For most enterprise risk management (ERM) specialists in asset management, banking, insurance or pension fund organizations, the need to integrate climate variables in decision-making processes is clear. In multiple jurisdictions, the regulatory agenda reinforces this long-term business logic and turns early-stage awareness exercises into "must-do" reporting items. The ERM expansion to climate risk is no easy task, though.

A complex scenario landscape

The first "global" climate models appeared in the second half of the 20^th century. Their evolution is linked to a combination of research breakthroughs, some Nobel Prizes and ongoing computing progress. Decades later, climate scenarios are still produced by multi-disciplinary teams of domain experts, with access to supercomputer resources. Fostered in large part by the work of the Intergovernmental Panel on Climate Change (IPCC) over the last 30 years, global climate model data are now published more broadly, with increased coordination and standardization across participating research institutes. Under the IPCC guidance, teams around the world have created new scenario sets blending emission trajectories (Representative Concentration Pathways, or RCPs) and socio-economic narratives (shared socioeconomic pathways, or SSPs). The combined "SSP-RCP" scenarios (see Fig. 1) are often used as building blocks for assessment studies, policy making and downstream financial scenario modeling exercises.

Fig. 1 - A complex climate scenario framework (Source: Ministry for the Environment, New-Zealand)

As advocated by riskthinking.AI CEO, Ron Dembo, in a recent SS&C webinar, climate scenarios are complex and must reflect the stochastic nature of possible long-term climate evolutions. For Dembo, this scenario generation process shall start with a large-scale aggregation of different institutes' model outputs for, as an example, the different SSP-RCP pathways. Proprietary data transformations and enrichment must be added as well.

Know your (climate) exposures

The generation of natural hazard distributions for each geographical area on the planet over different time horizons is only one (complex) part of the physical risk assessment jigsaw. Like in traditional financial risk management, it is also critical to identify and measure factor exposures due to the balance sheet structure of a firm, or the overall positions held by an investment fund. For climate physical risk, it means identifying geographical positions of physical assets owned or operated by a firm, and all portfolio companies. In line with long-term investors' appetite for "alternatives," these physical exposures can also be linked to private investments and related proprietary asset data. These assets can include commercial buildings, data centers, toll bridges, railways or power-generating assets such as solar or wind farms. Similar to how financial engineers need to select suitable pricing models to compute factor sensitivities of loans, bonds or equities, climate risk assessment requires process owners to source new data in the form of detailed geographical exposures across legal entities. As shown in Fig.2 below, the collection of granular physical asset data and leverage of large-scale natural hazard distributions should allow investment stakeholders to better understand likely climate impacts over various time horizons. The simulation results can also be used to identify primary risk drivers, shifts between acute and chronic hazard impacts and asset stranding risks across regions and industries.

Fig. 2 - Simulated primary physical risk drivers over different time horizons (source: askriskthinking.AI)

Expanding upon ERM foundations

Most sell- and buy-side financial institutions have established enterprise risk management (ERM) frameworks in place. These firms have a long experience in the identification and modeling of risks across various types. This also applies to the understanding of interconnectivity of risks, such as market, liquidity and credit risks. On their ERM journeys, these organizations have learned to evolve with new stakeholder demands for risk-based insights and regulatory requirements gradually shifting from rule-based regimes to more complex, principle-based frameworks. As explained by SS&C Algorithmics Dr. Andrew Aziz in a recent publication, the addition of climate risk to the chief risk officer's agenda might have a sense of "déjà vu" for ERM specialists. In the aftermath of the 2008 global financial crisis, in particular, regulators introduced more stringent programs to stress-test banks or insurance balance sheets and check overall liquidity levels. The same might be true for the integration of climate variables. After initial exploratory stress-testing exercises conducted by, for example, the European Central Bank (ECB) and the Bank of England (BoE), climate data integration requirements have or may now become mandatory across multiple jurisdictions. The ability to leverage decades of ERM experience, infrastructure and governance should be good news for most financial services and investment firms as they need, once again, to broaden the scope of their risk monitoring radars.

A key transformation agent

Enterprise risk management processes leverage technology across its multiple facets. In buy and sell-side organizations, ERM architectures blend legacy systems, customized data transformation layers alongside innovative simulation tools and cutting-edge computational resources often hosted on various Clouds. Risk managers across financial industry segments know how to use technology advances as key transformation agents. The same applies to teams focused on climate-related models and data services. At a time when the traditional ERM framework must again expand to take account of new business risks, stakeholder demands and climate-related regulatory requirements, the ability to embrace new technologies and AI-based tools will be crucial. The ability to build upon established ERM foundations will also bring comfort to many operational teams. Whether the ERM expansion to climate variables is an easy task or not, most CROs will need to address these new data, risk integration and impact assessment challenges in the coming years, if not sooner.

For deeper insights, view the recording of the joint webinar featuring SS&C Algorithmics and riskthinking.AI experts as they discuss about the climate risk imperative, and what it means for financial risk management.

Download our recent SS&C Algorithmics whitepaper publication to learn more about climate risk materiality assessment and why CROs need to take an ERM approach.

Contact usto learn how you can use SS&C Algorithmics' risk analytics in your own risk management, portfolio construction, asset allocation, economic capital management and reporting processes.

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