, what hasn't changed are the
underlying science, market pressures, and tangible physical and financial impacts
of the climate crisis, increasing regulatory scrutiny and rising consumer expectations.
(00:35):
We aim to filter out the noise by speaking with
industry experts to identify what is really driving value. Welcome
to ESG Currents. I'm Eric Caine, director of ESG Research
for Bloomberg Intelligence. Today we're talking about the ever present
topic of physical risk. Based on our Climate Damages tracker,
(00:58):
which tracks the costs associated with climate events across fifty countries,
we've seen eighteen and a half trillion dollars in climate
damages since the year two thousand. In the US, climate
costs over the last year alone have approached one trillion.
So the question many investors, corporates and policymakers are asking
(01:21):
is how can we assess or prepare for these climate
impacts going forward? And to help answer that question, I'm
joined by Ron Dembo, who is the CEO and founder
of Risk Thinking dot AI and the author of Risk
Thinking in an Uncertain World. Risk dot AI seeks to
(01:41):
model the impact of climate change on the future of
financial institutions, corporations, and even countries. And just a quick
disclaimer for this episode, Bloomberg LP, the parent of Bloomberg Intelligence,
is an investor in one or more of the companies
identified in this podcast. Any views in this podcast are
(02:02):
those of the author and Bloomberg Intelligence and don't necessarily
reflect those of Bloomberg LP. With that, Ron, welcome to
the program and thank you so much for taking the
time to join us.
Speaker 2 (02:14):
Well, thank you for having me Eric, It's a pleasure
to be here.
Speaker 1 (02:18):
Great, So we can jump right in with the first question. So,
as I mentioned in the beginning, physical risk and adaptation
have really become key areas of focus for the estree
market and investors more broadly, can you tell us a
little bit about Riskinking AI, where you fit in the
conversation and ultimately what you're trying to solve for?
Speaker 2 (02:39):
Thank you, Eric. We've seen that the discussion around ESG
has changed considerably recently. So for years, the big E
and ESG mainly focused on emissions and transcision risk. Physical
risks such as floods, fires, storms impacting tenable assets was
(03:01):
perceived as much too complex and too distant to be priced.
This is a key gap that riskinking was designed to address,
and the main issue we're addressing is that climate change
has introduced significant uncertainty into our economic system. So the
past is no longer dependable as a guide to the future,
which means that transition risk model traditional risk models are
(03:25):
increasingly effective ineffective. This results in trillions of dollars worth
of physical assets, as you mentioned, factories, power gds, buildings
and infrastructure being dangerously mispriced. Our role is to act
as the architects of this new paradigm. We are more
than just a climate provider. We are a financial risk
(03:48):
analytics firm that has developed a climate digital twin that
is a future model of the entire planet under climate
uncertainty right to twenty one hundred. We translate all of
this complexity of climate science, mathematics, and risk management into
a language the market understands financial risk. We provide tools
(04:12):
to accurately assess physical risk, enabling companies, investors, and regulators
to transition from a vague climate awareness to making informed,
data driven decisions on adaptation. Investment and financial resilience. I
hope that sort of summarizes it for you.
Speaker 1 (04:31):
Yeah, No, that's a great summary. And I think you
know the idea of ultimately translating everything into the language
of financial risk as one of the key components. Certainly
as the ESG conversation kind of continues to evolve and
really hone in on the idea of, you know, financial
materiality and financial risk. So we know, of course that,
(04:53):
as you said, the future is uncertain. I'm curious to
hear a little bit more about how you ultimately help
your clients understand the future when the only data that
we actually have certainty in is of course backward looking.
Speaker 2 (05:10):
I think that's the key question. Eric. You start with history,
but you can't rely on it alone. Relying only on
past data is kind of like driving a wind along
a winding mountain road by just looking solely into the
rear view mirror. So our method is twofold. First, we
(05:30):
base our models on the most reliable forward looking science available.
This changes from time to time, and we update them monthly.
We include as many projections as we possibly can that
are science based, the same ones used by the IPCC
and covering various scenarios and timelines up to twenty one hundred.
(05:52):
So this gives us a comprehensive view of the potential futures,
not just a simple extension of past trends. Second, and
this is vital, we don't just pick one of these features. Instead,
we embrace the uncertainty by developing what we call a
sarchastic view, that is, a view that captures full uncertainty.
(06:13):
Our Climate Digital Twin includes over one hundred and forty
billion forward looking projections which we digest and store worldwide
for over more than fifty hazards. So we verified this
probability data by comparing it to real world extreme events.
And that's where we use some history. So we take
(06:35):
black swan events, events that have never been observed before
in a specific location. For example, our twenty twenty two
distributions accurately captured ninety six percent of the record breaking
extreme heat events worldwide a year later. That is, all
the blacks ones that occurred actually appeared in our distributions.
(06:59):
In short, we rely on the past to calibrate and
validate them models, but we use the full range of
future focused climate science to guide us forward.
Speaker 1 (07:08):
Really interesting, and you mentioned you know, the black Swan
events being captured in the distribution. So I think that's
a good segue into the next question, which is you
know you mentioned also that you use all the science
based models that are out there, right, we know these
models are extremely complex and uncertain, and what you've ultimately done,
(07:30):
as you suggest it is kind of created a distribution
model of potential risks. So I guess the question is
where do you see the most value in these distributions.
Is it in the areas of consensus or perhaps the
tails of the distribution that maybe better capture those black
Swan events that you alluded to.
Speaker 2 (07:52):
So the value lies in the entire distribution, but most urgent,
actionable signals come from the tails consensus view, that is
where most of the most people really are believe the
world is going, or the mean of the distribution we
call the expected impact. It's important, but it shows our
(08:14):
consensus view. So in the stable world that's pretty good. However,
complex systems, whether it's the climate or any financial system,
don't fall apart because of averages. They failed during extreme events.
The tails of the distribution represent these events. They're serious
(08:34):
and scientifically plausible scenarios that cause devastating losses or potentially
could call devastating losses are the triggers. So as a
recent research shows, you know, a moderate climate chuck might
trigger a recession, but a tail risk event can permanently
(08:56):
break an economy, leading to a lusting down to and
we've seen this happen in many you know, butterfly flappings, swings,
and you know, the whole of two thousand and eight happens.
So that's why analytics focus so heavily on metrics like
tail risk or conditional value at risk as it's called
in the jargon, which messes the average loss in the
(09:19):
worst five percent of outcomes or one in twenty outcomes.
It quantifies the severity of these events that truly threaten
solvency and stability. The consensus tells you the direction of travel,
the tails tell you what you need to do to
survive in the journey.
Speaker 1 (09:38):
Interesting, so if the signal is really in the tail,
how ultimately can different entities use the data. Maybe we
can start with corporates and then talk about investors and
ultimately how the two can use this information.
Speaker 2 (09:56):
So this is where the data becomes truly powerful, and
data driven solutions are pretty interesting. For corporations, tail risk
data transforms adaptation from some vague nice to have a
the idea into a strategic, finance driven necessity. Instead of
merely knowing they face flood risk, a company can pinpoint
(10:19):
that a specific critical factory has a tail risk representing
a large portion of its asset value, requiring urgent action.
It is in prioritizing adaptation funds where they'll have the
greatest impact. So for investors, tail risk signals hidden vulnerabilities.
A company might seem financially sound on its balance sheet,
(10:41):
but that balance sheet doesn't price in critical physical risk today,
and our data show that its key assets are highly
vulnerable to physical risks they haven't been priced in. So
investors can use this data to assess resilience in their portfolios,
engaged with corporate management on the need for adaptation strategies
(11:02):
or conduct due diligence for m and a private credit
ensuring the underlying collateral isn't dangerously mispriced. Think of it.
Private credit is the fastest growing part of finance, and
yet the actual collateral for that credit is not being priced.
That's pretty systemic. Adaptation acts as a bridge between these
(11:25):
two ideas. For corporations, it's the measure that they take
to mitigate detail risk for investors, A company's credible adaptation
plan demonstrates active risk management, making it more resilient and
more appealing for investors.
Speaker 1 (11:43):
So maybe let's drill into a specific example. We've here
at bi on the HG team recently written about HCA, which,
for those who don't know, is a healthcare facilities company
that has a majority of its hospital bitals in places
like Texas and Florida that have higher hurricane frequencies. Last year,
(12:07):
the company reported about two hundred and fifty million dollars
in losses in the fourth quarter and negative impacts to
volume up between twenty and forty basis points due to
Hurricanes Milton and Helene. So we know the company has
been exposed historically and will likely continue to face risk
associated with climate change. So in advance of our conversation here,
(12:30):
I asked your tool Risk Thinking dot AI the simple
question what is hda's exposure to physical risk? And your
service returned quite a bit of data. For example, it
suggested that for the year twenty thirty and under a
stochastic view scenario, the company has a total value at
risk or VAR of seventeen percent of tangible capital asset value,
(12:56):
suggesting medium risk level. The model also suggests a downside
likelihood of eighty five percent and an expected impact of
eight percent of tangible capital asset value. So it's a
lot of information, and there's actually quite a bit more
on the website, which is impressive. But maybe let's start
with the top with value at risk or VAR as
(13:19):
we call it, and maybe you can walk us through
kind of what informs each of those data points that
I mentioned.
Speaker 2 (13:25):
This is a great practical example that you found, Eric,
I mean, it's really right on target. So let's just
break down those metrics that we actually discuss. First, downside
likelihood of eighty five percent is exactly the same metric
as the probability of rain next Tuesday is eighty five percent,
(13:45):
So it's the likelihood of a bad event, not the impact.
This is the most intuitive starting point. It indicates the
probability that climate conditions impacting HCAs assets in twenty thirty
between now and twenty thirty will be worse than historical average.
An eighty five percent chance strongly suggests that climate risks
(14:07):
are heavily tilted against them. The operating environment is fundamentally
shifting towards greater risk. Second, the expected impact of eight
percent of tangible capital value lost. This is a probability
weighted average of all possible outcomes from minor damage to
severe loss, and you can think of it as the
(14:29):
annualized cost of climate change. It informs HCA that on average,
they can expect future replacement or repair costs due to
all physical hazards to be approximately eight percent of the
asset value, So you can think of a balance sheet
hit they can expect. It's a crucial number for long
(14:50):
term financial planning and budgeting. Then, the third total value
at risk is seventeen percent. This is a tail risk metric.
The seventy percent it's called at a ninety five percent
confidence level, indicates a five percent or one in twenty
chance between now and twenty thirty, the total damage from
(15:11):
all possible climate events two ahta's capital assets will exceed
seventeen percent of their value the value of that asset.
This doesn't represent an average loss. It's the threshold that's
especially interesting for severe years. It's a figure that a CFO,
(15:34):
for example, a risk officer must consider when stress testing
their capital adequacy or their insurance.
Speaker 1 (15:41):
Interesting and as I mentioned, you know, when I queried
your service, there's a lot more data that comes up,
and in fact too much for us to cover on
this program. But one of the other things that the
model does is, you know, provide looks at var an
expected impact for specific perils including cyclone, sea level risk,
(16:07):
coastal floods, fires, et cetera. Curious if you could walk
us through how your model ultimately kind of aggregates these
individual perils up into an overall value at risk.
Speaker 2 (16:20):
This is a critical question and actually sets us apart
from many of the available solutions out there. We don't
look at one peril at a time. We look at
combinations of perils, multi factor stresses, you could think of
it at and we don't just simply add them up.
(16:42):
A simple sum would be a fundamental error. You can't
add two distributions for example. He ignores the complexity of
the non linear interactions between hazards. Patented method for multi
factor scenario generation provides a solution, and yes, how it works.
Only given asset we generate a full probability distribution of
(17:03):
that hazard the cyclone, flood and heat and so on.
At that time in that location. Then the algorithm combines
these individual distributions to generate literally thousands or even millions
of new, comprehensive multi factor scenarios. It's generated algorithmically because
humans can't do this, it's just too complex. It explores
(17:27):
every possible permutation, including unintuitive but high impact combinations like
severe drought followed by extreme rainfall in the same climatology
tour period. It makes a big difference if there's been
a drought before we have extreme rainfall. So for each
of these integrated scenarios, we calculate a single financial impact.
(17:49):
So it's a combination of scenarios hitting that asset and
its impact islimeasured, and as a result, we get a
distribution of impacts because we're doing many of these, and
the overall value at risk is then calculated from the
tail of that distribution. So this is a very complex distribution,
it's a multi factor distribution, and it's generated totally algorithmically.
(18:16):
So the overall value risk is not a sum of
these individual perils, it's the value risk of the sum
of the impacts across all of the integrated scenarios. So
just a very complex reality of how these hazards interact.
It's probably the main differentiator we have. I don't know
(18:38):
others that do this.
Speaker 1 (18:39):
So another thing that you ultimately provide is that RTAI
was thinking AI vulnerability ranking where we can ultimately see
the percentage of assets that may be stranded, stressed, or
at risk in twenty thirty for example, you can also
see that relative to the S and P or other benchmark.
(19:01):
And what we see for the company that we were
just discussing HDA is that the company is in the
lower third relative to the S and P or those
that are kind of least exposed. Curious as to how
you calculate the level of asset risks and whether you're
able to account for factors that we would associate with
(19:22):
those assets, like economic activity associated with facilities, or in
the case of a hospital, maybe the number of hospital
beds there, how well easily they could be moved or replaced.
Maybe could walk us through that. So thanks for raising this.
We recently published a worldwide vulnerability ranking that covers thirteen
(19:44):
thousand major companies and eighty thousand of the subsidiaries, and
it's available free on the net. You can just go
and type in a company name and get its vulnerability ranking.
So the ranking is based on our tail risk asset
damage function. The expected financial loss is a percentage of
(20:05):
the tangible capital asset costs in the worse five percent
of scenarios, so one in twenty chance you could see
this happen. We then set thresholds to classify assets. For example,
we look at the different assets of the company and
organize them by their tail risk. How big that a
loss they could assume. Some of these could lose as
(20:29):
much as all of their value over the next five years.
In a sense, they're stranded. You know, they're worth less
than the impacts on them. Others might be close to that,
and we could classify them as stressed assets. They're not
ensurable because or the insurance would be outrageously priced because
(20:50):
they have very high risk physical risk. And then the
ones with less and less tail risk, so we actually
organize them who have the biggest tail risk. That helps
us say, you know who should we go after first?
The stranded assets are absolutely they need to be the
(21:11):
dealt with immediately, so we list these are the assets
go after them. You need to either move them into
new assets fix their physical nature now.
Speaker 2 (21:25):
But you need to do that now the stressed assets
I think you need to do pretty soon after that,
and assets at are risk could become stress so you
might want to worry about them, but you've got a
bit of time. So we do that by calculating these
scores and actually looking at the tail risk and breaking
(21:46):
it down into these categories. And we've done that for
every company listed in the forty large economies.
Speaker 1 (21:53):
Impressive, So maybe taking a step back to more of
a macro view to end the com You mentioned at
the beginning that the ESG conversation, the climate conversation has
really shifted in recent years from a focus on transition
risk to physical risk and ultimately adaptation. Curious to hear
(22:18):
kind of your take on how the climate conversation is
evolving kind of beyond that and ultimately what's needed to
get investors, corporates and policy makers to really start incorporating
the type of analytics that you're providing.
Speaker 2 (22:37):
This follows the general theme of this discussion. You know,
we believe in data driven risk and the reason for
that is you can't really pick a distribution, some mathematical
distribution and then assume that you can measure risk with that.
You have to really do this based on what the
data is because it varies so much from company to company.
(23:00):
Conversations is advancing very quickly. We're moving beyond debating the
why of climate change and are now focused on figuring
out the how, how to manage risk, how to finance adaptation,
how to build resilience. It's shifting from an environmental or
corporate social responsibility issue to a matter of financial stability
(23:21):
and macroeconomic importance, as shown by our work with central
banks and regulators like us FREE, the Canadian major regulator,
and a partner of House. But to accelerate action, three
things are needed. Quantification, we must continue to transition from
narrative to numerical data. A business case is required for action.
(23:43):
By pricing physical risk and enabling a clear cost benefit
analysis of adaptation, we're providing that business case. The second
point is integration. Climate risk cannot be a niche topic
handled solely by the sustainability department. It must be incorporated
into the core processes of the financial system, such as
(24:05):
an enterprise risk management m and a due diligencence capital allocation.
Our collaboration with Bloomberg to embed our physical risk data
directly into the terminal and other platforms is a crucial
step in achieving this, and thirdly leadership. Ultimately, the tools
are now available. The final piece is leadership. We need
(24:27):
leaders across business and government willing to abandon outdated models,
embrace this new, highly uncertain reality, and make the strategic
forward looking decisions to cry you know, based on data,
and that way is the way in which we'll get
to a resilient future.
Speaker 1 (24:47):
Very interesting, So I really like that as kind of
a three point answer there. So again the idea of
shifting from narrative information to numerical data, into grading this
type of information into core processes. Then of course leadership
and the need for folks across businesses and governments who
(25:10):
actually kind of as you suggested, abandon the old models
and start to use this information and planning for the future.
So I think that's a really great way to end Ron.
Thank you so much for taking the time to join
us today.
Speaker 2 (25:23):
Well, thanks, Eric, I mean this is a real pleasure.
I appreciate the opportunity.
Speaker 1 (25:28):
Wonderful. So for our listeners out there, you can of
course get more information on all things climate, including our
climate damage is tracker by going to our dashboard on
the Bloomberg terminal, which is BI Space ESG GO, and
if you have questions or topics you'd like us to
cover in future episodes, please reach out to us at
(25:50):
esg currents at Bloomberg dot net. Thanks for listening, and
we'll see you next time.
Host
Eric Kane
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