Martijn Groot is VP Marketing and Strategy at Alveo
The concept of making responsible investments according to ESG criteria has been around for decades. In the past, however, this was a niche area and was generally the focus of highly specialist companies, often known as impact, or green investors.
They developed their own data collection process in-house and frequently built their own, what we would now call, environmental, social and governance (ESG) data hub to supply their analysts and portfolio managers. This data was then used as the basis for asset allocation, helping to support firms in the screening of companies and selecting the ones that aligned with their investment philosophy.
When other firms started to collect data to identify ESG risks and growth opportunities, they too treated it as a separate silo or bucket. The focus was on homegrown data management, with firms evolving the data over time into their own in-house ESG hub.
However, as we have moved into 2022 and the deadline for key ESG related regulations such as the Sustainable Finance Disclosure Regulation draws nearer, firms will need to do more to fully integrate ESG data across the business and firms will increasingly need to integrate this information into the whole investment management process: from research and asset allocation, to portfolio monitoring, to client and regulatory reporting.
Scoping out the use cases
The number of use cases for ESG data is growing rapidly. The need to disclose data to meet regulatory reporting requirements is a major driver for buy side firms like investment managers or asset managers and owners, as SFDR approaches.
Firms are obligated to report on a number of criteria. SFDR prescribes the reporting on 18 mandatory PAI (Principal Adverse Impact) Indicators for corporates, real estate investments and sovereigns. Effectively, any firm that sells or distributes investment products into the European Union has to do that.
Paradoxically, the disclosure requirements for corporates themselves lag behind the disclosure requirements of their investors. This has caused the need to estimate information or rely on third party expert opinion to fill the gaps in the data that portfolio managers and analysts need to support their decision-making around new product development, for instance.
Investors have also acquired more appetite for making investment choices based on green criteria, so fund managers and wealth managers too need relevant data to help develop client reports for those investors.
Those are among the key requirements for asset managers and asset owners but there is also a growing need for ESG-data on the banking or sell-side of financial services. ESG data, for example, is much needed to support customer onboarding and, in particular Know Your Client (KYC) processes.
They need to effectively cover the E of ESG – in other words the environmental aspect […] data will need to be gathered around specific companies’ carbon emissions, pollution footprint, water usage and biodiversity
In core banking and in corporate lending, in particular, banks will, in the future, have to report on the composition of their loan book: what firms are they lending money to, for example, and what are the main business activities of those firms? The European Union has developed the EU Taxonomy which provides a classification of business activities.
So, in the future, if a company signs up to get a bank loan, as part of the screening criteria, it will be asked to disclose what kinds of business activities it is involved in and what kinds of sustainability criteria it has in place. Banks may then also be incentivised to give a cut on the interest rate on loans made to more sustainable businesses.
Banks and other sell-side financial services firms will also frequently screen their suppliers, as part of a process known as KY3P (Know Your Third Party) or KYC (Know Your Supplier). They like to know in detail who they are doing business with, so they can then report on that in their annual report to shareholders.
Another key use case for banks is climate stress testing. Banks have to stress test the products they hold in their trading book for their own investment against certain climate scenarios – two degrees temperature change by 2050, for instance, to give one example.
ESG data also has a role to play in the way banks manage their mortgage book. Banks are increasingly looking for geospatial data, for example to work out the flood risk of the properties they finance. Are they next to the ocean, for example? Are they in a flood plain of a river? A lot more attention is being paid today to the banking book and trading book and, more generally, to retail and residential commercial real estate funding.
As part of this process, both sell-side and buy-side financial services companies will need to integrate ESG data with data from the more traditional pricing and reference data providers.
That will give them a composite view, incorporating not just the prices of instruments and the terms and conditions but also the ESG characteristics – all in a single place. Firms will also need to put the right data quality metrics and governance on top of all this in terms of onboarding new data sets; requesting new metrics and new screening criteria.
If they get all this right, firms will usher in the coming of age of the ESG data function as it transforms from a homegrown cottage industry into fully-integrated core business function. ESG considerations, like all of sound decision making, requires good quality data. The process of ESG data collection, vetting and integration will mature and will be integrated with financial, regulatory and client reporting functions.
Decisions taken in business processes using ESG criteria will be documented and tracked. Firms will make different trade-offs and use different ‘shades of green’ but all will have to communicate, track and report.
Like all of data management, consistent and high-quality information on ESG now needs to percolate across the whole organisation and be put on a firmer footing. It needs to integrate with all the different data sets to provide a composite picture.
That can then become a key source of intelligence, not just for the front office but also for multiple business functions, including supply, client reporting, regulatory reporting and portfolio construction.
Challenges to negotiate
Today, companies are maturing fast in their approach to ESG data management. But there are nevertheless barriers along the way. One of the big challenges is data availability. There are different types data providers on the scene today. These include firms that aggregate third party disclosures and bundle them into their enterprise data offerings.
Another source of ESG data that financial services firms need to tap into, given the gaps in corporate disclosures, comes from rating providers, who provide their expert judgment as to how green firms are or how well they are achieving against a broader range of ESG criteria.
However, there are challenges here also. It is not always transparent as to how these providers have arrived at their ratings, what input data and what weights they have used to arrive at a single rating, so like-for-like comparisons are not easy as ratings are subjective.
The third area of data that financial services firms need to access relates to expert opinion, often generated by third parties. CDP, the not-for-profit charity that estimates carbon emissions, is a case in point. The fourth key element is around sentiment data: how a company is perceived in the market.
Often this includes an assessment of how a given firm is covered in the traditional news media and also on the public Internet and how it is regarded in social media discussions. Typically, this is more useful in helping to form a view over the short-term because opinions in this area inevitably change quickly.
Data quality is often a further challenge. Many data sets are incomplete or suffer from spotty coverage. Attaining a complete data set is in itself challenging. And because not every rating provider provides information on how they arrive at their rating, it is often difficult to compare rating A to rating B, for example, and then aggregate it at portfolio level.
Another important challenge is workflow integration. The biggest issue here often is how to anchor the ESG data in a range of different business processes in order to put users on a common footing.
First of all, financial services firms need a common data set on the ESG characteristics of all the companies they deal with, whether they invest in them, whether they lend to them, whether they supply them with goods and services. They need a broad range of data.
They need to effectively cover the E of ESG – in other words the environmental aspect. That means data will need to be gathered around specific companies’ carbon emissions, pollution footprint, water usage and biodiversity, for example.
Equally, this data set should include content relevant to the S of ESG (the social element of the term). That might cover areas like the gender pay gap and human rights and so on. Finally, the G covers areas like board composition and general governance.
Firms also need workflow integration in the technical sense meaning the ability to mould data into different shapes so it can be fed into different applications that may have their own data models their own technical standards definitions and data dictionaries.
It then needs to be cross-referenced, supplied, sourced and published via streaming data. Businesses need to put all of this kind of wiring in place as a process that comes after building the common composite data set.
Then, of course, over and above the common footing of ESG data, firms will also need for certain use cases, specific criteria that they use when it comes to what is sometimes called the ‘secret sauce’ around front office asset allocation, where each individual organisation may well build their own metrics, ratings and criteria on whether or not they invest in something. But that is built on top of the common foundation.
It is effectively a different kind of data management. There is the data management for control, where firms need data quality, data lineage, proper quality assurance carried out on the data before they hand it over. That is typically used for business as usual (BAU) applications and operations.
It can be contrasted with data management for insight where firms are looking to add value and build up their own intelligence and metrics to support portfolio managers, quants and analysts that work in the front office.
Finding a way forward
So, ESG data is increasingly in demand by financial services companies, both buy side and sell side. However, accessing it, ensuring it is of good quality, comparable with other ESG data sets and well-integrated within existing workflows is challenging and difficult.
Fortunately, data management solutions are now coming on stream that enable companies to start providing a process of collecting and aggregating ESG data, comparing it for quality, proofing it and enabling users to fill in the blanks through business rules and their own metrics.
Firms will need to cross-reference, match and combine the data, as well as assimilating it with traditional data on companies and their financial products. The traditional prices and security terms and conditions of financial services providers helps build a composite picture from those sources. The wider variation can be that firms can choose how they interpolate, or proxy, missing data fields or build their own metrics on top.
Then there is the distribution side. There are now various ways of distributing data via streaming and different files to make sure that the last mile integration can be done quickly and effectively and that they can rapidly onboard new consuming applications.
Again, technology is increasingly available to enable firms to do all this. This kind of capability can already been offered on site hosted as application management, or as a data as a service solution in the cloud. In other words, the challenge of ESG data integration can now be met by financial services businesses. They can increasingly move to a ‘deeper shade of green’, safe in the knowledge that the technology is available to support them in that critically important journey.