ESG Data Guide 2022

Bringing societal impact to markets through powerful, scalable tech

Sustainability AI-Powered Tech for Accurate and Easy Analysis and Reporting

Clarity AI was founded back in 2017 with the mission of bringing societal impact to markets, through a fresh approach to ESG and sustainability data, based on technology and transparency. 

We address the main challenges of the industry through state-of-the-art solutions powered by advanced technology and deep sustainability expertise. Here are a few examples of how we do it:

  1. Data quality, coverage, and granularity

Our three primary sources of data include external data providers, our own data collection processes, and our own estimation models. This allows us to develop reliable data sets at scale. 

Based on a recent study done by Clarity AI’s research team, in a sample of more than 30,000 data points from three different data providers, discrepancies were present more than 40% of the time.

We compare multiple sources and identify the most reliable one for each data point, using advanced technology such as machine learning and natural language processing.

As a result, our data coverage goes up to 13 times more than other providers. Additionally, we provide unique systematic transparency on the source and confidence level of each data point.

     2. Complexity of methodologies, lack of transparency, and aggregated confusion

At Clarity AI, we allow anyone to chart a path to a more sustainable world because we acknowledge the fact that there is no one size fits all approach to sustainability assessment. To achieve this, we empower our clients to form their own views. 

Beyond high-quality data, we provide transparent and science-based methodologies that are accessible for all our solutions. Because our clients might have specific sustainability preferences and needs, we offer customizable scoring metrics. For ESG risk, for instance, we rely on SASB financial materiality matrix but we also offer the ability to adjust each and every weight at a pillar, sub-pillar, or KPI level, to select between a best-in-class or a best-in-universe approach, and to choose specific data relevance thresholds.

      3. Inadequate number of skilled resources to ensure timely follow-up on portfolios and error corrections

As a tech company, we do not rely on human analysts to deploy adjustments at scale. There is no other way to efficiently cover up to 50,000 issuers and more than 300,000 funds. This agility also enables us to keep up with the pace of regulatory evolution. For instance, it took us only two months to implement the two new taxonomy environmental objectives on biodiversity and pollution. Every two weeks we automatically update new reported taxonomy alignment figures publicly disclosed by companies. Lastly, relying on robust AI models (maintained by a team of over 25 data scientists) allows us to implement data consistency checks, identify outliers, and proceed with error corrections when needed.

      4. Conflicts of interest

Our scores are outputs of algorithms and do not rely on qualitative assessments by analysts. The underlying methodologies are transparent and applied systematically. Moreover as illustrated above the scoring processes are customizable by our clients. Finally, not only do we stay away from any advisory services for corporations, but we also pride ourselves on being  independent of any investment solution providers, thus having full ability to focus on delivering best-of-breed sustainability assessment capabilities. 

As the European Securities and Markets Authority (ESMA) puts it, “the market for ESG rating and data providers is indicative of an immature but growing market”. At Clarity AI we found that the best way to overcome the market limitations to help the Sustainable investment industry grow at scale on a sound basis was to define ourselves neither as an ESG data nor as an ESG rating provider but as a sustainability tech platform.