S&P has the world’s largest database of assessments of the sustainability of companies’ practices and business models. Over 10,000 companies around the world are assessed each year.
This is done on the one hand by issuing industry-specific questionnaires with structured and unstructured questions, which is the most productive aspect of this method. Company reports and publicly available information are also compiled and analyzed on a regular basis.
ti&m has trained a range of machine learning models, which are now capable of comparing businesses' statements and responses with company reports and other sources. These models undertake repetitive tasks for analysts and significantly reduce the time needed to complete assessments.
for two specific types of questions: Finding relevant information in documents and evaluating responses from the “Material Issues” category, such as “Executive Compensation”.
Front-end / Back-end
solution for visualizing documents, incl. displaying the relevant text passages for different categories.
of all relevant documents referring to the company to be assessed.
Data Processing Pipeline & Integration
for extracting text from documents into the existing S&P application using OCR.
Management of machine learning models
to create and train new models for specific categories.
of software as microservice architecture
Training NLP models
for the specific domain without excessive data volumes.
The process first had to be adjusted so as to enable the analysts to generate structured data that could then serve as the ground truth for the models.
The many different question types (documents/quantitative responses/timelines/free text) mean that different models and approaches are required.
Prioritization was based on the time-saving potential for each question type.
The project was launched in collaboration with the Corporate Sustainability Assessment (CSA) department of RobecoSAM (2019), and was later taken over by and integrated into S&P Global (2020/21).
Million Data Points
Head of AI & Automation
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