Operationalizing ESG Information – How Financial Services Firms Can Do It Right

By Martijn Groot, VP Marketing and Strategy, Alveo

Today’s financial services firms increasingly recognize the key role that ESG metrics play in decision-making throughout the investment management process. This is driving many people to step up their ESG data management processes. In a recent Alveo study surveying the views of 300 asset owners and managers in the UK, US and Asia-Pacific, 95% of respondents said they were looking to improve their data management ESG.

Part of this is governed by regulation. The push towards ESG disclosure under the Sustainable Finance Disclosure Regulation, which impacts any company selling or distributing investment products in the EU, means asset managers are required report on the ESG measures of their portfolios and provide appropriate documentation on data sources. or patterns behind the reported information.

However, only 21% of the Alveo survey sample cited “regulatory reporting” as a key driver of their use of ESG data. This indicates that beyond regulatory compliance, improving ESG data management is something that companies see as a necessity to increase the value of their business.

ESG factors impact most business processes. In corporate banking, for example, ESG data is increasingly crucial to support customer onboarding and, in particular, Know Your Customer processes. Banks and other sell-side financial services firms also frequently screen vendors, in a process called Know Your Third Party. They will also want to weather stress test the products they hold in their trading portfolio for their own investment against adverse weather scenarios.

On top of all this, sell-side and buy-side companies will need to integrate ESG data with data from more traditional price and benchmark providers to give a composite view, integrating instrument prices; terms and conditions as well as ESG characteristics.

Evaluate the challenge

ESG data should be embedded across the organization, integrating with all the different data sets to provide a composite picture, becoming a key source of intelligence, both for the front office and for workflows in the areas of risk, finance and operations.

For many companies, it is difficult to do this well. Finding accurate ESG data and interpreting it correctly is difficult because information must be gathered from multiple datasets, including third-party estimates, ratings, news, and corporate information.

In addition, there are often disparities in the methodologies used by third parties to rate or score companies on ESG criteria, which complicates the analysis. However, the biggest challenge for many companies is how to consistently integrate ESG data into all required business processes that cross departmental boundaries to put users on a level playing field. This requires Quickly integrate new data sources, integrate, harmonize and verify data, fill gaps if necessary and provide them to users and business applications.

Achieving all of this is complex. The data management function and operating model are often siloed and ill-suited to quickly integrate new information and embed it into a company’s operations. ESG data still often needs to be integrated into broader reporting, especially in finance and risk, which are usually the functions where all information flows necessarily come together. Companies are therefore focused on improving their management of ESG data and are ready to invest to achieve this.

Whenever new categories of data or new risk metrics are introduced, data management practices typically begin with improvisation using desktop-level tools including spreadsheets, databases, and more. local data and other workarounds. This is gradually streamlined, centralized, operationalized and eventually integrated into core processes to become the status quo.

Generally, companies should refer to a comprehensive data model that covers regulatory ESG information and underlying data sets. Additionally, they should achieve transparency and clearly record the sources and types of data used, the business rules used, and any manual corrections.

Find a solution

A holistic approach to ESG data management is needed to provide consistent data to address multiple use cases. This means using data management solutions and data-as-a-service offerings, which are now available to help companies acquire the ESG information they need, the capabilities to quality check, complement and complement it. enrich with their own proprietary data or methods and integration functionality to put users and applications on an equal footing.

Achieving this requires that all data quality challenges are addressed from the outset. What organizations need is a process that transparently acquires, integrates and verifies ESG information.

Any data management function must also facilitate the ease of discovery and explanation of information and its effective integration into the workflows of business users. Specific functionalities should include cross-reference taxonomies and condensed information, for example to generate reports on indicators that serve as performance KPIs or that meet reporting mandates.

Data derivation capabilities and business rules can spot gaps, highlight outliers, whether tied to historical patterns or within a peer group, industry or portfolio ; and provide estimates if necessary. Additionally, historical data to run scenarios can help in proper risk and performance assessment of ESG factors.

The regulatory speed to drive a sustainable economy not only confronts companies with a very tight implementation schedule, but also major challenges around the sourcing, processing and quality assurance of large sets of often unstructured data. Mastering this data challenge is a prerequisite for successfully competing for new market offerings and sustainable products.

Early operational readiness is key to staying ahead of the curve in adapting to the new ESG regime. The key decision points that need to be considered right now are, first, determining the target operating model and governance, second, designing the target data and system architecture, and third, pursuing ‘proven approach for custom implementation.

Once a data management system has been implemented into an efficient operating model, it has many benefits: from effectively integrating data and provisioning business users to securing lineage. data management and data usage and cost management. This increases the return on all existing and future ESG data investments. Ultimately, enterprise-wide availability will benefit the entire organization and ensure that businesses optimize their data.

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