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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.

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.

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.

Alternative data to include sustainability into investment decisions

Alternative Data to measure sustainability

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.

To learn more about the use of Alternative Data for companies’ ESG assessment, become a free member of LinkedIn group.

Benefits from our integrated and modular approach:

  • Combination of company self-disclosed information and external alternative data to provide a 360° complete and accurate view of its ESG profile.
  • Weekly updates on social media and news data related to single companies and ESG themes.
  • Integration of digital diffusion and sentiment analysis in the ESG scoring process.

An ESG solution to support Institutional Investors and Corporates

Asset/investment managerspension and hedge funds in monitoring corporate ESG performance for ESG integration in portfolio management.

Companies to: monitor ESG reputation and communication effectiveness; monitor and integrate stakeholder perceptions into corporate reporting and business strategies; and benchmark against industry competitors on their contribution to individual UN SDGs.

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

1. Data collection

Data are automatically collected from the different sources described below with different frequency (daily, weekly or annually).

2. Conversion into SDG-related indicators

Textual content is first analyzed via Natural Language Processing tools such as text classification, entity extraction and sentiment analysis.

3. 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.

4. 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.

5. 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.

For institutional investor

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.

Discover the added value of Alternative Data