ESG scoring AI
We provide a 360° assessment of corporate ESG performance, by combining internal data (traditional, self-disclosed data) with external ‘alternative’ data (stakeholder-generated data) in order to measure the gap between what the companies communicate and what is stakeholder perception related to corporate sustainability commitments.
We provide a 360° assessment of corporate ESG performance, by combining internal data (traditional, self-disclosed data) with external ‘alternative’ data (stakeholder-generated data) in order to measure the gap between what the companies communicate and what is stakeholder perception related to corporate sustainability commitments.
Dedicated to Institutional Investors and Corporates
ESG performance and value creation.
A study* revealed that companies with a high ESG rating performed better in terms of budgetary outcomes and stock returns. As a consequence, there is a need for new models of ESG performance analysis, which could help companies to manage risks and opportunities and to increase profit and value on the long term.
*Banor SIM in partnership with the Politecnico di Milano; data refer to 882 stocks comprised in the Stoxx Europe 600 Index in the period 2010-2017.
External Alternative Data as measuring factor.
Measuring ESG performance only through traditional in-bound information (data published by the company itself) could lead to an incomplete assessment, as data is partial and the nature of such disclosure is strictly connected to the particular reporting framework chosen by the company.
Therefore integrating such assessment with the analysis of great amounts of external Alternative Data – by adopting an AI-based approach – generated by company stakeholders provides a more complete view of corporate ESG performance in terms of reputation.
Tracking corporate contribution to UN SDGs
FinScience Alternative ESG Scores evaluate corporate sustainability based on company performance on each of 17 UN Sustainable Development Goals (SDGs) of the UN 2030 Agenda. The 17 SDGs, together with 169 specific targets, provide a framework for governments to address their actions and investments in tackling current and future global challenges for society and the environment.
An ESG solution to support:
tracking their ESG reputation and communication strategy
monitoring and integrating stakeholder perception in company reporting and business strategies
comparing contribution to UN SDGs of companies operating in the same industry
Alternative Data to integrate sustainability in investment decisions
Learn more about our ESG methodology
Key Features
- 400+ SDG-related indicators
- 100,000+ data sources
- AI-driven data collection
- Weekly updates
- Sentiment-analysis integration
- Corporate reputation evaluation
- Green/social washing detecting tools
- Controversial activities detection
- Benchmarking tools
The Scoring process
Data Collection
Data are automatically collected from the different sources described below with different frequency (daily, weekly or annually).
Conversion into SDG-related indicators
Textual content is first analyzed via Natural Language Processing tools such as text classification, entity extraction and sentiment analysis.
SDG Meta-Score calculation
The data sources are classified into internal or external, according to whether or not it is voluntarily disclosed by the company. Internal and external scores for each SDG are computed.
Internal/External Score calculation
External and internal scores are obtained by aggregating the SDG scores, also taking into account the industry in which the company operates by means of materiality matrix.
Final FinScience ESG Score
The final FinScience ESG score is obtained by averaging internal and external scores, applying a penalty coefficient for those companies where internal and external disclosure results as conflicting.
Benefits from our integrated and modular approach:
To learn more about the use of Alternative Data for companies’ ESG assessment, become a free member of this LinkedIn group.
For Institutional Investors
Ad hoc projetcs
Thanks to the development of this solution, FinScience has gained considerable expertise and experience in machine learning custome projects for institutional clients in the financial sector. Learn more about our solutions dedicated to institutional investors.