Integrating ESG Factors into Credit Loss Provisioning: A Strategic Imperative
As the financial world grapples with the increasing importance of Environmental, Social, and Governance (ESG) factors, a new challenge emerges: how to effectively incorporate ESG into credit loss provisioning under IFRS 9. This integration is not just a matter of compliance, but a crucial step towards more accurate risk assessment in an evolving financial landscape.
As the financial world grapples with the increasing importance of Environmental, Social, and Governance (ESG) factors, a new challenge emerges: how to effectively incorporate ESG into credit loss provisioning under IFRS 9. This integration is not just a matter of compliance, but a crucial step towards more accurate risk assessment in an evolving financial landscape.
EBA’s guidelines on loan origination and monitoring require banks to consider ESG in their credit assessment process. Even ECB has set expectations for banks to use ESG in their risk management processes. Also, as IFRS 9 requires the consideration of forward-looking information for assessing credit loss provisions, banks and financial institutions need to integrate ESG factors into their provisioning process to ensure compliance with the standard.
Ignoring ESG risks could result in underprovisioning and non-compliance with IFRS 9. However, given that ESG is a novel risk and banks and regulators have traditionally relied on historical data for risk management, developing alternative approaches to address this new challenge has been necessary. Although there could be some acute and immediate impacts from ESG risks, the implications of these risks typically unfold over extended time horizons. The intensity of the impacts is also expected to increase over time. The long-term nature of ESG risks makes it even more challenging to account for their impact within the 12-month window used for assessing impairments and credit losses. Building the capability to develop robust models that capture this novel risk takes time, as data must be collected and processed for modelling purposes. In the meantime, banks are experimenting with different methodologies such as:
- Incorporating ESG adjustments into macro assumptions
- Capturing ESG risks indirectly by including the impact on other existing risk drivers, e.g. financial ratios
- Making in-model parameter adjustments
- Implementing post-model overlays
According to ECB, there has been a significant increase during the past couple of years in the number of banks that consider ESG factors in their credit loss provisioning. But ECB has noted a wide divergence in the sophistication of the methodologies that are used for assessment and measurement of adjustment needs. It is very important that banks show adequate level of substantiation in their methodologies and try to make enhancements by going away from subjective assessments. As mentioned above, some banks take ESG factors into consideration by incorporating adjustments into their macro assumptions that go into their IFRS9 models. The weakness with this approach is lack of sensitivity as no or very little differentiation is made between the varying effects on different segments and portfolios. Another common gap in the credit loss provisioning process of the banks that take ESG into account in their calculations is the disconnect between the ESG factors and the staging process. When the adjustments are made on a more subjective basis and indirectly through the macro component of the model, it would be difficult to translate the impact of them into the staging process.
To cope with the challenges posed by the novel risks and particularly the ESG risks, and ensure that they are adequately considered in the risk management framework and models of banks, the following areas should be worked on:
Data Management: Data is the building block of models. Banks need to develop a robust and flexible process for capturing and gathering ESG-related data. This goes beyond traditional data collection. The goal is to create a dynamic data management system that can:
- Easily incorporate new data points
- Adapt to emerging ESG metrics
- Provide a flexible foundation for ongoing risk assessment
Flexible Risk Modelling: Risk models must be designed with inherent adaptability. This means creating sub-models (Probability of Default, Loss Given Default, Credit Conversion Factor) that can be:
- Quickly refined
- Easily adjusted without extensive time and resource investment
- Responsive to new ESG insights and data points
Post-model Overlays & Governance: Given the lack of historical data to build meaningful statistical models with acceptable quality, a robust and transparent process should be in place to ensure that novel risks such as ESG are identified, measured and factored in the risk management processes and frameworks. When using post-model overlays as a tool to capture novel risks, it is important to ensure sufficient risk coverage and risk sensitivity by using relevant analyses combining quantitative and qualitative factors and not relying solely on subjective judgements. Alternative modelling in the form of stress tests and scenario analysis could make up a sound ground for overlays.
As the financial sector navigates the complex intersection of ESG and credit risk, incorporating these factors into credit loss provisioning becomes not only a regulatory requirement but a strategic imperative. By tackling these challenges head-on, banks can strengthen their risk management, enhance long-term resilience, and contribute to a more sustainable financial system.
To successfully navigate this evolving landscape, organizations need a clear and comprehensive roadmap for their ESG journey, developed with input from all parts of the organization, as well as the capability to react promptly to new insights and expectations in the ESG space.
