SIX, a global data aggregator and operator of exchanges in its native Switzerland, as well as in Spain, has released a flurry of new data products since January, including a suite of ESG tools and two global equities index families that herald a plan to become a one-stop-shop for ETFs.

According to Leslie, the frenetic pace of partnerships, product releases and enhancements this year is just the tip of the iceberg. The Zurich-based, bank-owned organisation has more to come, all built around a trove of data and data capabilities it has built up over more than 90 years of operations.

At heart, it remains a global pricing reference data provider – that’s the “base data” that SIX “is built on”, says Leslie. But the company is putting in place ambitious plans to leverage that core data competency to meet the increasingly complex demands and use cases of financial institutions.

“I believe that the fundamental data set – having really good-quality reference data and pricing data – allows us to create new value-added services and insights to our clients, and that remains the same whether we’re talking about GenAI or good old fashioned master reference,” Leslie tells Data Management Insight from SIX’s offices in London. “Unless you’ve got those basics you can’t really make sensible decisions, let alone produce reliable analytics.”

Expansion Plans

Leslie says SIX sees its USP as the ability to leverage that core data product to create applications for a multiplicity of use cases. Already it is using its fundamental datasets as the backbone of regulatory, corporate actions, tax, sanctions and ESG products for its banking clients.

A slew of recent acquisitions, investments and partnerships have been similarly guided by SIX’s programme of creating services that can tap into its core offering. The purchase of ULTUMUS in 2021 and the deepening of a long-standing association with BITA earlier this year were part of a plan to forge the company’s ETF-servicing business, each deal enhancing SIX’s indexing capabilities.

In ESG too, it has been aggressively striking deals to help burnish a slate of new sustainability offerings. Products unveiled in the past year by ESG product strategy and management head Martina MacPherson all benefit from supply deals struck with vendors including Sustainalytics, MSCI, Inrate and the CDP, as well as new partnerships with companies including Greenomy. Among the ESG products launched recently is an SME assessment tool, which MacPherson said will bring thousands of smaller companies into the ESG data ecosystem, into which banks and investors might otherwise have had no visibility.

Working Data

SIX’s ESG provisions illustrate what Leslie describes as the company’s dedication to making data work for companies.

“Organisations need to figure out how they’re going to incorporate data and how they’re going to make it relevant,” she says. “Well, the only way you can make it relevant is if it’s got something to hook on to, and that’s where you get back to those fundamental data sets.”

Leslie explains that one of the driving forces behind the company’s vigorous expansion plans is the changing demands for data among banks. No longer can any part of the industry rely on end-of-day pricing data, or monthly and quarterly reports. Ditto for risk managers and compliance teams.

The consequence has been a shift in the workloads of the front-, middle- and back-offices. No longer is research the premise of middle-office teams, Leslie offers as an example; the front office needs those insights quicker and so it has made sense for banks to embed data access and functionality within asset managers own analytical workflows.

“Asset managers see that the speed of data is increasing all the time and so the buy side, which was perhaps in the past much more built around end-of-day or less immediate requirements, is moving much more into real-time and intraday needs,” she says. “That requires, therefore, real-time market data, and that is expected by regulators, it’s expected by customers, and its therefore expected by market participants.”

AI Challenge

Jokingly, Leslie likens data operations to raising a child: it needs constant attention and feeding to grow and thrive. The simile is just as true for banks’ data management needs too; they are constantly changing and growing, influenced by internal needs and external innovations. That’s exemplified by the race to integrate artificial intelligence (AI) into processes and workflows.

Recent SIX research found that more than nine out of 10 asset managers expect to be using AI within the next three years and that half already do. Driven by its own clients’ need to understand what AI will mean to them, SIX has begun looking at how it can enhance its products with the various forms of AI available.

It has taken a structured approach to the programme and is looking at where AI can help clients improve efficiency and productivity; examining how it can improve customer experience and support; and, testing how it can be incorporated into products. For the latter, SIX is experimenting with off-the-shelf GenAI technology to identify aberrations in trading patterns within a market abuse solution.

On this subject, too, Leslie stresses that SIX can only think about such an evolution because it is confident that it has a solid foundational data offering.

“Our role is to make sure that we’re providing data that is fit for purpose and enables our clients to do business in a competitive way,” she says. “So that will include, as it always has, providing trusted, reliable data that the client knows is fit for purpose and on which they can make decisions. And that’s as true if it’s going to an AI model as if it’s going into a client digital wealth platform or portfolio reporting or risk solution.”

Values Align

Leslie took up her latest role at SIX in 2020 and also is a member of the board for the SIX-owned Grupo BME, Spain’s stock exchange, previously holding roles at LSEG and Thomson Reuters.

She is proud to be part of an organisation whose stakeholders are banks – about 120 of them – and not shareholders “trying to race to hit a quarter result”. She feels a very strong alignment with its values, too.

“It’s an organisation whose purpose is to enable the smooth functioning of the economy and has consistency and trust at the very core,” she says. “When half the world is voting this year, this stuff’s important, and when we’re talking about AI, or we’re talking about market failures then the thing that brings trust and progress is the data that sits behind it. To be a trusted provider in this day-and-age is a critical service.”

The post Building Future Growth Around a Foundational Data Core: SIX’s Marion Leslie appeared first on A-Team.

As part of the arrangement, ratings provider Moody’s will gain access to MSCI’s data and models, which will eventually replace its own content in services offered to banking, insurance and corporate clients. That will include MSCI’s ESG ratings and scores. In return, MSCI will be able to use Moody’s Orbis database, which contains information on more than half a billion private companies.

The move has been described by the companies as groundbreaking and credit ratings specialist at Aston University, in the UK, Daniel Cash said it was inevitable that such “convergence” would happen among big players in the ESG rating and credit rating sectors.

“This is the first really important move in the related sectors together. It is an important move for the ESG rating sector specifically,” Cash told Data Management Insight, noting that Moody’s indication that it would step away from ESG ratings would increase the “duopoly in the ESG rating sector between MSCI and S&P”.

More Developments

The companies said they would also explore ways for MSCI to benefit from Moody’s credit rating scoring models for private companies: “Stay tuned for more updates,” said MSCI chairman and chief executive Henry Fernandez.

Both companies said the strategic partnership would “bring greater transparency on ESG and sustainability to markets and power better decisions”.

Moody’s is bolstering its ESG data capabilities at a time when regulators are requiring risk assessment providers to tighten the quality of their offerings and potentially to open their methodologies to public scrutiny. This comes amid accusations that opacity within ESG ratings is fuelling greenwashing.

The company already provides two ESG scores products but said the deal would not have an impact on Moody’s Ratings, its credit ratings business.

For MSCI, the deal will offer clients a portal into the increasingly important private equities and credits markets. ESG data on these companies has become a key target of financial institutions as they have diversified their holdings into alternative assets amid fluctuations in global capital markets. According to recent estimates, about a third of all institutional money is now tied into private markets.

The implementation of the EU’s Corporate Sustainability Reporting Directive (CSRD) is expected to raise the profile of smaller companies further, providing greater transparency into their ESG performances.

Earlier this year MSCI unveiled its MSCI Private Company Data Connect platform that brings together sustainability data from unlisted companies for use by private market funds and investors.

Market Implications

Cash, who predicted coalescence within the ESG ratings market in his 2021 book “Sustainability Rating Agencies vs Credit Rating Agencies: The Battle to Serve the Mainstream Investor”, said the timing of the Moody’s and MSCI announcement was significant.

“It is not coincidental that this move takes place as the EU are becoming the first major market for the agencies to actively regulate the ESG rating space,” said Cash, who is also ESG ratings and regulations lead at global law firm Ben McQhuae.

“Regulators – particularly in the EU – will need to be watching these developments very carefully because the impact and effect of duopoly on this nascent field is a significant ‘unknown’, which could have a dramatic effect down the line. As both S&P and MSCI are major providers of investment indices, this duopolistic move could have far-reaching effects.”

The post Moody’s-MSCI Partnership Seen Impacting ESG Ratings Sector appeared first on A-Team.

For that reason, getting ESG data management right has become an important goal of institutions as customers and regulators demand more effective integration of sustainability themes in their investment, risk and reporting processes.

Four Pillars

During the “ESG Data Sourcing and Management to Meet Your ESG Strategy, Objectives and Timeline” event, Clarity AI board director Ángel Agudo argued that there are four pillars to implementing a “perfect” ESG data management strategy.

Firstly, Agudo said firms must be able to onboard multiple data sources quickly and combine them effectively with market data. The complexity of ESG means that it’s unlikely that one provider will be able to offer all the datasets any one institution will need. Having the capabilities to “mix” it with financial data would be crucial, Agudo said.

That was a point echoed by Neuberger Berman head of ESG data Aria Goudarzi, who said that being able to map that information to data on asset-issuing entities was the only way to make sense of ESG data. That, he said, would require standardisation of entity identifiers; at the moment, however, data providers often use their own proprietary classification systems.

Mapping Data

The difficulties of mapping ESG across different datasets had prompted many firms to build their own ratings and scoring models, said Alveo chief product officer Neil Sandle. While it is an oft-stated criticism of ESG data, Sandle made no apologies for repeating that making information comparable – the key aim of data matching – was the very essence of getting ESG data right; without that, investors would not be able to make well-informed risk and allocation decisions.

On the subject of data sourcing, Goudarzi added that providers needed to be chosen carefully, one at a time, so that institutions could avoid “drowning” their systems in data that they are unable to use. Focusing on what is really needed is the most important element in the data acquisition process, he said. On top of that, having the right architecture to ingest and process those datasets would be fundamental in building a solid data management setup. Without those foundations, he said, the “whole building will crumble before you”.

Agudo’s second recommendation for a robust ESG data management setup is to ensure the quality of that data. As long as there has been ESG data, there have been issues with its quality; datasets are often incomplete, they lack standardisation and the data’s unstructured nature makes integration more challenging.

Nuanced Approach

While he agreed in principle with an argument put forward by Alveo’s Sandle that ESG data should be treated in the same way as all other data within institutions’ systems, Agudo said it wasn’t always possible. ESG data is nuanced, he said, and because of that required additional processing steps. Chief among those, he said, is the need to accurately identify and consider the methodology used by the data’s provider in putting together the dataset.

Securing that data was Agudo’s third management pillar. With so many data sources being engaged, there is a risk that confidentiality provisions could be breached as institutions slice, dice and combine different datasets.

And finally, he said, having the analytical capabilities to “transform that data into information” is important in helping firms make better decisions.

The goal of good data management was to ensure ESG data worked for an institution. The benefits of getting it right, the panel said, were better risk-management models, better access to markets, greater workflow efficiencies and the avoidance of legal actions and reputational damage.

Most importantly, argued Neuberger Berman’s Goudarzi, was the knowledge that being able to make allocation decisions backed by properly managed data would ensure that capital was being directed to the securities and companies that would be best placed to address the climate and social issues facing the world.

The post Webinar Review: ESG Data Sourcing and Management to Meet Your ESG Strategy, Objectives and Timeline appeared first on A-Team.

The country’s first such framework, created by the Financial Conduct Authority (FCA), is aimed at preventing greenwashing and fostering trust in British sustainability markets. It’s designed to protect the interests of investors by enshrining strict rules on how financial products can be advertising, marketed and labelled, and seeks to ensure such information is “fair, clear, and not misleading”.

Critics, however, have pointed to several potential pitfalls that face institutions as they put processes in place to comply with the new SDR. Because the FCA requires that all claims must be backed by robust and credible data, many of the new challenges are likely to be borne by firms’ data teams.

New Classifications

Under the SDR, asset managers – and later portfolio managers – will be expected to provide greater transparency into the sustainability claims attached to their funds and provide data to demonstrate the ESG performance of the funds’ component companies.

Institutions and companies in scope will be asked to voluntarily categorise their investment products according to the concentration of sustainability-linked assets within them. There are four categories of declining levels of sustainability, ranging from “Sustainability Focus” to “Sustainability Mixed Goals”.

This reflects but differs from the European Union’s Sustainable Finance Disclosure Regulation (SFDR), in which asset managers are compelled to classify their products’ according to a similar range of categories.

Among several other SDR requirements, asset managers will be asked to provide entity- and product-level disclosures and adhere to new fund naming regulations – which forbid the use of descriptions that it terms as “vague”, including “ESG” and “sustainability”.

Effective Strategy

While the SDR has been welcomed as a good first step by campaigners for stronger and more transparent sustainability markets in the UK, its implementation could prove tricky. Among the challenges institutions face is the code’s apparent incompatibility with other similar regulations that firms would face overseas. Some observers have complained that the SDR’s fund sustainability categories don’t easily match the Articles 6, 8 and 9 classifications of the SFDR.

This is where data managers will be of critical importance.

“As with all regulations, financial institutions must ensure they have an effective data management strategy in place from now, enabling systems to efficiently collect and aggregate ESG risk-related data to evidence sustainability claims both internally and externally,” GoldenSource head of ESG, connections and regulatory affairs Volker Lainer told Data Management Insight.

“Now, much higher levels of scrutiny are needed on the underlying methodologies and calculations involved in determining ESG scores. Firms that prioritise this will find themselves in a much stronger position as and when the next stages of the UK’s SDR are implemented.”

Data Doubts

The FCA announced the details of the SDR in November last year. It stressed at the time the importance of data management to compliance with the SDR last year. Firms in scope should “have in place appropriate resources, governance, and organisational arrangements, commensurate with the delivery of the sustainability objective”, it said.

“This includes ensuring there is adequate knowledge and understanding of the product’s assets and that there is a high standard of diligence in the selection of any data or other information used (including when third-party ESG data or ratings providers are used) to inform investment decisions for the product,” it said.

Legal experts questioned whether the UK’s financial industry would be able to fully comply. In a report published in April, international law firm Baker McKenzie asked whether firms would be able to keep up with the data requirements expected of the regulation, and questioned whether the data would even be available.

Careful Consideration

While gaps in ESG data still exist, A-Team Group’s ESG Data and Tech Summit London heard that the data record is improving with many more vendors providing ever granular datasets. Market figures caution, however, that the data imperative of the SDR should still be carefully considered.

“With more specific product labelling rules set to apply to from July, UK firms must brace themselves for these ongoing changes to better navigate the complexity jungle. It is clear data and regulatory content mapping is the key differentiator for service providers here – relying on trusted vendors that can provide quality, accurate data and content in pre-established delivery formats,” said Martina Macpherson, head of ESG product strategy and management in the Financial Information division at SIX.

“This is the only way firms can back up their sustainability credentials, meaning they will be better placed to meet new regulatory requirements and prepare for those to come later this year.”

The post UK’s Debut SDR Rules Raise Data Management Concern appeared first on A-Team.

Institutions are now grappling with how they can take their digital programme further, especially given that the rising demand for data-management expertise has made it difficult to find the talent to put plans into action. The answer lies in outsourcing data management capabilities to a partner that can take a holistic view of an organisation’s data estate and processes, argues S&P Global Market Intelligence.

In a report published by A-Team Group, the company says that a new generation of third-party data provision is called for, one that can offer the technology and the data feeds to accelerate the digitalisation of institutions as well as the know-how to execute their programmes.

“Today, institutions’ needs are more nuanced and sophisticated. In this new marketplace, the service providers that will prosper are those that can offer data management and analytics skills alongside trusted, robust data sources and underpinned by scalable technology,” the report states. “Not only that, but these solutions must also be configurable to the new investment and risk-management use cases.”

Evolving Strategies

The S&P Global Market Intelligence report, entitled “The Evolution of Outsourcing Data Operations for ESG and Private Assets”, argues that established outsourcing strategies have tended to be focused on providing solutions to specific challenges.

The new alternative is a strategy such as that taken by S&P Global Market Intelligence’s cloud-based Data Management as a Service offering. This solution considers institutions’ broader needs – from sourcing through to distribution and monitoring – and, importantly, it is scalable.

“This solution can be seen as a one-stop-shop in which institutions leverage all the opportunities of software, data and third-party competencies via the cloud, to fully extract the value inherent in their data and scale their operations,” the report states.

S&P Global Market Intelligence illustrates how its solution can help in this scenario through the lens of two new use cases that such organisations are increasingly having to tackle: private market investment and integration of ESG data and processes.

The report argues that both domains offer separate novel data challenges that can be solved through the

The report also offers insights into how:

• The new trading environment is placing novel data challenges
• Cloud solutions are helping institutions overcome new data management pressures
• Tight data talent markets are impacting institutions
• Data Management as a Service brings together tools and skills that enable professionals to tailor individual solutions to specific challenges.

Download the full report here.

The post The New Frontier of Outsourced Data Management: S&P Global Market Intelligence Report appeared first on A-Team.

While more data is available to firms, thanks to a combination of new reporting regulations and standardisation of disclosure frameworks, the increasing variety of information needed and the volumes in which it will be delivered means that the pressure on data managers to get this information into their systems is unlikely to abate any time soon.

“We are still in the early stages, and there are still limitations in how companies report their data,” Agudo told Data Management Insight. “So there remains a need to put all that unstructured data together to make it comparable and to complement what’s missing. That means there will be a need to emulate that data through estimates and leverage other sources of information, which could include reports of other organisations, NGO information, news, asset-level data – and more. Ultimately, investors need to make sure the data sourced is fit for purpose.”

Transformation

Agudo will be among a panel of three experts on the “ESG Data Sourcing and Management to Meet your ESG Strategy, Objectives and Timeline”, webinar, which will be held on June 11. The other speakers comprise Aria Goudarzi, SVP and head of ESG data at Neuberger Berman, along with Neil Sandle chief product officer at Alveo.

The sourcing of ESG data has undergone a transformation in  the past few years. The space was initially provisioned by established financial data providers. Their one-stop-shop approach was eventually supplemented by the arrival of innovative providers of ESG-specific datasets and analytics. Clarity AI is among those, offering clients tech-based end-to-end solutions, for more sophisticated use cases and a higher degree of flexibility to swiftly adapt to changing market needs and requirements.

Other relative newcomers offer customised datasets that are focused on specific ESG themes that are becoming more central to institutions’ investment and risk processes, such as nature and biodiversity, human rights and diversity.

The chain of processes required to enable the integration of ESG data into firms’ wider data estate is something that Agudo said needs to be addressed at the sourcing stage, rather than left until it has been ingested into data management systems. But he says the challenge lies in achieving this while also adhering to the firm’s overriding needs-based data management methodology.

“You can manage data in the most standardised way possible and there are many platforms that already offer those capabilities. However, embedding  the methodology into the data management process is the challenging part,” he said. “Making sure that you process all that information and can integrate it in a way that aligns with the methodology, providing you the right insights, is difficult.”

Easier Process

Nevertheless, institutions are benefiting from a greater convergence of elements required to widen the pipeline of ESG data and increase its availability.

The creation of reporting guidelines by the IFRS’ International Sustainability Standards Board has helped to dovetail several often-competing disclosure codes into one set of guidelines. These are being integrated into regulations being constructed by regulators around the world.

And on the regulatory front, the European Union’s Corporate Sustainability Reporting Directive, which compels 50,000 companies to begin disclosing their ESG performance data, is expected to provide a template for other jurisdictions to encourage greater data submissions.

“Now that we can start measuring and understanding companies’ ESG performance better, investors need to grow their knowledge of the dependencies of all those metrics and the implications for their own investment decisions,” he said.

“It will be interesting to see the new dynamics with companies and how service providers can support answer those questions that are coming to the table now.”

• The “ESG Data Sourcing and Management to Meet Your ESG Strategy, Objectives and Timeline” webinar will be held on June 11, 2024, at 10:00am ET / 3:00pm London / 4:00pm CET. There’s still time to subscribe, by clicking here.

The post Webinar Preview: ESG Data Management Challenge of New Sourcing Landscape appeared first on A-Team.

From helping to pull data from unstructured sources, such as reports and other written documents, to cross-referencing and matching disparate datasets, AI has become an indispensable part of ESG data managers’ toolkit.

Entity Matching

Customers of S&P Global Market Intelligence are struggling with the complexity of these new datasets, explained, Neil Robertson, the company’s managing director of commercial strategy. S&P Global Market Intelligence offers a suite of tools to help in the processing and management of sustainability data. Among them is its Kensho application, which is empowering clients in one of the trickiest parts of ESG data management – matching it with financial entity datasets.

A challenge faced especially by institutions that run multi-asset business models is linking unstructured data on topics such as carbon intensity or diversity to specific companies. The problem is most acute with private companies that aren’t attributed the same legal identification codes as listed public businesses.

Trying to cross reference ESG data to those firms requires huge amounts of processing time that often can’t be carried out manually. Robertson explained that this issue was alleviated by Kensho, which uses AI to link ESG datapoints to 28 million companies and 77 million securities.

Kensho is but a part of S&P Global Market Intelligence’s broader ESG data management as a service offering, which enables clients to outsource the management of key data processes to the company’s cloud-based hub of expertise and data feeds.

Data Quality

As Clarity AI’s name suggests, its business is focused on bringing AI to the data management process. At the ESG Data and Tech Summit London, the company’s chief sustainability officer, Lorenzo Saa, explained that the most important element of AI to ensure the quality and content of the data on which the models work. Get the data inputs right and the outputs would be sound. Saa explained that this was particularly important given that clients were increasingly shunning ratings and scores in favour of raw data.

Achieving this calls for not only good quality data but also full data coverage of any given ESG topic. AI can aid in this, Saa said, explaining that the technology’s ability to retrieve data from disparate unstructured sources was enabling firms to bolster their datasets and fill gaps in reported information.

AI is also capable to filling data gaps through its enormous analytical prowess, which enables the technology to make robust estimates to complete missing datapoints. Far from traditional estimation models, which have been criticised as unreliable and accused of fuelling greenwashing, Saa said AI models are able to work on vast tracts of data and variables to produce outputs that are accurate and trustworthy.

Third Parties

There was broad agreement between Saa, Robertson and the Summit’s third Key Note speaker, SoftServe’s Antonina Skrypnyk, on many topics related to AI deployment, especially on its power to transform a multitude of workflows.

One other key point of concord was the need to keep “humans in the loop” of any AI-enhanced workflow, particularly to ensure the quality of input data and the validation of outputs. Another important matter of agreement was the best practice of ensuring that firms’ entire corporate bodies were invested in any AI-led digital transformation.

AI integration can be very expensive and the process of transformation can be a long one. Consequently, said Skrypnyk – SoftServe’s VP FSI for EMEA solutions and consulting – it is vital that internal and external stakeholders are fully committed to their proposed AI “journeys”.

The post Summit Key Note Speakers Highlight Growing use of AI in Data Management appeared first on A-Team.

Enter the Corporate Sustainability Reporting Directive (CSRD), the ambitious regulatory framework aimed at standardising sustainability reporting across the EU. As firms scramble to align with these new requirements that become mandatory for the first tranche of covered firms in 2025, the directive promises to reshape the landscape of corporate sustainability, holding companies to a higher standard of environmental and social responsibility.

The Securities and Exchange Commission (SEC) has attempted to implement its own set of guidelines. However, these efforts continue to be entangled in a web of litigation and partisan debate. As firms navigate this turbulent environment, the contrast with Europe’s more pragmatic approach becomes apparent.

In this article, we will delve into the CSRD implications from the contexts of governance, workflow, data management, and enabling technology and explore the challenges that arise in each area.

The Corporate Sustainability Reporting Directive (CSRD)

The CSRD mandates that companies report in accordance with the European Sustainability Reporting Standards (ESRS). These standards encompass a broad range of sustainability topics, structured into two general standards and ten topical standards covering ESG aspects. The general standards (ESRS 1 and ESRS 2) provide guidelines on the overall framework for sustainability reporting and general disclosures that all companies must make. The topical standards delve into specific areas such as climate change, pollution, water and marine resources, biodiversity, and social matters like labour practices and human rights.

Companies must conduct a double materiality assessment to identify which sustainability issues are material from both an impact perspective (how the company affects the environment and society) and a financial perspective (how sustainability issues affect the company’s financial performance). This comprehensive approach ensures that all significant sustainability impacts, risks, and opportunities are reported.

The European Financial Reporting Advisory Group (EFRAG) provides comprehensive guidance on implementing the double materiality approach required under the CSRD. Double materiality encompasses both financial materiality (how sustainability issues affect the company’s financial performance) and impact materiality (how the company’s operations impact the environment and society). EFRAG has been tasked with developing the digital XBRL taxonomy for the ESRS, which will facilitate the tagging of sustainability reports in a machine-readable format.

EFRAG’s implementation guidance (IG) outlines several critical steps and considerations for firms to measure materiality effectively:

Understanding Context and Stakeholders

Firms are advised to start by thoroughly understanding their operational context, including business processes, business relationships, and affected stakeholders. This step involves mapping the company’s value chain to identify relevant sustainability matters and potential impacts, risks, and opportunities (IROs).

Criteria for Materiality Assessment

EFRAG recommends using objective criteria to assess the materiality of identified impacts. For impact materiality, companies should evaluate the severity of impacts based on their scale, scope, and irremediable character, along with the likelihood of potential impacts. For financial materiality, the focus is on the magnitude and likelihood of financial effects, including performance, financial position, cash flows, and access to capital.

Stakeholder Engagement

Engaging stakeholders is crucial for substantiating the materiality assessment. This involves consulting affected stakeholders (e.g., employees, communities) and users of sustainability reports (e.g., investors) to gather diverse perspectives and ensure the assessment reflects the concerns and priorities of all relevant parties.

Key Challenges for In-Scope Firms

ESG terminology introduces many new terms and definitions, some of which are not readily represented digitally, creating new data requirements and sourcing challenges.

Existing GRC teams will need to absorb new roles and responsibilities, or new roles will need to be created and positions filled with the right skill sets. The terms of reference, authorities and accountabilities for these roles must be clearly defined.

Integrating materiality assessments into corporate governance frameworks requires that boards and senior management be actively involved in overseeing the materiality assessment process, ensuring that sustainability considerations are embedded in strategic decision-making. Regular updates and reviews of the materiality assessment process are essential to maintain its relevance and effectiveness

The materiality assessment process must be integrated into existing business processes, particularly risk management, strategy development, and reporting. This integration ensures that sustainability risks and opportunities are considered alongside traditional financial metrics.

Companies will need to collect, process, and analyse large volumes of sustainability data from many sources. including internal operations and external stakeholders. One of the biggest challenges will be identifying, collecting and preparing these new data sources. Scope 3 emissions data is particularly complex in this regard.

Scope 3 emissions encompass the indirect greenhouse gas (GHG) emissions that occur throughout a company’s value chain, both upstream and downstream. These include emissions from suppliers, business travel, employee commuting, waste disposal, and the use of sold products.

Unlike Scope 1 (direct emissions from owned or controlled sources) and Scope 2 (indirect emissions from purchased energy), Scope 3 emissions are often the most challenging to measure and manage due to their diffuse nature and dependence on third-party data. Despite these challenges, addressing Scope 3 emissions is crucial as they frequently represent the largest portion of a company’s total carbon footprint.

Scope 3 emissions include those produced by suppliers (upstream) and by customers using the company’s products or services (downstream). Scope 3 is critical because it often represents the largest portion of a company’s total emissions, significantly affecting the company’s carbon footprint.

Effective management and reduction of Scope 3 emissions are essential for companies aiming to achieve net-zero targets and contribute to global climate goals. Ideally, carbon footprints would be available at individual workload levels in near real-time. But that’s a long way off, and several things need to happen before it becomes feasible. Principal among these is the lack of scope 3 data granularity currently available from cloud service providers.

Data centre power consumption is a major contributor to the carbon footprint of capital markets firms. The rapid uptake of Generative AI(GenAI) and other frontier AI technologies is projected to disproportionately increase data centre power consumption. This will force firms to rethink their data centre and hybrid multi-cloud strategies.

Technology will be a critical enabler for a successful CSRD implementation as it is for all GRC functions. GenAI and LLM’s ability to process vast quantities of unstructured sustainability data and automatically match compliance obligations with impacts, risks and opportunities (IROs) for double materiality assessments will significantly boost efficiency and productivity for sustainability compliance.

The alignment between CSRD and the IFRS Sustainability Disclosure Standards, including the integration of Scope 3 emissions, reflects a global move towards more transparent and standardised sustainability reporting. The International Sustainability Standards Board (ISSB) has also included Scope 3 emissions in its climate-related disclosure requirements, emphasising the importance of these emissions in understanding a company’s overall environmental impact and climate resilience.

As companies navigate the CSRD and the data integration challenges of Scope 3 emissions, they are presented with an opportunity to lead in sustainability and transparency. By embracing the challenges of robust data management, advanced technology integration, and comprehensive governance frameworks, businesses can not only achieve compliance but also drive significant environmental and social impact.

The post The CSRD – Governance, Workflow, Data and Technology Impacts for 2024 appeared first on A-Team.

That was one of the key messages from the opening presentation at A-Team Group’s third annual ESG Data and Tech Summit London, in which Nirav Shah, senior executive director, quant and ESG technology at JP Morgan Asset Management, painted a picture of an improving but still evolving data landscape for sustainability-focused institutions.

‘Patchy’ Quality

Speaking to Mark Davies, partner and EMEA lead at data management provider Element 22, Shah paid close attention to data quality, which he said remains “patchy”. Drawing particular attention to the importance of data lineage and traceability, he said that without good quality data, ESG processes can’t function optimally.

When asked if AI offered a solution to the “quality gap”, Shah said he thought it as unlikely for the time being. AI is helping to solve the “content gap”, by finding themes, patterns and insights in datasets, he said, but it was yet to show it could help improve data quality. Large language models (LLMs), of which Generative AI (GenAI) is an example, would be especially lacking in this regard because they are not deterministic and every time a user asks it a question, it gives a different answer.

Getting Better

Shah was positive about improvements to data sourcing. He said that “basic” ESG data, such as emissions and climate information, were now relatively easy to obtain. Nevertheless, the picture was less rosy for specific sustainability factors that are emerging in importance. For instance, Davies noted that only 30 per cent of portfolio companies were reporting gender pay gap statistics and only half that proportion were disclosing on their water emissions performance.

That was posing a problem especially as firms such as JPMorgan were using fewer aggregated metrics such scores and ratings and instead were focusing on net-zero transition progress, which requires granular raw data. However, the downside is being balanced by more companies reporting Scope 3 data, thanks to the creation of new and innovative datasets.

Shah said the perception of ESG was changing from being part of firms’ investment theses to being an integral element of their overall risk strategies. In this regard, integration of sustainability data should not be treated any differently than other financial risk data.

Organisations that did otherwise often wasted time creating processes that “are ancillary to their core operations” to the neglect of data quality oversight. It would also undermine the ability to cross-reference the information with other financial datasets.

AI Potential

After 12 months in which the financial sector – and others besides – had been dominated by speculation about the opportunities offered by AI, it was unsurprising that the ESG Data and Tech Summit London devoted considerable time to the theme. (A-Team Group will focus on the importance of this far-reaching new technology at its AI in Capital Markets Summit London on June 20.)

Shah said applications such as machine learning were bringing “fundamental change”. But he cautioned that the use cases for GenAI were yet to be fully understood and that the benefits offered so far are very expensive to scale.

An audience poll echoed that sentiment. At the top of respondents’ answers to the question “What’s your biggest challenges frustrations when dealing with ESG and AI?” was proof of concept and the cost to scale.

Like many of the speakers over the course of the summit, Shah said it was important to keep humans in any workflows that utilise AI capabilities. LLMs may be able to pull in more data than established methods but it will still needs analysts to view it outputs for the foreseeable future.

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From next week, we will continue to provide our sustainability data and tech blogs, news and views through our core brands – Data Management Insight, RegTech Insight and TradingTech Insight.

It’s a bold move but one we feel reflects the changing nature of the ESG space as it moves into a new era of greater data reliance, artificial intelligence-driven processes and broader regulatory oversight.

Swift Development

Since the sustainable investment project went into full swing about a decade ago, ESG considerations have become firmly integrated in the data processes of financial institutions. This is a remarkable shift.

In the early days, traditional suppliers of financial data scrambled to put together the then niche non-financial datasets requested by investors who wanted to put their money into companies that made a difference to a warming climate and social ills. Their operations became more sophisticated as regulators began to promote and monitor these burgeoning sustainability markets.

Change hastened change and specialist data vendors quickly emerged, enabling firms to cherry pick datasets on the specific ESG factors that are most material to them. Now, AI is helping to refine research and operations, and more importantly, mine trends and patterns in data that can lead to better investment and risk decision making.

From the days of one-stop-shop providers, we now have innovative operators offering data on everything from corporate emissions, biodiversity and nature loss, through to human rights controversies and sanctions.

A New Venture

In 2021, A-Team Group established ESG Insight to reflect this fast-changing space.

In that time some of the toughest challenges facing the space have begun to be resolved.

• The International Sustainability Standards Board’s guidelines were announced, setting a path for the streamlining global reporting standards.
• The Sustainable Finance Disclosure Regulation and the Corporate Sustainability Reporting Directive in the European Union were implemented, creating rules that are expected to stimulate – and ensure the validity of – better and fuller ESG data disclosures.
• The application of AI has begun helping firms to extract more value from their sustainability data.

The challenges that faced ESG markets when we founded ESG Insight have changed significantly. And so it is time for A-Team Group to reflect those changes.

Shifting Priorities

Our polling has shown that, by a substantial margin, the challenges facing institutions have begun to shift. Data quality – and by extension, data sourcing – is still a tricky hurdle. But our most recent ESG Data and Tech Summit in London showed that its relative importance is waning as data gaps close and quality improves.

Firms are now focusing increasing attention on tackling the enormous data management and regulatory challenges that the sprawling ESG space has fostered. The volumes of unstructured data flowing into firms’ systems is expanding every day, putting strain on their technology setups and their newly established teams of data scientists. Regulatory compliance professionals are also under intense pressure to adopt new use cases and adapt existing processes to meet fast-evolving reporting requirements.

Fresh Approach

These shifting priorities mean that the task of reflecting this new phase of opportunities and challenges will be best served not through an ESG-specific lens but through one that takes a broader view of sustainability data’s management and its uses.

ESG has evolved into a business-as-usual part of firms’ operations. That isn’t to say it is no longer important. In fact, the opposite is true. Ten years ago, there was no ESG data. Now it is an integral part of the global financial system, helping to foster a sustainability market that estimated to account for around US$35 trillion of assets by the end of the decade.

By covering ESG within its wider data management context, A-Team Group is recognising that sustainability has properly “arrived” and that its coverage requires a more holistic approach.

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