ESG data resiliency: From fire-fighting regulations to commercialising new opportunities
Introduction
ESG has become the new normal. We are experiencing a major overhaul of investing regulations and a new generation of investors pushing for greater transparency and more accountability.
Investors now expect asset managers to generate consistent returns across multi-asset portfolios while having a positive impact on the community and environment.
Investment firms want to better reflect stakeholder values across social and climate issues and realise there is an opportunity to deliver meaningful returns, value and competitive edge, to attract new clients and secure future growth.
So why are so many asset management firms still so far behind?
The problem lies in the status quo of best endeavour approaches and heavily manual workflows, which many firms operate in, but particularly in ESG investments and sustainable finance.
This can pose a considerable risk to both reputation and human capital by facilitating greenwashing and employee burnout. However, the ESG data challenge is not problem
in of itself; it is the epitome of what is wrong with capital markets infrastructure today. There are huge limitations from having a mainframe legacy compounded into one investment area that just so happens to have planet-saving consequences.
Investment managers are battling with challenges on several operational fronts: the aggregation of complex and diverse data sets; interpreting non-standardised reporting frameworks; and the lack of entity-level granularity. At the heart of this is the fundamental problem of understanding and deriving value from ESG data.
Achieving consistent data quality and building a flexible data foundation will be critical for firms to secure the resiliency they need to manage climate-related risk, meet stricter ESG regulations and respond to investor-driven transparency.
In this guide, we shine a light on the primary operational pain points and show how investment firms can break away from a ‘best endeavours’ or ‘fighting fires’ approach to find commercial opportunities that will grow their business for the future.
Meeting the SFDR challenge
Since the EU’s introduction of its landmark Sustainable Financial Directive Regulation (SFDR), a bifurcation has emerged between ESG approaches in some geographies, particularly between the US and Europe. Global companies must comply with SFDR if they operate in the EU and UK.
The key challenge that articles 6,8 and 9 funds face is deriving value from multiple, disparate ESG data sets to drive reporting and informed decision-making. Data consistency, understanding and granularity are big issues, which is no different to any other asset class or data challenge.
To meet SFDR’s requirements, investment firms need a robust data and reporting strategy to achieve operational resiliency and navigate the regulatory ecosystem.
They must transparently demonstrate the following:
- How have you incorporated sustainability risk into your investment decisions?
- How have you used relevant data to comply with SFDR’s definition of sustainable investment activity for articles 8 and 9?
Managers also need to understand how to manage ESG data sets and have a flexible data foundation. This starts with sourcing data that is fit for purpose.
Navigating the fragmented data vendor universe
Vendors play a key role in assessing external data providers to generate accurate and complete data to meet disclosure requirements. Given the fragmented nature of the vendor market, it is important for investment managers to source from multiple providers. But sourcing ESG data from multiple vendors is still a primary challenge for managers, as well as consistent and accurate data. Incomplete data means that managers are often forced to fill in the gaps on spreadsheets.
Having a broad selection of vendors is essential for three key reasons:
- There is a lack of data correlation between vendors, with as little as 0.6 correlation in environmental and climate data and 0.8 in governance data.
- Timeliness of data can vary considerably across vendors.
- Certain vendors lack drill down between parent and entity level securities, which can impact portfolio ratings.
While COOs do recognise the value of having multiple niche ESG data players, many are forced to squeeze their pool of vendors due to having several expensive contracts. It is particularly hard to justify this right now when many firms are under pressure to lower costs.
There is a growing view among managers that a centralised utility with a layer of analytics on top could reduce costs and lead to greater data quality and completeness, however this would require a framework for vendors to work together.
This can be achieved in a scalable manner through a modern data stack to provide transparent financial data lineage across departments. It must be underpinned by a flexible operating model to help firms adapt to changes in regulation while avoiding unnecessary costs.
This would generate value from multiple, disparate data sources to promote consistent understanding and feed into portfolio management reporting, governance and decision-making. Firms could then automate data cleansing activities through a rules engine that searches different sources to fill in any data gaps.
While 2022 was the year the industry really felt the SFDR pinch, it was also a year when many firms discovered that a strong data strategy and delivery of insights is less a luxury and more of a necessity, to firefight both regulatory and client demands.
Key challenge areas include the sourcing of data, to ensure access and consistency for all your business users. Where third party data is concerned, the challenge lies around the completeness of data and management of vendors and contracts. Many firms are tackling this with a central function, including the creation of Chief Sustainability Offers, to handle data consolidation, cataloguing and capturing, efficiently. Going forward, addressing controls, governance and data quality will become more of a priority as the regulatory landscape evolves.
Getting the house in order is now essential. However, it is not all about firefighting and combating greenwashing. Achieving a flexible data operating model now, for ESG means firms can get out in front of market opportunities, including the continued inflows from asset owners.
Sourcing ESG data from multiple vendors and ensuring that it is made available consistently and accurately across the business continues to be a challenge for firms.
The crucial factor lies in firms’ capacity to traverse seamlessly between different levels within instruments, underliers and entities. This allows the user to extract vital information which can be used for generating insightful reports, implementing effective governance practices, and making informed decisions. By effectively traversing across these elements, firms can derive the value and meaning required to enhance their overall performance.
The ESG data challenge is not unique, many of the challenges apply to other types of data but the increasing focus on ESG and its relevance
as a ‘new data set’ have exposed these existing challenges such as ridged data models. Like investment strategies and asset classes,
the trick is to interoperate with existing systems (or data models) and be able to migrate, change and interpret between the various models or systems.
Fit-for-purpose data
Data is still often used for operational purposes rather than client reporting purposes, but firms realise this is no longer viable in the context of SFDR. There is now a clear urgency to access clean, interoperable data at a lower cost.
The sourcing and aggregation of data are a big concern for firms, particularly in client and regulatory reporting. They often lack the tools to source and aggregate data promptly and correctly and are also forced to manually reconcile due to sourcing challenges or incompleteness of market data.
Firms should therefore adopt best practice around consolidating and translating multiple data points to derive value for operations, trading, and reporting.
A robust data and reporting strategy can help funds achieve four outcomes:
- Navigate EET regulatory reporting across the ecosystem of vendors and distributor.
- Efficient collation and disclosure of information on sustainability risks and product characteristics.
- Accurate data and reproducible reporting.
- Compliance with SFDR requirements and avoidance of breaches and fines.
- Creation of new investment strategies that already have ease of reporting built in.
Investment firms are keen to avoid an expensive, ‘big bang’ change to their data and operations. The solution is working with a partner who can deliver through an evolutionary approach while being mindful of a firm’s operational limitations and low-risk appetite.
A cloud-native Modern Financial Data Stack can deliver that much-needed careful balance between low-risk and low-cost operational transformation while providing the flexibility to interpret and derive value from their investment, market and reference data.
With cost high on the agenda, firms are having to think wisely about how to piece together a data strategy to navigate the EET regulatory reporting environment. Sourcing forms a major headache for many, with a significant amount of time spent filling in the gaps manually.
While this data is often operational rather than for client purposes, with SFDR now in the mix, even article 6 funds will have to provide far more reporting and risk measurement than before, meaning both process and output will need to be tightened up.
What firms need to consider at this juncture, is an integrated approach, to expedite data search & discovery, collaboration and analysis, curation, data governance, metadata management and data quality through a centralised data catalogue.
This will, in the long term, provide firms with the accuracy and granularity needed for reproducible reporting and client communication. Without this quantified data, companies risk greenwashing accusations, or reporting false or misleading information, which can have legal consequences.
Investment managers must be able to monitor their portfolios’ ESG performance in real time and disclose principal adverse impacts. For this, they need a robust data foundation and enhanced data management capabilities to ensure portfolio compliance with the ESG mandate.
The added investment management benefits, including an extensible model that can pull in data from multiple systems and interfaces, means firms can utilise ESG metrics from a variety of different sources. Mapping securities to legal entities to enable granular analysis of a portfolio, safeguards against the all too easy comparison of apples to oranges.
Whilst the reporting requirements are standardised, even within the same company funds will naturally be focused on different areas of the ESG spectrum, so portfolio construction and reporting may be driven from different
data sets. With this level of functionality and resiliency, investment managers can avoid the risk associated with the rigid and siloed confines of legacy data.
Resourcing and resiliency
Mapping multiple ESG ratings at issuer level through to a security is critical to enabling granular ESG analysis and applying the EU taxonomy under SFDR. It is important for reporting P&L to track performance for article 8 and 9 funds, but also for article 6 funds which will need to move quickly as investors increasingly demand greater accountability within client reporting.
As well as enabling granular analysis at the portfolio level, LEIs also help funds avoid comparing apples with oranges.
To create SFDR robust portfolios, firms need entity level ESG granularity, but many vendors do not go into this level of detail at the parent or group level. It is therefore critical for firms to look for vendors that can deliver legal entity hierarchies which hold data at the applicable level.
To help support their ESG operations, some larger asset management firms are creating Chief Sustainability Officer roles. Smaller firms without dedicated resources prefer to rely on third-party ESG data vendors while also looking to buy or build from software providers.
Operational resiliency should be a top priority for investment firms when looking for a long-term solution to comply with SFDR. COOs should check whether their operating model can stand the test of time in an evolving regulatory landscape. It should be able to cope with new datasets and reporting requirements and deliver the analytics and insights required to create new products while achieving better accuracy and keeping the total cost of ownership low.
Entity level data is a key priority. With cost high on the agenda, it is easy to narrow the pool of vendors in your mix however this presents its own risks, particularly in a fragmented landscape.
Having a broad selection of vendors is critical, given the ability to drill down between parent and entity level securities is not possible with certain vendors.
Understanding the implications this can have on portfolio ratings and any ‘secret sauce’ analytics added on top, is important both for demonstrating confidence internally and to clients.
Article 8 and 9 funds need to make use of multiple data sources and data models to create investment universes that meet specific ESG mandates. To achieve airtight portfolios for SFDR, firms must have entity level granularity, to accurately map multiple ESG ratings at issuer level, through to underlying securities, and enable granular ESG analysis at the portfolio level.
What we are hearing in the market, is that only certain ESG vendors provide identifiers at various levels of the corporate hierarchy. To address this, we believe it is critical for firms to look for vendor support where they can demonstrate instrument mastering expertise and can deliver legal entity hierarchies. And they need this fast. For article 6 funds, the tide is moving, and investors will demand greater accountability within client reporting too.
Opportunities to drive growth
Many asset managers are still in ‘firefighting’ mode when it comes to ESG data management and meeting regulation requirements. They have yet to tap into its commercial opportunities such as proprietary insights, launching new products and driving sustainable growth. This will require achieving consistent ESG data quality in a scalable manner.
But some managers already have plans in place to achieve greater auditability and transparency of data, as well as the use of data insights across non-ESG portfolios. This could be applying their learnings about sourcing and use of ESG data across their overall operations to understand where and how they can increase data quality and consistency.
It could involve reviewing their data operating model to deliver greater efficiencies, safeguard against green washing, reduce costs, meet ESG disclosure requirements and client demand while developing a competitive edge.
Having a SaaS-based Modern Financial Data Stack gives firms the power not only to fire-fight regulations and manage reputation issues, but also to take advantage of new market opportunities, notably launching new products to meet client demand and drive revenue.
For the majority of firms, the current priority is understanding where and how they can optimise their data operating model to increase data quality and consistency. This includes reviewing current processes to deliver greater efficiencies, meet ESG disclosure requirements, and client demands as painlessly as possible.
Reproducibility and auditability matter, not only for the regulatory response but also to seek market opportunities and increase market share. From our engagement we have seen some frontrunning firms appreciating the need for greater auditability and transparency of data.
This includes exploring the role compliance training can play, to help stakeholders produce ESG data of the same quality and at the same cadence as financial data, leveraging financial compliance standards.
This would be hugely beneficial not only for regulatory reporting but to lay the groundwork to glean greater insights from ESG data, to support new strategies and products.
It’s clear that we are still in the formative phase of regulation for ESG. It is likely that standards will be pushed to boards such as the ICAEW or FASB. In this respect the collection of data and its application not only to reporting but to core financial calculations is on the horizon. Having an operating model and technical infrastructure that can adapt quickly will be paramount to efficiently deliver on what will be a changing set of requirements for some time.
With resilience forming a key focus for both firms and regulators, the questions COOs must ask themselves are whether their operating model can stand the test of time in an evolving regulatory landscape? Can it cope with new and changing data sets and reporting requirements, and still achieve the accuracy needed? And can it deliver the analytics and insights required, to create new and appealing products while keeping total cost of ownership low? Having the right data stack and operating model will be critical to achieving all of this. As will having the ability to drill down and map the global parent to each underlying security – to achieve greater alignment between portfolios, investment strategies, and ultimately sustainable finance principles.
This will help firms proactively manage risk in the long run: from combating greenwashing claims, to evolving regulatory requirements, without the need for burdensome change management projects.
More than anything, being able to confidently access timely and accurate data through a future-fit, SaaS data foundation is as much a necessity for ESG investment operations, as it is for enriching client reporting and due diligence. The latter of which, can help investors cut through the confusion and form a significant differentiator for client attraction.
Conclusion
In this guide, we have set out the challenges that asset managers face such as data consistency and granularity, which are not limited to ESG. But firms are under pressure from ESG regulatory forces and investors are demanding more. Best practice can be found in sourcing ‘fit for purpose’ data, deriving value from multiple, disparate ESG data sets and utilising LEIs to understanding sustainability risks. This, combined with timely consolidation and translation, can efficiently manage climate-related risk, meet evolving ESG regulations and respond to investors’ transparency needs.
It is time to move away from rigid and siloed legacy data stacks and spreadsheets towards a flexible data model. A cloud-native Modern Financial Data Stack not only enables firms to fire- fight regulations and reduce reputation risk but also give them the bandwidth to launch new products to meet client demand.
Having a flexible model will enable firms to adapt to changes in ESG regulation further down the line and seek out opportunities to deliver meaningful change to the planet and returns to stakeholders.
About FINBOURNE
FINBOURNE’s solutions deliver an interconnected network of functionality and data that enables the investment community to better serve clients in a constantly evolving market. Its investment management solutions and cloud-native data management platform ensure that investment and operations teams can increase revenue, reduce costs, and better manage risk across the investment life cycle.
About KPMG
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