ESG issues have always been important in the financial and corporate landscape, but over the past 18 months or so it’s been taken to a new level. The COVID-19 pandemic has hugely increased the focus on the health and sustainability of our planet, together with pressing questions of social equality and cohesion. Climate change concerns have escalated, the Black Lives Matter movement has become a powerful force, geopolitical risks and tensions continue to create volatility — the list goes on.
Along with a continuing focus on governance, ethics and compliance, all of these factors have combined and coincided to propel ESG to new levels of significance. And they are being particularly championed by the Millennial generation — that is about to inherit probably the biggest transfer of wealth ever seen over the next two decades as baby boomers pass the baton. Millennials want to know not just how much return an investment will make, but how it will make that return and at what cost to people, planet or communities.
This means that investments have to be repurposed towards something more meaningful than straight returns. Increasingly, institutional as well as individual investors are throwing their weight behind this — becoming the main drivers for change in financial markets.
The ESG data challenge
The investor sentiment shift towards conscientious society, a greener planet and improved governance practices is driving all financial institutions, whether big or small, traditional or fintech, to be fully cognizant and in control of the ESG profile of their investment and underwriting positions, and embed ESG considerations into their reporting and risk management frameworks.
But doing so is not straightforward. Whereas financial reporting is standardized and in familiar formats, corporate reporting around ESG dimensions is anything but. Companies are not obliged to report most ESG-related information; therefore, practice is fragmented and disparate. There are few common templates, meaning that companies will publish different information in different ways. There are a multitude of surveys in the market, but these only offer limited help: companies participate in some surveys but not others, and might answer slightly differently each time.
Much ESG information is also self-reported. Inevitably, this opens the possibility of ‘greenwashing’. Understandably enough, corporates are keen to paint themselves in the best light possible.
Cutting through the noise, obtaining relevant information quickly, and analyzing it effectively, is therefore much harder than perhaps it needs to be. We have also seen the opening of something of a two-speed market. Big global institutions across banking, fund management and insurance have been highly active in either building their own in-house data analytical capabilities for ESG such as State Street, Goldman Sachs, JPMorgan or acquiring one of the new breed of fintech data aggregators — or a combination of both. M&A in this space has been prolific for example, Blackrock recently bought Baringa’s Climate Change Scenario Model and integrate it into its Aladdin risk management framework1, while HSBC Asset Management bought a stake in Radiant ESG2, a US-based ESG asset management start up.
This means that the big institutions are able to track data signals across multiple sources and decipher them almost instantly. They can react to breaking news and adjust their positions in response.
In recent years even large credit rating agencies and market data providers went on a buying/acquiring spree to remain competitive and cater to the ESG and sustainability related demand.
KPMG’s ESG IQ platform
For others, it remains much more difficult especially when resources are constrained across multiple competing priorities. However, there are tools available to help — such as KPMG’s ESG IQ platform.
ESG IQ is an analytics platform developed by KPMG’s Lighthouse data scientists and engineers, in conjunction with some of the largest asset managers and Google. ESG IQ enables clients to select and pool both structured ESG reference data from multiple providers and unstructured data from a wide range of sources, including news reports, social media posts, blogs, NGO reports, research reports, and pages across the web. The platform can even pull out ESG data from ‘dark data pools’, such as legal documents, trade confirms (depending on the asset class), and other sources using advanced Natural Language Processing (NLP).
We believe it is unique in that other score providers can only score individual entities or companies. But the ESG IQ platform can go beyond this, scoring whole investment funds, sovereign wealth funds, bonds, equities, structured bonds (tranches of RMBS/CMBS/ABS etc.) or an illiquid product such as loans or mortgage-backed securities.
The tool also enables clients to unravel the score, providing the root-cause-analysis of what factors and issues have led to the rating. The client can then determine for themselves the materiality of the findings — recognizing that different factors may have a greater or lesser weight depending on the client’s guiding ESG strategy. For example, we have used the tool with one of the biggest US based Asset Managers to help their treasury team unravel their asset-backed portfolio with structured bonds, sovereigns, agencies, municipals, government bonds and more.
A critical capability
ESG data is rapidly becoming a mini industry of its own, with some 200 data providers and a plethora of fintech start-ups already operating in the market, globally. This in itself reflects the importance now attached to ESG. It has well and truly moved into the mainstream of financial services and is on its way to redefine capital markets as we know it, into a more transparent and conscientious one.
For any financial institution, ESG related decision making is a critical capability. Ensuring you have the tools to do this is not a nice to have but an increasingly essential requirement.
You can’t manage what you can’t measure — so robust ESG data is key. There’s a lot of noise in the market and of course some selective ‘greenwashing’ too. It’s becoming a pre-requisite to have access to AI-powered tools that ingest structured and unstructured data across multiple sources and give fast and reliable analysis on which to base decisions. You need tools you can trust.
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