Use of ESG (Environmental, Social and Governance) parameters in investment decision making is on the rise, and given the strong corelation between investors returns and robust ESG risk management the trend of ESG integration in investment decision making will accelerate. Investors are seeking greater transparency from corporates as ESG risks are having tangible impact on financial performance.
Asset owners and asset managers are using this data provided by best ESG data providers and in-house models to find long-term investment opportunities with lower financial risk and high Alphas. According to FinScience, more than 70% of global investors applied an ESG lens to more than a quarter of their portfolios in 2021, up from 48% in 2017.
Investors require accurate ESG data for metrics, ratings, and optional or mandated ESG reporting, for example SFDR reporting.
Given the inadequate standardization and the breadth of material ESG issues, finding all required data in the ESG catalogue of any single data provider is unlikely. Hence asset manager are either working with multiple data providers or commissioning specialised data providers to collect their ESG data requirements.
Why is the Quality of ESG Data Important?
As portfolio selection and management is increasingly driven by sophisticated analytical tools, the accuracy of the analysis depends largely on the quality of the underlying data. Quality of data itself is predicated on the methodology used for data collection, the experience of the analyst collecting data and the comprehensiveness of disclosures. As research increases in complexity, and human intervention in portfolio selection reduces, the quality of data become more significant.
Analyst vs AI-Driven ESG Data
If high-quality ESG data is to bring considerable value to investors, it must be frequently updated. As frequent manual update of data is very expensive, a growing number of investors are looking to AI-derived ESG data. This is ESG data compiled by shifting through enormous amounts of unstructured data using machine. While the cost and time to market benefits of benefits of AI derived data is well understood, the quality of the data is still below the high standard expected by the financial services industry. As well as information about ESG important for your business.
To overcome this limitation, specialised data providers are using a combination of analyst and AI derived data. Analysts initially define the sources of data, train the machines and check the quality of machine curated data. As the reliability of the AI derived data increases, it is incorporated in the data feeds/output sent to the market.
Access to high-quality ESG data is becoming a source of competitive advantage as more investors connect the dots between ESG performance and investment performance. A combination of Analyst and AI-derived ESG data from a new generation of ESG data providers is providing required quality levels and emerges ad the best approach to acquiring unbiased, objective, and up-to-date ESG data.