Describe how you actively include input and information, wherever possible, from relevant stakeholders or interested parties, in the research process or in reaching assessment conclusions.
The Covalence approach is based on a diversity of sources of information and relies on web monitoring and artificial intelligence together with human analysis. We compare ESG data publicly reported by companies (disclosure) to online narrative content reflecting the perceptions of stakeholders such as the media and NGOs (reputation). This approach allows users to track inconsistencies, monitor changes and benefit from timely alternative data. The information is delivered in an actionable format to support ESG risk exposure mitigation and long term value creation.
Stakeholders such as NGOs, governments, trade unions and the media describe the role and activities of companies in positive and negative terms generating either endorsements or controversies. Since 2001, Covalence has specialized in the semi-automated analysis of such narrative content. This expertise materialized in the award-winning EthicalQuote reputation index.
We use data collection and classification tools relying on artificial intelligence techniques (machine learning, natural language processing) in order to analyse the narrative content. This process is reinforced by human interventions to classify the content in terms of polarity (positive/negative) and criteria. Our team of analysts thoroughly checks entries proposed by the software, thus ensuring high curation standards. Only sources that are publicly identified and available online are considered.
Today, the Covalence database includes more than one million documents from over 50’000 different sources on 6000 companies that have been classified and curated by more than 600 analysts in collaboration with over 30 universities.
The database leverages the use of machine learning techniques thanks to the expertise of our Scientific Advisor Prof. Patrick Ruch, field expert and professor at the University of Applied Sciences and Arts Western Switzerland. The use of classification algorithms allows us to fully automate the collection and pre-classification of information including complex information such as polarity – or sentiment – as well as multiple criteria.