Home Finance Getting on the Right Side of ESG Data: Don’t (Green)-Wash It Away

Getting on the Right Side of ESG Data: Don’t (Green)-Wash It Away

by internationalbanker

By Martijn Groot, VP Marketing and Strategy, Alveo


The setting of climate policies by financial services firms is a positive sign that these organisations increasingly recognise the need to implement climate action to attract and retain customers, employees and investors. In turn, they are also increasingly asking their suppliers for their ESG ratings. Financing the future will have to take climate change mitigation through net-zero strategies into account. 

However, the strong growth in demand for investment products with ‘green’ credentials has led to an abundance of ESG labelled products sometimes with unsubstantiated claims or even misleading information on their sustainability. This has had regulatory and legal consequences and accusations of greenwashing – and that can have serious implications.

According to the EU Taxonomy Regulation, greenwashing refers to the practice of gaining an unfair competitive advantage by marketing a financial product as environmentally friendly, when in fact basic environmental standards have not been met. However, the term can be used in a more generic sense to suggest that a product or service is provided by a firm that has certain ESG credentials, when it does not.

As Justine Sacarello, UK Legal ESG Head for KPMG in the UK, recently put it in an article entitled, ESG: Addressing greenwashing in financial services, firms need to be proactive in mitigating the risk of allegations of misleading statements or greenwashing to avoid enforcement action and complaints, particularly, regulatory investigation and censure, civil litigation and the negative financial impacts arising from reputational risk.

We are certainly seeing instances of greenwashing increasing in number across the financial services sector. In May 2022, the US Securities and Exchange Commission charged BNY Mellon Investment Adviser for misstatements and omissions around ESG considerations when making investment decisions for mutual funds it managed. To settle the charges, BNY Mellon agreed to pay a US$1.5 million penalty. The case is believed to represent the first time that the SEC has settled with an investment adviser concerning ESG statements.

DWS recently faced regulatory probes in the US and Germany after its former chief sustainability officer alleged last year that the company inflated its ESG credentials.

Fighting back – firms battle to secure their reputation

Until recently it was difficult for financial services firms to counter such claims, largely because ESG data management remained at a low level of maturity across both the buy side and the sell side. Although reporting frameworks such as the principles for responsible investment (PRI) and global reporting initiative (GRI) standards have been in place for decades, the absence of standard data collection, integration, and reporting solutions often required firms to create their own “ESG data hub” to provision their analysts, front office, and client reporting teams. Because data was often missing, firms had to find ways to fill in the blanks using third party opinions or internal estimates.

While the Sustainable Finance Disclosure Regulation (SFDR) requires asset managers to report on their portfolio ESG metrics and provide documentation on the sources or business rules behind the reported information, such disclosures are just a start. SFDR requires firms to report on mandatory Principal Adverse Impact (PAI) Indicators as well as some optional ones. Paradoxically, the reporting requirements for the companies that asset managers invest in lag behind the SFDR timetable.

Sourcing accurate ESG data and properly interpreting it is a particular challenge, as information needs to be gathered from a wide array of data sets including third party estimates, ratings, news, sentiment data and corporate disclosures. Corporate disclosures especially are still patchy and sometimes difficult to come by, while the withholding of relevant data means that records are frequently incomplete or held in silos. This causes an information gap and the need to supplement corporate disclosures with third party ESG scores, expert opinion, as well as internal models to come to an overall assessment of ESG criteria.

That’s clearly a concern as any information gaps will make compliance more complex to achieve. Data preparation processes need to withstand rigorous scrutiny as regulators demand the ability to explain figures as they look into the thorny greenwashing issue. Firms must be proactive in mitigating the risk of allegations of misleading statements or greenwashing to avoid enforcement action. In doing so, though, firms face challenges in terms of usability, comparability and embedding data.

Usability issues include the disparity in methodologies third-party firms use to estimate or score firms on ESG criteria. Rating firms have their own input sets, models and weights and often come to different conclusions. Compared to credit ratings, the correlation between the scores given to a firm by different rating agencies is lower.

Comparability issues in ESG are exacerbated by different standards, different reporting frequencies or calendars and also the lack of historical data to track progress and benchmark performance over a longer time period. ESG data is often reported at issuer level and needs to be linked to instrument data. Firms also need the ability to drill up and drill down corporate structures and to cross-reference between different entity and instrument identifiers to easily find ESG data. Composite datasets (including historical data) gas to be created out of validated and cross-referenced data from a range of data suppliers covering ratings, estimates, sentiment data and corporate disclosures joined into one data model.

The biggest challenge in many firms, however, is how to embed the ESG data in a range of different business processes to put users on a common footing. This requires the capability to quickly onboard new data sources, integrate, harmonise and vet that data, fill in the gaps where needed and provide it to users and business applications. So what’s the solution here? How can firms start to deliver the kinds of ESG data management environments that enable them to fight back against accusations of greenwashing and build truly sustainable organisations.

Putting ESG data to work

A comprehensive approach to ESG data management is needed to provide consistent data to service multiple use cases. Data management solutions and Data-as-a-Service offerings are now available to help firms acquire the ESG information they need, the capabilities to quality-check, supplement and enrich it with their own proprietary data or methods, and the integration functionality to place users and applications on a common footing. This will help firms establish strong ESG credentials and push back against potential accusations of greenwashing.

Achieving this demands that any challenges presented by the quality of data are dealt with from the outset. What organisations need is a solution that seamlessly acquires, integrates and verifies ESG information. Additionally, historical data to run scenarios can help with adequate risk and performance assessment of ESG factors.

A data management function should also facilitate the easy discoverability and explainability of information and effective integration into business user workflows. In short, data management should service users from the use case down, rather than from the technology and data sets up. Specific capabilities should include cross-referencing taxonomies and condensing information, for example to report on indicators that serve as performance KPIs, or that meet reporting mandates in the financial sector.

Data derivation capabilities and business rules can spot gaps, highlight outliers, back-fill historical data, look for patterns, or outliers within a peer group, industry or portfolio; and provide estimates where needed. Concurrently, firms require the ability to track and report on metadata and data quality metrics for the overall composite set as well as individual vendors’ data sets.

Once a data management system has been put in place within an effective operating model, there are many benefits: from efficient data onboarding and provisioning business users to securing data lineage and usage monitoring. This significantly increases the return on any existing and future ESG data investments. Firm-wide availability will increase usage and, in turn, will benefit the whole organisation and ensure firms are getting the maximum mileage out of their data. And of course, with such a robust approach in place, firms should be in a strong position to counter any complaints of  greenwashing that they might face and instead put their credentials as forward-looking, sustainable organisations on display.


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