As an industry, the rapid increase in importance of ESG use cases has come upon us faster than most structural changes. This is partly driven by the market, with investors opting for responsible investing and each deciding what that means to them, and partly by regulations stemming from international agreements on net-zero and other climate-related sustainability targets.
At this stage, many of our clients are finding that ESG is generating more questions than answers. The questions generally fall into two categories: those concerning business needs; and others around the data management and decision support capabilities required to deliver on those business needs.
Typical questions around business needs and ESG use cases include:
If I need to replace an investee company in my portfolio because its ESG scores are too low, how can I easily identify another company that has good ESG scores and the underlying financial fundamentals that justify an investment?
If ESG scores are not available for a company, how can I easily find its parent, or another company in the same group that is representative of the company in question, and use its ESG scores as a proxy for the missing ones?
What information do I need and what steps can I take, such that my clients have confidence that my portfolio, fund or ESG scores are not influenced by greenwashing?
What ESG-related challenges will I face when I aggregate my portfolios for reporting?
What granularity of data will I need for ESG-related risk analytics?
What will it take to screen out securities that are incompatible with my mandates for sustainability, or to screen for position sizes and sector allocations?
Typical questions around the technical capabilities needed to support the business include:
How do I efficiently operationalize my ESG data and apply the discipline of data governance?
How can I efficiently complete my Sustainable Finance Disclosure Regulation (SFDR) submissions and manage everything around the EU Taxonomy?
How do I satisfy the workflows for all lines of business and the functional needs and insights for investment management, client reporting, client analytics, ESG products and marketing?
How much of my ESG workload can I automate and how can I reduce integration effort?
What infrastructure, tools and capabilities do I need to put in place now to manage future ESG taxonomies, jurisdictional regulatory requirements and reporting templates?
With ESG criteria-based investing set to continue rapid growth, firms that offer ESG investments or research, and seek competitive advantage, are identifying many operational capabilities they need to consider. First, they need to make sense of all the relevant sources of data on ESG-compliant investments. Second, they need to know what is required to comply with regulations, such as the Markets in Financial Instruments Directive (MiFID II) sustainability preferences or the EU’s SFDR. Third, banks and investment firms need to understand operational ESG use cases for modeling, analyzing and publishing ESG data, reports and research, and how they can competitively meet those demands. This last step is complicated by the lack of standards for assessing ESG compatibility.
Firms offering ESG-aligned investments have identified data transparency, availability and data costs as significant challenges. Much of the discussion focuses on the lack of standard, publicly available ESG data from corporates. This has resulted in data products being offered by data vendors and ratings agencies that service a need but come with their own challenges around methodologies and standards. The key concern is that the usability and reliability of ESG ratings and data products needed to do business competitively and comply with regulations are undermined by a lack of transparency in the methodologies used by data and ratings vendors. Also, in some areas there is a lack of data, or gaps in the data available on investee corporates.
This makes effective tooling imperative, and this is where GoldenSource can help with ESG data management. To solve for the questions above, firms need to understand the use of ESG data and have easy integration of, access to, and querying of data from a network of vendors to get all the ESG data coverage that is relevant to their research, analysis, fund/portfolio management and reporting. Fundamental to achieving this will be built-in cross-referencing of the materiality of the data provided by different sources to arrive at as much normalization and standardization as possible.
Finally, to achieve maximum competitive advantage will also require automated workflows and validations for all those business processes, linkage between ESG, instrument and issuer/entity data, plus the ability to drill down into the underlying granular ESG data to uncover unique insights of value.