Sustainability assessments with AI

Machine-Learning

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.

PoC

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.

Globale Search

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.

Technical implementation

of software as microservice architecture

01

Training NLP models

for the specific domain without excessive data volumes.

02

Data

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.

03

Approaches

The many different question types (documents/quantitative responses/timelines/free text) mean that different models and approaches are required.

04

Priority

Prioritization was based on the time-saving potential for each question type.