8 minute read
The year of carbon and emergence of forward-looking net-zero analytics - by Peter Plochan
the year of carbon and emergence of forward-looking net-zero analytics
by Peter Plochan
Advertisement
Throughout 2021, topped off by the COP26 summit (the United Nations conference on Climate Change), attention to climate change and climate risk gained significant momentum and managed to climb up the agendas of leading global policymakers, regulators, corporates and financial institutions. For the financial industry in particular, there were a number of specific challenges (see below) introduced by these recent developments that require attention from risk management and finance professionals.
Industry challenges
Extensinve pressure from governments, regulators, markets & customers
New risks & opportunities from transitioning to Net Zero Economy & portfolios
Increasing financial losses from the changing climate “This is the Greatest Big Data project for our bank” G-SIB’s head of Climate
carbon accounting and carbon weighted assets
In parallel to the climate risk evolution, the bankers, insurers and asset managers alike are paying increasing attention to carbon intensity of their operations, mainly focusing on their financing and investment activities. Following the COP26 summit, the Glasgow Financial Alliance for Net Zero (GFANZ)1, representing over 450 financial firms across 45 countries with total assets of over US$ 130 trillion, committed themselves to fully decarbonize their loan and investment portfolios, and reach netzero portfolio carbon emissions by 2050 with significant decrease achieved already by 2030.
In some jurisdictions, banks are already being required to report these so-called “financed emissions”, or Scope 3 emissions, starting in 2022. Using the term “Carbon-Weighted Assets” might be more appropriate because that is what the leading portfolio carbon measurement methodologies, such as the PCAF2 Carbon Accounting standard, are all about.
1 / https://www.gfanzero.com/about/
2 / Partnership for Carbon Accounting Financials (https://carbonaccountingfinancials.com/files/downloads/PCAF-Global-GHG-Standard.pdf)
Similar to Basel Risk-Weighted Assets, the counterparty’s carbon footprint (the equivalent of risk rating under Basel) is used for weighting the exposures in order to come to a single measure that indicates how carbon intensive (instead of how risky) the exposure is and can be aggregated up to the counterparty, segment and portfolio levels.
All in all, carbon footprint data on both customer and institution levels are gaining significant attention these days. The problem is that this information is not always easy to find.
climate and net-zero (big) data analytics
For example, a CRO of a G-SIB stated in a recent SAS webinar that they were able to get carbon footprint data for only 17% of their corporate customers; for the rest they had to use proxies. And even if the information is found, there are often challenges with comparability and auditability of that information. When institutions calculate their portfolio financed carbon footprint using the PCAF framework they have to assign each exposure-level carbon calculation into one of the 5 Data Quality buckets with scores based on the availability and reliability of the respective carbon data used in the calculation. As part of the framework, they are obliged to report their average data quality score per asset class.
Lastly, this is just the static carbon footprint as of now, but to plan their portfolio decarbonization path all the way to 2050, financial institutions need to form a view on how the carbon footprint of their customers is going to evolve and how it will be impacted by different climate scenarios and actions the institutions and policymakers might take along the way. The Transition Pathway Initiative3 provides a first glimpse of how a central repository of public carbon commitments of large corporates would look, including both their current and future transitional carbon footprint (where available). The data collected is publicly available and thus accessible for financial firms. On the other hand, we see industry coming together in order to share and compare data; for example, the Global Credit Data4 consortium represents 55 global banks that share credit data among themselves and has recently established two dedicated working groups in order to determine which climate data can be pooled and shared among the banks.
According to a study performed by Standard Chartered Bank, for 52% of top corporates, net-zero transition will be the most expensive project they have ever undertaken.5 No wonder the information is difficult to obtain if corporates themselves cannot get their heads around it. And, maybe because of just that, 61% of institutional investors stated they will not invest in companies that lack a clear net-zero transition strategy. Therefore, going forward, talking about carbon footprint will become a must-have and no longer just a niceto-have.
3 / https://www.transitionpathwayinitiative.org/sectors/shipping
4 / https://globalcreditdata.org 5 / https://www.sc.com/en/insights/zeronomics/
Understanding the current and future carbon footprint is also a key factor for transition risk assessment of customers, but there are many more data (e.g. geographic asset distribution, disclosures, physical hazards sensitivities, net-zero transition risks) that have to be collected in order to assess how exposed to climate risk an institution’s customers and portfolios are.
Based on the new customer data, new models have to be developed which are more complex in order to factor in all the new information – potentially including unstructured data as well.
Climate and net-zero (big) data analytics
All in all, climate risk modelling brings a full array of challenges (see above) compared to traditional credit risk modelling, and a number of leading financial institutions are already deploying the latest advanced analytics techniques to tackle these.
There are many portfolio roads that lead to net-zero Rome, and each of them will have a different risk/return profile; the challenge lies in finding the optimal portfolio mix that will arrive at a portfolio which is net-zero carbon footprint by 2050, with the lowest risk and highest returns along the way under different macro and climate scenarios. To identify this optimal pathway, portfolio managers will need to collect more data and conduct more forward-looking portfolio analytics, get better and smarter in simulating the impact of their portfolio choices on portfolio KPIs and KRIs that from now on need to also include carbon footprint indicators and forecasts.
Compared to today’s way of working, this will include the need to run more simulations with multiple alternative asset allocation mixes over longer time horizons (see below), under multiple scenarios and with alternative business and modelling assumptions.
Looking for optimal de-risking & decarbonization path
Having done their math, portfolio managers can then take smarter portfolio allocation decisions backed by analytical evidence and maximize their returns while meeting their carbon targets and minimizing the risks. But in order to get there, the strategic asset allocation processes of institutions will have to incorporate more extensive usage of scenario analysis and portfolio optimization techniques according to the Net-Zero Investment Framework,6 put together by the leading global asset managers. As a matter of fact, according to the 2021 Global ESG Survey,7 more than 50% of surveyed asset managers plan to invest in their data aggregation and analysis capabilities.
Leveraging advanced analytics, scenario analysis and simulation capabilities, financial institutions can more easily and rapidly assess the impact of the alternative management actions and portfolio strategies on their KPIs and KRIs at the institutional, portfolio, sector and also the individual customer level.
Understanding quantitatively the potential future impact of alternatives (e.g. the 2 net-zero portfolio decarbonization mixes above) is an important input to find and make optimal decisions both on the portfolio and customer level.
Moreover, once the optimal portfolio management strategy is selected, growth products targeted and segments defined, the dedicated customer intelligence analytics can be used to identify optimal target customers for new product sales and campaigns, automate the loan decisioning and approval process, and thus improve the execution and success of these new strategies.
6 / The Institutional Investors Group on Climate Change (IIGCC) (https://www.iigcc.org/resource/net-zero-investment-framework-implementation-guide/0)
7 / https://securities.cib.bnpparibas/esg-global-survey-2021/
parting thoughts
Climate risk and net-zero represent additional use cases and the logical extension of existing analytical capabilities that are already in place and used by financial institutions around the world.
“[A] Bank’s ability to assess its overall exposure to climate risks across all of its significant operations will be heavily dependent upon the quality of its IT systems and its ability to aggregate and manage large amount of data.” (Bank for International Settlements8)
The challenges that accompany new developments will put extra pressure on the existing processes and infrastructure and institutions with legacy systems, and fragmented application landscapes will face difficulties in responding to these challenges in an efficient and effective manner.
An opportunity now exists for institutions to leverage the current climate risk and net-zero wave and modernize and automate their analytical activities and capabilities across the end-to-end process chain (see below) starting from climate data collection and integration through analytics, all the way to optimal decisions on both the portfolio and customer level.
In the end, institutions that can better analyze the impact of various alternative future scenarios and management actions on their customers and portfolios will be able to take smarter impact-aware decisions and successfully navigate the challenging and volatile waters.
Climate risk & net-zero process chain
8 / https://www.bis.org/bcbs/publ/d518.pdf
author
Peter Plochan
Peter Plochan is EMEA Principal Risk & Finance Specialist at SAS Institute assisting institutions in dealing with their challenges around climate risk, finance and risk regulations, enterprise risk management, risk analytics. Peter has a finance background (Master’s degree in Banking) and is a certified Financial Risk Manager (FRM) with 15 years of experience in risk management in the financial sector. Peter also delivers risk management trainings globally (PRMIA, RISK.NET, Bluecourses) covering climate risk, stress testing, ERM and Model Risk Management.
