Alternative Data in ESG: regulatory risks
Alternative data is big business. Globally, buy-side firms spent $1.71 billion on it in 2020, more than half of all hedge funds use it to make investment decisions, and it’s growing used to validate sustainability claims of potential ESG investments.
In January, the Financial Conduct Authority issued a commentary statement, Access and Use of Wholesale Data, following a 2020 call for input. The use of alternative data by companies under consideration and its potential risks. Alternative data and its application will inevitably increase as companies develop systems and technologies capable of producing more meaningful, reliable and profitable information. In turn, regulatory attention will likely become more focused and associated risks more clearly identified.
What is Alternate Data?
Alternative data is not easy to define. It is not information from official or company sources, such as statements of accounts, financial reports or market data. It is usually a form of big data from which the financial health or activity of a company can be assessed. For example, satellite imagery could reveal the number of cars parked in front of a supermarket and monitor the increase or decrease in the number of customers to predict fluctuations in income. Other examples may include mobile phone data, credit card transactions, website traffic, online browsing activity, product reviews, app store analytics, and social media content.2
How is it used and what is its importance for ESG?
Used with more traditional data sources, alternative data gives investors a more complete picture of a potential investment. However, usually data alone is not enough. Raw data needs to be processed and analyzed, a process that is increasingly performed by AI technologies. As these technologies grow in sophistication and power, a broader base of data sets could be processed and manipulated to produce more meaningful, valuable, and timely business insights. Social networks are a good example. Although it provides a huge amount of information about consumers’ lives and attitudes, extracting reliable information is no small feat. In addition to applying natural language processing techniques, it is also necessary to eliminate unwanted and any false information. Its usefulness as a dataset will be compromised unless one can easily and “effectively filter out the believable from the noise”3. However, it takes little imagination to appreciate the value of alternative data if harnessed effectively.
Another good example of how alternative data can be used relates to supply chains. Large state-owned companies can have complex and international supply chains. The use of alternative data can allow investment firms to identify potential trends or future challenges in elements of supply chains. Gaining insight into an individual supply chain may not have any material benefit, but where one can gain visibility into all or significant parts of a company’s supply chain, it could provide a significant advantage.4
As acknowledged in the FCA’s feedback statement”alternative data is essential for some managers to understand complex investments such as ESG investments”. For example, investors can access alternative data that uses natural language processing of the companies’ news feed to provide deeper ESG insights than would be available in the broader market. (esg analysis article)
In part, the usefulness of alternative data to verify a company’s ESG credentials stems from a lack of consistent global standards for ESG reporting and concerns about reliance on ESG rating.5 Companies’ ESG performance reports can vary widely in terms of consistency, accuracy and timeliness. To this extent, the use of alternative data can align with the regulator’s emphasis on ensuring transparency and accuracy in the ESG investment market, and therefore strengthen protection against greenwashing.6 Beyond simply verifying that a company’s ESG credentials align with its investment mandate and values, investors are increasingly seeing a link between sustainability, both process and outcome, and profitability. future.7 Big data (alternatives) combined with sophisticated processing are increasingly seen as a way to dig deeper into companies’ actual ESG practices and outcomes.
What are the potential regulatory risks?
FCA’s call for input document captured its tentative concerns about the alternative use of data. His goal was to “explore whether there are barriers to business access to data or data analysis techniques, either of which may be a factor of harm”8 and to “understand if this dynamic can create informational advantages where companies with exclusive access to data or technology can use it to potentially identify market movements before their competitors”.9
The call for input and feedback statement identifies several areas of risk presented by alternative data and related technology. These market risks include barriers to competitiontenas well as privacy and ethical risks.
Many of the associated market risks stem from the increased use of technology, particularly algorithms, to analyze alternative data. These include “clustering” when algorithms follow the same market signal, the risk of instant crashes, and inadvertent market abuse when algorithms perform in unexpected ways. A central concern is the potential harm to market integrity. FCA is aware of competition issues, where unavailability of data leads to information asymmetries11but could alternative data pose a risk of insider trading?12
Under Article 7 of the Market Abuse Regulation, “inside information” is: of a precise nature; not public; relates, directly or indirectly, to an issuer or financial instruments; and, if made public, would likely have a significant effect on the price of the instrument.13 The potential classification of alternative data as inside information depends on whether it is public or not. Interestingly, some companies have avoided using overly predictive (i.e. overly meaningful) datasets of information normally considered inside information, such as quarterly earnings.14
Factors that indicate information is “public” include: 1) it is generally available, including via the Internet or other publication, or where it can be compiled from other generally available information; 2) members of the public can obtain the information by observation without infringing any rights or obligations of privacy, property or confidentiality.15 It doesn’t matter that the observation or analysis can only be done by someone with above-average financial resources, expertise, or skill.16
There is little doubt that other datasets are “generally available”; the fact that they are accessible only at a price does not make them non-public. However, is there a threshold above which the financial and/or technological resources required, whether to purchase or process the dataset, render the “information” unavailable in any real or practical sense? The FCA statement acknowledges that cost is a barrier to using these datasets.
Privacy and ethical risks
The FCA’s Call for Input noted that companies are increasingly accessing alternative data through third-party sources, many of which may be unregulated and/or fall outside the scope of UK law. on data protection. This raises complex privacy risks, for example regarding image recognition and user location information, as the statement notes. Accordingly, regulated entities using alternative data must have appropriate controls in place to ensure that their data providers have obtained the data ethically or in accordance with the General Data Protection Regulation.17for example by carrying out due diligence.
Additionally, when it comes to the analytics and algorithms used to process the data, companies need to ensure that they have adequate governance and oversight in place to protect against biased or unfair results.
The American Experience
Law enforcement activities in the United States suggest that these risks are significant. The SEC has already taken action against App Annie, a company that sells market data on mobile phone app performance (such as number of downloads and total revenue). This data was collected by the company by offering a free service to the applications themselves. The SEC found that, contrary to its representations to its customers, App Annie used non-aggregated, non-anonymized data from the apps it served, in order to make the market data it sold more accurate. The SEC order also noted that the company had assured its customers that it had put in place internal processes and controls to ensure that it did not sell them material nonpublic information in violation of the federal securities laws.18 We note however, unlike the UK, that the definition of non-public in a US context relates to the dissemination of information, rather than its availability in the UK, “public” means widespread availability, unlike to the American definition of “widespread”.
The FCA draws few conclusions in the statement, but plans to monitor market developments. It has commissioned further research on the extent of alternative data use and data analysis, to better understand the risks and benefits. Whatever the outcome, these issues are likely to gain traction for two reasons. First, given the reliance on alternative data to assess ESG benchmarks, its use will increase in line with investor interest in social and sustainability issues. Second, the FCA increasingly sees itself as a data regulator as much as a financial one, as big data pervades every aspect of our lives, including financial services.19 Ultimately, companies will need to identify, understand and manage the risks raised by the use of these datasets.