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The ESG Interview: Following the (Data) Science

Paul Fahey, Head of Investment Data Science at Northern Trust, says emerging technologies will help investors to integrate ESG factors and to track the impact of their decisions.

A dearth of data on sustainability continues to make life difficult for ESG investors. Add to this the wide inconsistencies in reporting and a lack of standardised metrics and perfect storm conditions are created, making life increasingly challenging for investors under pressure to build a responsible portfolio.

Fifty-nine percent of investors with an estimated US$11.3 trillion in assets under management who responded to a recent BNP Paribas survey agreed that “data remains the primary barrier to integrating ESG” into their investment strategies. While this is an improvement on the two-thirds who reported this issue in 2019, inconsistent data sets continue to present a significant impediment to progress.

Paul Fahey, Head of Investment Data Science at Northern Trust, says: “If there are inconsistencies data sets, then straight away investors will run into problems in analysing and reporting on ESG. Comparability of data is a cornerstone of successful ESG investment.”

Spreadsheet tyranny

The Chicago-based firm provides asset servicing services to investment managers, pension funds and other institutional investors globally. It is attempting to bring some harmony to the cacophony created by the multitude of sources from which investors source data.

A key challenge is that investors’ urgent search for data inputs to support green investments is taking place on what one might call a brownfield site.

Fahey argues that many asset managers are sorely lacking data science and behavioural analytics that would help make more informed investment decisions, sustainable or otherwise. He points to analysts’ and portfolio managers’ persistent use of data sources housed in diverse and incompatible formats in investment decision making. According to Fahey, these sources are too qualitative in nature and ultimately inefficient.

“Today, managers are trying to pull information together from a mosaic of Word docs, Excel spreadsheets, Evernote and email,” he says. “They need to be able to analyse all this data in a meaningful way.”

A Northern Trust survey of 300 hedge fund and long-only managers globally published this July found that 59% claim to be using five to eight data sources of ESG investment data.

Fahey says: “I think that range is soft and probably does not fully capture the full extent.”

He expects asset managers to rely on even more datasets in future, asserting: “If we did the same survey in 18 months, it would not surprise me if we saw those datasets closer to more than 10.”

Northern Trust says 98% of managers want to incorporate data science into their investment decision making, a move Fahey says will allow them to focus their skills on making better ESG choices.

“We need to remove the tyranny of spreadsheets so you can do all of the work the risk management portfolio construction in a single place,” Fahey says.

Data science enables portfolio managers and analysts to sort through huge amounts of information quickly and efficiently. The technology can find patterns or even alert managers to danger signs, and makes the whole investment process – be that ESG-based or not – easier to measure, analyse and report.

While appropriate application of data science techniques and principles can yield time savings and insights, colleagues of Fahey see ESG investing’s data challenges within a wider context.

In a recent blog, Melanie Pickett, Northern Trust’s Head of Front Office Solutions, and Lauren Burley, Vice President, Front Office Solutions Data Strategy, made the case for a four-point plan for effective ESG-based decision making across all investment operations.

“Portfolio management, analysis, due diligence and capital allocation in the front office and regulatory reporting requirements in the middle and back offices will all have to evolve to take on an ESG investment mandate,” they noted.

Asset class opacity

While Northern Trust has skin in the data science game, there are other organisations that also find that institutional investors need access to consolidated, high quality independently verified data.

Recent research from fellow custodian CACEIS, which is the asset servicing banking group of Crédit Agricole and Santander, found 69% of pensions schemes need access to data to measure climate risks from their schemes’ investments and over half (57%) say they struggle with the lack of consistent data and inability to make comparisons, while 72% require better reporting of ESG and carbon data.

The survey also points to an absence of “consolidated, high-quality look-through data” leaving pension schemes overly reliant on consultants or asset managers to address climate and other ESG risks.

Part of the challenge lies in the available data across different assets classes; the private markets are renowned for opacity in general, but the issue is particularly acute when it comes to ESG metrics.

Custodians are, Fahey argues, well placed to get a ‘look-through’ across all asset classes but adds that much of the improved transparency in all asset classes is a result of investor pressure.

“It is investors themselves and asset allocators that as a collective group are putting more pressure on managers to define what their process is, what they are delivering, from an ESG standpoint, and how are they delivering value,” he says.

A key area of investment operations about which asset owners have made their priorities known is securities lending. Increasingly, they have realised the need to ensure stock is not out on loan, when needed for voting purposes, and that collateral is not accepted if it falls beyond ESG exclusion criteria.

Investors are reviewing their policies and working with agent lenders to improve information flows. According to Fahey, improved data science can also go some way to helping asset owners avoid conflicts of interest between their securities lending activities and their wider ESG strategy.

Regulatory divergence

The need for better sustainability data is made even more acute by the regulatory demands on investors for additional reporting on the ESG-related risks, much of which differs across jurisdictions.

Fahey points to Europe’s Sustainable Finance Disclosure Regulation (SFDR), the US Securities and Exchange Commission’s planned climate change disclosure requirements and the recently announced Sustainable Disclosure Requirements (SDRs) in the UK.

However, Fahey says that while divergence in global legislation “provides challenges”, it is the job of global asset servicing firms to covers multiple reporting requirements.

“We have flexibility and the ability to pivot the data analysis. Clients will not have to do a complete reversal when SDRs come in. We have the ability to flex what we are doing for SFDR for what we think the UK’s SDRs will look like,” he says.

However, Fahey is keen to stress that regulations should not be the driver for investors when assessing ESG credentials across their portfolios.

He says: “If anyone takes regulation as a handbook then they are probably not going to get to where they need to.”

Fahey expects data science to become more important to investors and asset managers in the future, particularly as the focus switches from simple performance to how those returns were achieved within an ESG context. This will become even more important as investors look to discern positive intent and impact, rather than risk avoidance.

“Investors benefit from data science,” he says, “because they have a manager with a digitised process which can be played back to the investors, who are becoming more critical about why that manager has been successful. They want to know how the manager achieved their returns, and whether that was based on skill and ESG knowledge.”

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