Posted on : May 20th 2022
Author : Sundar Rengamani
In environmental and social governance (ESG) parlance, greenwashing is the deliberate practice of making a company appear more sustainable (or green) than it really is. According to the 2021 RBC Global Asset Management Responsible Investment Survey, 72% of global investors integrate ESG principles in their investment approaches and decision-making. Tackling greenwashing requires a vigilant or automated system that works collectively with multiple data sources and formats to track down and report suspect claims.
The current trend is to identify greenwashing using sophisticated artificial intelligence (AI) and machine learning (ML) tools. Applying AI-enabled tools to the trove of unstructured data available helps counteract greenwashing. The role of subject matter experts is critical in developing AI-enabled systems that rules-based machine learning models can train.
Detecting greenwashing involves a range of activities and sources. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents and collect evidence across executive statements for signs of vagueness or obfuscation. It usually provides leads regarding any greenwashing attempts.
Also, the sheer volume of generated data makes it difficult to manage and process and hinders the holistic picture of an organization. This opens ups opportunities for organizations to identify and exploits potential gaps. The present data-intensive environments help identify and challenge misleading corporate narratives around ESG by gathering data-based evidence from various sources.
Unstructured data can come from almost any source. Nearly every asset or piece of content created or shared by a device in the cloud carries unstructured data. With the introduction of regulations such as the Sustainable Finance Disclosure Regulation (SFDR) -- in the EU -- investors have increased attention regarding ESG data operations. Investors are desperate for an information and insights advantage.
Advanced AI/ML platforms can collect and analyze vast amounts of alternative data – ranging from satellite images to social media chatter – to assess the impact of ESG activities. In addition, alternative data sources are infusing transparency and data standardization, making it difficult for companies to mask greenwashing practices.
Straive’s Straive Data Platform (SDP) – an end-to-end data management platform focused on unstructured data solutions – aggregates data from multiple sources. It blends unstructured data with structured ones while scanning various information sources, e.g., news articles. Our automated solutions trawl through corporate information assets and public data like satellite images. Thus, Straive can develop ESG scores to judge degrees of greenwashing.
ESG data lies scattered across thousands of news articles, corporate filings, NGO reports, government papers, social media posts, email communications, etc. Real-time extraction and transformation of ESG data into insights requires advanced platform capabilities.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation. It usually provides leads regarding any greenwashing attempts. AI tools can deconstruct corporate statements, company annual reports, alternative data sources, etc., for triangulating climate-related claims or disclosures.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
Alternative ESG data - Often associated with financial analysis, alternative data is a term that typically refers to externally sourced information about a particular company to gain additional business insights.
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