That’s before indirect costs such as reputational and legal damages are considered.

Since its creation in 2018, TurinTech AI has been on a mission to help firms reduce costs. The UK-based company leverages GenAI to pinpoint areas in mission-critical software systems where optimisation is needed and then it generates better code to enhance performance and efficiency.

TurinTech AI’s technology offers a range of services that can streamline billions of lines of code to reduce applications’ pressure on CPU processing power and cloud use. By doing so companies also reduce the energy needed to carry out their everyday processes, a benefit that decreases their carbon footprint, said chief executive Leslie Kanthan.

“Financial institutions, banks and hedge funds have huge amounts of code –hundreds of millions of lines of code – and it would take 40 Guys 10 years just to review a couple of million lines of code; it’s an intractable problem,” Kanthan told Data Management Insight.

Efficiency Gains

TurinTech AI was formed by three PhD candidates who met at University College London and had gained experience of – and become frustrated by – the code optimisation tasks they’d been required to carry out at the financial companies where they subsequently worked. They founded TurinTech AI with its first product, a code generator powered by machine learning called evoML. Its GenAI Artemis AI code optimiser followed.

TurinTech AI says that its Artemis AI innovation has allowed clients to improve the efficiency of their coding by as much as a third. For example, it was able to improve by 32.7 per cent the runtime of pull requests on QuantLib – an open-source code library favoured by financial institutions for quantitative finance tasks, including modelling and trading.

“It ensures that you are using less of your service resources,” said Kanthan. “So if your code was taking up 30 per cent of your Amazon budget, it might now be taking 20 per cent of your Amazon budget and at the same time improving your footprint for ESG.”

Firms can be expected to dedicate about half of their overall software development budgets to debugging and fixing errors over the 25-year life expectancy of a large software system.

That low-skill work will most likely be carried out by highly trained technology professionals. Kanthan points to the experience of a globally known technology brand client that employed hundreds of developers to manually go over code to find inefficiencies.

“They’re all PhDs and professors who should be building new applications not going back through existing code,” he said. “We saved them the time, we saved them the labour resource, we gave them cost efficiency and we allow them to get more output from what they already had.”

Speed Bumps

Financial institutions face greater exposure to coding quality challenges also because they need to develop and deploy new applications at speed. Under such time-to-market constraints, developers will build applications as quickly as possible, and that might mean they use tried-and-trusted phrasing that may not be the most efficient.

A common example, Kanthan said, is the use of for-loops, which are quick to write and are reliable, but they are not as efficient as other structures.

“It’s so hard as a developer to do things the most efficient way because of the time constraints; if they’re given an objective and told it is needed by the end of the week, they do it as quickly they possibly can – their priority is to get the result they want,” said Kanthan. “So, they might do it in a messy way that duplicates many functions.”

The pressure to default to a reliable solution is also seen in the continued use by some organisations of the Fortran. It’s an old CPU-hogging language but it is dependable, and replacing it would incur a huge transitional cost. TurinTech’s Artemis AI can be deployed to translate those old-style codes into modern and more efficient C++.

“Fortran is a very old and obsolete language but because it works, it doesn’t break and no one wants to touch it,” said Kanthan. “It’s too expensive to get Fortran developers because they are hard to find and very expensive. So, you’re talking about spending thousands of pounds per day per person to work on millions of lines of code, using our product will bring enormous savings.”

The post TurinTech Deploys GenAI to Accelerate Financial Software appeared first on A-Team.

By Yann Bloch, VP Product Management at NeoXam.

In today’s investment world, the importance of integrating environmental, social, and governance factors into investment strategies is no longer up for debate. Asset managers globally recognise that sustainable business practices are not only vital for ethical considerations but are also critical for long-term financial performance. Despite this recognition, a significant challenge persists: accessing reliable and comparable ESG data, particularly from private companies that often lack standardised reporting practices. The solution to this problem lies in the innovative use of artificial intelligence (AI) technologies.

Private companies are increasingly producing sustainability reports that provide valuable insights into their ESG performance. However, these reports come in various formats, use different terminologies and offer varying levels of detail, creating a complex, unstructured data landscape. This lack of standardisation makes it difficult for asset managers to efficiently extract and utilise the data, hindering their ability to make informed investment decisions that align with ESG criteria.

The emergence of AI is poised to revolutionise how asset managers handle private market ESG data. AI, particularly machine learning models, can be trained to recognise and interpret the diverse formats and terminologies used in these sustainability reports. Take natural language processing (NLP) as prime case in point. A subfield of AI focused on the interaction between computers and human language, NLP can automatically extract key data points from unstructured texts. This transformation of unstructured data into structured, actionable information is a major step forward for the industry.

One of the primary benefits of using AI in this context is the ability to automate the data extraction process. Traditionally, asset managers had to manually sift through reports, a time-consuming and error-prone process. AI tools can scan thousands of documents in a fraction of the time it would take a human, ensuring that no critical information is overlooked. This not only increases efficiency but also allows asset managers to process larger volumes of data, providing a more comprehensive view of a company’s ESG performance.

AI is great for the extraction of data and even better when combined with robust data management technology. At the receiving end of AI-driven data extraction, robust data management systems ensure data quality, including consistency and completeness, and combine it with data from other sources. This integrated approach amplifies the value of AI by providing a holistic view of ESG metrics, essential for informed decision-making.

In addition, AI can enhance the comparability of ESG data from private companies. By standardising the extracted information, these technologies enable asset managers to compare ESG metrics across different firms, even if the original reports were vastly different in format and detail. This level of comparability is crucial for making informed investment decisions and for accurately assessing the ESG performance of potential investment targets.

Another significant advantage is the ability to keep pace with the evolving ESG reporting landscape. As regulatory requirements and industry standards for ESG reporting continue to develop, AI models can be updated to incorporate new criteria and metrics. This ensures that asset managers are always working with the most current and relevant data, maintaining the accuracy and reliability of their ESG assessments.

The integration of AI into ESG data management also supports transparency and accountability. By providing clear, structured data, these technologies enable asset managers to present their ESG findings to stakeholders with greater confidence and clarity. This transparency is not only beneficial for investor relations but also for meeting regulatory requirements and for maintaining the trust of clients who are increasingly demanding sustainable investment options.

The application of AI technologies in extracting private market ESG data represents a significant advancement for asset managers. These tools address the critical challenge of unstructured data, providing a streamlined, efficient, and reliable means of accessing the information necessary to drive sustainable investment strategies. As the industry continues to evolve, embracing these technological innovations will be essential for asset managers looking to stay ahead of the curve and deliver on their commitments to sustainable investing.

The post Unlocking Private Market ESG Data through AI appeared first on A-Team.

Before the measure was even introduced by the Financial Conduct Authority (FCA), however, it was apparent that fund managers’ likelihood of adopting the guidance would be limited by their data setups. Experts have told Data Management Insight that solving this challenge would be critical to meeting the goals that underpin the SDR.

Since July 31, managers have been asked to voluntarily label their products according to the degree to which they can be considered sustainable.

Those labelled “Sustainability Improvers” denote assets that have the potential to become sustainable but may not be now. “Sustainability Impact” products are those that invest in solutions that bring beneficial ESG impacts. “Sustainability Mixed Goals” labels indicate investment vehicles that combine the other two. A fourth, “Sustainability Focus”, is reserved for products that have at least 70% of allocations to sustainable assets.

Those seeking to adopt the labels must show they meet the requirements by the beginning of December.

Clarity Needed

Critics have predicted a slow uptake of the labels by fund houses, with some arguing that more clarity is needed about how the labels can be properly applied. At the heart of that challenge is likely to be firms’ ability to gather and use the data necessary to make those decisions.

The FCA said last year that asset managers and manufacturers must have robust data, governance and technology setups to adopt its measures. A poll during a webinar by consultancy firm Bovill Newgate, however, found that 90% of financial services respondents said they were not equipped with the correct ESG reporting data.

Emil Stigsgaard Fuglsang, co-founder at ESG data and consultancy firm Matter said data would be a potential pain point for firms operating in the UK.

“While many global investment managers already have these competencies in place thanks to the requirements of other regulations, the majority of smaller British firms do not,” Fugslang said.

“This means they face the challenge of accurately defining sustainability in their investments and implementing data and analytics solutions to track and document their performance against these definitions at the fund-level. This will be no easy task, but those who take action now will be best prepared by the December 2 deadline.”

Investor Protections

The labelling guidance follows the publication of anti-greenwashing advice by the FCA earlier this year, which seeks to protect investors from abuse by encouraging asset managers and manufacturers to be clear and precise in the descriptions of their products.

The FCA is keen to safeguard investors against being lured by false claims of an asset or product’s sustainability. The threat of greenwashing has been wielded as a weapon in an ESG backlash, most notably in the US, that has seen billions of dollars pulled from sustainability-linked funds.

While the measure is designed primarily to protect retail investors, it is expected also to have an impact on institutional capital allocators. One of the first funds to adopt an SDR label, AEW’s impact fund, has taken the Sustainable Impact categorisation and is offered only to institutions.

The SDR is also widely predicted to set transparency standards that institutions are likely to follow.

ESMA Guidance

The UK’s latest SDR implementation came as Europe’s regulators sought changes to some of the European Union’s disclosure rules. The European Securities and Markets Authority (ESMA), last week suggested changes that would affect the bloc’s lynchpin Sustainable Finance Disclosure Regulation (SFDR) and other measures.

In an opinion piece it set out a set of proposals that urge tweaks to the EU’s wider sustainable finance framework, arguing that there needs to be greater “interconnectedness between its different components”.

Among ESMA’s proposals are a phasing out of the phrase “sustainable investments” within the SFDR and a recommendation that market participants should instead make reference only to the green Taxonomy that underpins European market rules. Further, it suggested an acceleration of the Taxonomy’s completion, incorporating a social taxonomy.

It also urged that ESG data products be brought under regulatory scrutiny to improve their quality.

Clash of Standards

Other recommendations on how sustainability products should be described could conflict with the new measures introduced by the FCA.

ESMA suggests that all products provide basic information on their sustainability, with greatest detail given to institutional investors. It also urges the introduction of a “best in class” product categorisation system. That would include at least a “Sustainability” classification, denoting products that are already green, and a “Transition” grouping of funds that aim to be sustainable.

Martina Macpherson, head of ESG product strategy and management at SIX Financial Information, said institutions would need to familiarise themselves with each code.

“Challenges for asset managers remain to categorise funds in line with the UK’s labelling regime, and to align them with the EU’s fund labelling rules introduced by ESMA,” MacPherson said. “Overall, ESG fund labels represent a significant next step to address transparency and greenwashing concerns. Meanwhile, the mounting public and regulatory attention surrounding sustainable investment demands firms to use the most reliable, legitimate, and timely data to inform their decisions.”

The post Latest UK SDR Measure Highlights Data Challenge appeared first on A-Team.

The venture saw Gresham Technologies delist from the public market to create the new company, which will be known as Gresham. The deal has resulted in a company that combines Gresham Technologies’ transaction control and reconciliations, data aggregation, connectivity solutions and regulatory reporting capabilities with Alveo’s enterprise data management for market, reference and ESG data.

Backed by Alveo’s majority investor STG, a technology-focused private equity firm, the combined business has got to work promoting what it calls its enterprise data automation offering.

Data Management Insight spoke to chief executive Ian Manocha, formerly head of Gresham Technologies, and chair Mark Hepsworth, who held the leadership role at Alveo, about the genesis of the merger and their plans for the future.

“We think it’s a big thing, and I hope the industry recognises that too,” says Hepsworth.

Data Management Insight: What was the rationale behind this merger?

Ian Manocha: Mark and I have known each other and for quite a few years and have always seen the strategic value of working together.

Mark Hepsworth: We’re complementary businesses. We at Alveo focus on enterprise data management, market data, reference data and ESG data and Gresham has built a business around reconciliation, investment management data and connectivity services through to regulatory reporting. The common thread is that we’re both solving data management problems for customers in financial services.

DMI: Where do you see complementarity?

MH: There’s a lot of overlap in terms of some of our customers but also the type of customers that we both sell to, the parts of those customers that we sell to both on the sell side and the buy side, and in areas like exchanges. Also, often at a senior level the same person is responsible for what their firm is doing around market data, as well as reconciliations data for example, and data management..

DMI: What triggered the eventual decision to merge?

IM: A number of things really came together at the right time. There was STG’s interest in us and the board’s view that our shareholders would be open to an exit at the right price. And from a Gresham perspective, we had a sense that, at this stage of the company’s development, we were going to be better served coming off the public markets and having the backing of a large firm like STG to accelerate our journey to take on the opportunities that we were seeing in the market. Mark and I started having the ‘we are finally going to make this happen’, conversation.

DMI: What are those opportunities?

IM: Between us we’ve got the landscape well covered so the question is now, having got all that data and now having the capability to manage it and ensure the quality of it, and of course, the reconciliation capabilities, a part of that question is, ‘what more can we do with it – how can we convert that into a business opportunity for our clients’? That’s the exciting area. So we see an opportunity now to invest more in areas like AI and to invest more in other players in the market.

DMI: What are your plans for growth?

IM: Gresham built a business organically and with some M&A work – we’ve acquired four firms in my nine years at the company. But that’s become more difficult for us on the public markets. It’s well known that there are challenges around liquidity particularly for small caps. We now have the financial backing of STG to look at those opportunities, whether we go after other firms or through organic investment, to fill out that vision of being the leading player in the data automation space for capital markets.

DMI: What will the new company offer its customers?

MH: What we’re really looking to do is create a significant new player in data management for financial services. We now have a broader range of capabilities and data management solutions that stretches further across the enterprise than they did before so we can solve more problems for clients.

Clients have a real focus on data both operationally and in terms of efficiently processing that data and delivering to business users, and doing that with the right level of governance control and transparency. All our customers are regulated and ensuring that they’re using high-quality data in their downstream processes is very important.

IM: Our customers are looking for a real heavyweight player in the data automation and data management space. They want a single heavyweight, well-funded, global company with strong technology capabilities and deep domain expertise to be their partner in their digital transformation because they’re fed up working with people that don’t understand the detail of capital markets data, and they’re fed up with having too many parties to work with.

DMI: What factors are driving the demand you want to meet?

MH: What I’ve seen over the years is that clients effectively feel data management could be easier than it is, that there’s more manual process than then they’d like. Both our companies have really focused their roadmaps in recent years around how we make that easier for customers. We moved to the cloud and both adopted open-source technologies that facilitates easy data management, as well as focused on improving business user self service. We really want to make data management easier for customers and that’s really where we’re going with the automation piece in our new tag line

IM: I’ve long felt that customers are looking to simplify their operating models. It’s not just about having the technical software, it’s also the skills and the capacity to deliver the change that’s needed. That’s particularly true in the mid-sized and smaller firms. There’s no way they can possibly build all that capability in house so we want to be the partner that they seek to deliver that end to-end-capability as a service.

DMI: Are there any practical technical issues you have had to overcome in your integration?

IM: Both firms have got modern development shops, cloud-native tech stacks and we use modern tools, so the kind of legacy stuff that’s harder to move forward is not an issue for us. And at the product level, things like APIs and cloud solutions, you don’t necessarily need to have the deep level of integration you did in the past. So for customers that won’t be visible.

DMI: What products and services will you be offering initially and what do you have in the pipeline?

MH: We will continue to offer those solutions we’re famous for: data automation and control, reconciliations and exceptions management, market data EDM, investment management data aggregation and regulatory reporting. But we’re also excited to get going on new initiatives.

IM: First out of the gates will be offerings for investment managers leveraging the greater richness of data that we now manage on their behalf. Let me give you a practical example, in Alveo market data pricing projects we are readily able to source pricing data for liquid assets but often struggle to obtain pricing for illiquid assets. Whereas in many Gresham NAV reconciliation projects were are pulling latest available pricing for some illiquid assets. So together we can fill a price visibility gap for our customers.

There are many other examples where we can now inject valuable insights into core processes without firms having to invest in costly, risky, data lake projects.  And thinking more strategically, leveraging the Alveo data management technology will help business users with self-service and distribution of these combined data sets. It’s super exciting for us and the customers we’ve spoken to are also enthusiastic which it the acid test.

The post Meeting New Capital Markets Challenges: Gresham and Alveo Leaders Discuss Merger and Future Plans appeared first on A-Team.

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.