Why sustainability now plays such a big role in investment decisions?
“There are two main reasons. First, sustainability factors are increasingly financially material. Second, clients, regulators and other stakeholders expect sustainability considerations to be part of sound investment practice.
In practical terms, this means that climate, nature and societal issues now have a direct impact on revenues, capital expenditure, assets, liabilities and funding costs across many sectors.
If we look at this from risk management and resilience perspective, factors such as transition policies (for example the bank’s Net Zero Pathway Assessment or Fossil Fuel Strategy), carbon pricing, litigation risks and supply‑chain disruptions linked to geopolitical events can all drive earnings volatility and significantly increase the risk of stranded assets.”
What sustainability models actually do?
“Sustainability factors require robust models that translate complex, forward-looking topics into insights that can actually be used in investment and risk management decisions.
Sustainability-related models therefore serve several purposes. One of the most important is the principle of double materiality. These models help financial institutions address both financial materiality and impact materiality at the same time.
They also help turn ESG topics into decision-ready insights for sustainable investments and lending.
And finally, sustainability models make it possible to quantify risks and opportunities in cash flows, valuations, credit metrics and capital needs. This, in turn, support more informed decisions on company valuations, limits and other key parameters.”
A closer look at work behind the scenes
“I work with two sustainability-related models – House View and mScore. Both are high-tier models, which means they must be re-validated annually. Because these models are constantly evolving, re-validation takes up a significant part of my time.
Each validation covers several areas: assessing the quality and adequacy of the documentation, validating input data and performing a conceptual review. I then conduct implementation, performance and stability testing, followed by the analysis of the results.
We also assess whether the models align with the current regulatory landscape, which continues to evolve. Each validation ends with us issuing observations to model developers and owners.
In addition, I cover areas such as validations of the Group Investment Profile (GIP) and SustAInalyst, a GenAI-based chatbot that supports investment and ESG specialists with asset management and responsible investment queries.”
Where sustainability modelling is heading next?
“Regulatory expectations are constantly evolving, pushing sustainability models towards greater (almost scientific) rigour, traceability and resilience. Especially in the context of the forthcoming Sustainable Finance Disclosure Regulation (SFDR) 2.0 which should enter into force in late 2027 or 2028.
Looking ahead, there are five key developments I see shaping sustainability modelling:
- Transition pathway modelling. Greater focus on forward-looking credibility of company transition plans, pathway alignment and real-world outcomes, not just carbon footprint snapshots from the past.
- Embedded controls and explainability. Pre- and post-trade sustainability controls, greenwashing risk checks and user level explainability built into whole investment decision making process.
- Extended scope beyond climate. Integration of nature and biodiversity (TNFD), human rights and minimum safeguards, with double materiality principle underpinning the logic of sustainability models.
- Shift to issuer reported inputs. Wider use of Corporate Sustainability Reporting Directive (CSRD), European Sustainability Reporting Standards (ESRS) and EU Taxonomy disclosures as well as implementation of SFDR 2.0. Ideally this should result in proper audit trails, version control and reduced reliance on questionable vendor ratings.
- Scenario and uncertainty management. Routine use of multi scenario, probabilistic and sensitivity analysis for Principle Adverse Impacts (PAIs), Do No Significant Harm (DNSH) principles and alignment metrics, with special focus on treatment of estimation uncertainty.”
