A practical approach to embed ESG data within your risk framework

Explore how merging financial and extra-financial & ESG data with real-time analytics allows financial institutions to adapt to ever-evolving ESG demands and regulations and make better decisions.

Guilllaume Felix
February 26, 2024
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The draft guidelines put forward by the European Banking Authority (EBA) in January 2024 aim to extend the ESG risk management framework to regulatory processes, adding robust governance, policies, methodologies and systems, including prudential calculation with backward and forward looking metrics. 

Financial institutions need to be ready to implement ESG risk management approaches based on their business model and activities. To comply with evolving regulations, the main challenge is to strike the right balance in shifting from static superficial disclosures to robust and flexible processes bringing new business-led opportunities. 

To overcome ESG risk challenges, the EBA advises financial institutions to adopt effective risk strategies for their business model and risk profile over short, medium and long-term horizons. The identification of ESG risk drivers must be performed comprehensively and regularly based on sound data processes, combining exposure, portfolio, and scenario-based methodologies across traditional financial risk categories (credit, market, liquidity, concentration, …).

Financial institutions should establish robust internal procedures and sound systems to collect and aggregate ESG data and related risks. To adhere to data governance and IT infrastructure best practices, assessing and improving ESG data quality is a must at different levels of aggregation.

The new objectives focus on the definition of new methodologies and the unlocking of capabilities, according to the EBA, to: 

  • Identify ESG risk drivers and their transmission channels to prudential risk types and financial risk metrics via the institution’s exposures;
  • Map exposures and/or portfolios according to ESG risk drivers, and any concentration within or between them, 
  • Measure and manage material ESG risks including with a forward-looking perspective at counterparty and asset-level data

The EBA says that financial institutions should set up a wide range of backward and forward-looking ESG risks metrics and indicators to be monitored continuously:

  • Historical losses and forward-looking estimate(s) of exposures-at-risk and (potential) financial losses related to ESG risks, e.g. based on scenario-types methods; 
  • Amount and share of income (interest, fee and commission) stemming from business relationships with counterparties operating in sectors that highly contribute to climate change Large institutions should introduce more granular monitoring metrics, such as by calculating income stemming from relationships with the most carbon-intense counterparties
  • A measure of the potential gap between existing portfolios and benchmark portfolios consistent with the climate target net-zero emissions 
  • Carbon emissions
  • The percentage of counterparties with whom the institution has engaged on ESG risks matters, 
  • Ratios representing as part of the institution’s total exposures the share of environmentally sustainable exposures financing activities,
  • A breakdown of portfolios secured by real estate according to the level of energy efficiency of the collaterals, 
  • A measure of concentration risk related to physical risk drivers (e.g. measurement of exposures and/or collaterals in high flood risks or wildfire risks areas), such as by using a sufficiently granular geographical split of exposures (...)

To calculate these new metrics, banks will face incremental hurdles, each demanding meticulous attention and innovative solutions:

  • Data Availability: Ascertaining the requisite data sources and potentially necessitating the collection of external data to supplement existing resources
  • Data Quality: Applying the data quality principles already in place, from BCBS 239,  across the bank, to ensure accuracy and reliability, with a concerted effort towards continuous improvement,
  • Sophistication of evolving metrics: Confronting the complexities inherent in the development and testing of sophisticated new metrics, requiring a blend of expertise and experimentation

The integration of dedicated new stress scenarios and monitoring within existing processes may also be a challenge. For instance, to go beyond the disclosure exercise, business impact must drive effective ESG-led decisions. 

What Opensee brings to ESG risk management

Opensee is reshaping how financial institutions manage ESG risk by combining financial data with extra-financial data. This integration empowers users with real-time analytics and the ability to swiftly adapt to evolving datasets and analytics demands. The platform's robust features allow for the exploration of multi-dimensional data in real-time, facilitating granular assessments of physical risks and climate stress testing, enhancing decision-making and risk analysis in managing ESG challenges.

The platform not only serves as a comprehensive repository for quantitative analysis and monitoring, but also offers unparalleled flexibility and agility thanks to an evolutive Data Model, which can be easily changed or enriched with new datasets It enables users to autonomously generate complex calculations and tailor analytics to meet the dynamic requirements of ESG frameworks, ensuring transparency, replicability, and accuracy. This adaptability is crucial for financial institutions aiming to align their strategies with global commitments like the Paris Agreement and the Net-Zero Banking Alliance (NZBA).

Built on a philosophy of user empowerment, Opensee provides a suite of tools and components that foster autonomy among business, quant, and IT users. With limitless storage, robust data manipulation capabilities, AI tools, and Python libraries, users can customize analytics and simulations to their specific needs without compromising integration with existing IT infrastructures, whether deployed on the cloud or on-premise.

Through its innovative approach, Opensee equips financial institutions with the tools to navigate complex datasets effortlessly, enhancing their ability to make informed ESG decisions. This technology enables institutions to stay ahead of the curve, leveraging data to drive a sustainable strategy and respond proactively to the increasing demands of regulatory requirements and business landscapes.

About the author: Guillaume Felix joined Opensee as Head of Liquidity and Banking Book risks Solutions, leveraging 15 years of experience garnered in distinguished financial services consulting firms like EY advisory and Equinox-Cognizant. Throughout his career, he has honed expertise in credit risk, prudential regulation and data management for the Banking industry.

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