By Hugh Leask | Editor, Hedgweek , Richard Peterson | Director - Product Head, Credit Data and Analytics, FIS , Paul Landi | Senior Solutions Consultant, FIS
With investors keen to capitalise on the burgeoning opportunities in ESG, the ability to quantify and assess risk in portfolios and positions has now become key for industry players – whether it’s screening the investable universe with negative and positive ESG metrics, conducting bottom-up analyses of companies, understanding what indicators are material to a particular company or sector, or engaging with companies on policies.
But a lack of industry standardization in the way such factors are identified, measured and quantified is pushing market participants to turn to AI and machine learning solutions to help them contend with the increasingly complex array of datasets and disclosure demands.
“The smart money is beginning to realize the desperate need for more highly-evolved data and enhanced technology to really take advantage of these disparate datasets,” says Richard Peterson (pictured), director and product head of data and analytics at FIS, and a member of the steering committee for integrating ESG initiatives at FIS.
A greater degree of climate awareness among institutional asset owners, and an increased drive among national regulators and global bodies, has powered the adoption of new investment benchmarks, goals and targets, according to Paul Sinthunont, senior analyst at financial services technology and regulatory advisory firm Aite-Novarica.
For some, this may mean a new portfolio benchmark that investors judge their performance against. Others, meanwhile, look to the UN’s Sustainable Development initiative to meet their targets.
“Many firms start by taking a top-down approach to the ESG integration, looking at things from an asset allocation level,” says Sinthunont, who specializes in buyside trends in technology, operations and regulation, with a focus on portfolio management and risk management, performance attribution, ESG investing, investment accounting, and client reporting.
He also points to post-investment risk management processes which aim to better understand a position’s exposure to climate risk (both physical and transitional risk), and reputation risk, among other things.
“You then look at what that entails in terms of disclosure requirements – you have corporate disclosures for public companies, you may have business-level disclosures that aresector-specific disclosures (e.g., energy sector) and you may also have product level disclosures that are required for mutual fund structures such as European UCITS funds. .”
Meanwhile, the global regulatory landscape has seen a sharp upwards trend in policy interventions over the past two decades, with the European Union leading the way the Non-Financial Reporting Directive, EU Taxonomy and most recently the Sustainable Finance Disclosure Regulation (SFDR).
Across the Atlantic, the US Securities and Exchange Commission announced in 2021 the creation of the ESG Task Force in the Enforcement Division, while the Japan Exchange Group published the Practical Handbook for voluntary corporate ESG disclosures in 2020. Brazil and China have also launched separate initiatives involving sustainable economies and green bond projects in recent years, Sinthunont notes.
But it’s not just national governments and regulators who are shaping the standards and policy landscape. NGOs and non-profits – such as the IFRS Foundation, GRI, TCFD and the Value Foundation – as well as a range of exchanges, industry trade associations, and other assorted third parties – such as credit rating agencies – are also incorporating ESG into the mix.
“All of this provides for a very diverse landscape in terms of what to report on and which frameworks to report,” Sinthunont adds.
Against that backdrop, FIS is utilizing cutting edge technology like AI and machine learning algorithms to offer what it calls a more “holistic” experience for clients, integrating the increasingly elaborate and disparate datasets and information with more traditional approaches to investment analysis and reporting.
The aim, ultimately, is to offer clients as much flexibility and customization as possible in their analyses.
“Being able to take that data and really make sense of it is the biggest challenge,” says Paul Landi, director, product strategy, digital investment analytics at FIS. He acknowledges the need for asset managers to provide additional commentary and explanations, beyond a simple quantitative check-box, in order to “get to that second layer of the onion” in terms of information.
Building on this point, Peterson - who has more than 20 years’ experience in the financial services industry, including roles in ABS sales, origination and strategy - says large parts of the investment management industry are “still in the wild west” when it comes to ESG analysis of companies and the level of data provision.
He points to a growing number of questions from clients surrounding the ability to better understand data, particularly on the social and governance elements of ESG, which Peterson believes are often trickier to quantify and benchmark.
“If you asked everyone what their definition of ESG, or any of the factors are within ESG, I think you’d have hundreds of responses. That's really the issue,” he observes.
“There are different types of datasets, there are all these different providers out there - how do you really utilize all of this data in conjunction with your traditional financial analysis? We need to get to a more transparent and standardized approach to evaluating ESG data.”
Peterson believes the industry will get to that point, and it will be technology which will ultimately enable data to become more transparent and more standardized, a side-effect of which will mean greenwashing – an ongoing challenge – will become less prevalent.
“AI, machine learning, all of these types of initiatives are going to help cut through the data, and cut through the noise, and really deliver what is relevant for your investment analysis,” he adds.