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esg data, ESG Data Analytics

Ensuring ESG integrity with an AI/ML-enabled Systems

Posted on : May 20th 2022

Author : Sundar Rengamani

Tackling greenwashing requires a vigilant system

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.

A system that works

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.

Exhibit 1: Readily Organizing and Analyzing Unstructured Data Is Difficult


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.

The data context

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.

Exhibit 2: ESG Data Sources are Vast and Complex


Source: Straive.

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.

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