Harnessing the Web and Knowledge Graphs for Automated Impact Investing Scoring

TL;DR

This paper presents a data-driven system that automates the creation of SDG scoring frameworks for companies by leveraging web data, knowledge graphs, and machine learning classifiers, significantly reducing cost and effort.

Contribution

It introduces a novel method for collecting web and knowledge graph data and trains classifiers to predict SDG alignment scores, streamlining impact investing assessments.

Findings

Achieved a micro F1 score of 0.89 in SDG score prediction.

Automated SDG scoring reduces time and cost compared to manual methods.

Provides explainable predictions with relevant data for human analysts.

Abstract

The Sustainable Development Goals (SDGs) were introduced by the United Nations in order to encourage policies and activities that help guarantee human prosperity and sustainability. SDG frameworks produced in the finance industry are designed to provide scores that indicate how well a company aligns with each of the 17 SDGs. This scoring enables a consistent assessment of investments that have the potential of building an inclusive and sustainable economy. As a result of the high quality and reliability required by such frameworks, the process of creating and maintaining them is time-consuming and requires extensive domain expertise. In this work, we describe a data-driven system that seeks to automate the process of creating an SDG framework. First, we propose a novel method for collecting and filtering a dataset of texts from different web sources and a knowledge graph relevant to a set of companies. We then implement and deploy classifiers trained with this data for predicting scores of alignment with SDGs for a given company. Our results indicate that our best performing model can accurately predict SDG scores with a micro average F1 score of 0.89, demonstrating the effectiveness of the proposed solution. We further describe how the integration of the models for its use by humans can be facilitated by providing explanations in the form of data relevant to a predicted score. We find that our proposed solution enables access to a large amount of information that analysts would normally not be able to process, resulting in an accurate prediction of SDG scores at a fraction of the cost.