WeeFin x FactSet webinar: leveraging geographical and sectoral sustainability data

On 11 December 2025, WeeFin hosted its fifth webinar in a series dedicated to sustainability data providers, with FactSet as its guest. Marion Aubert, co-founder of WeeFin, spoke with Pascal Naim-Schmidt, Vice President and Principal Sustainability & Thematics Specialist at FactSet, about the RBICS and GeoRev datasets.

Replay is available here.

Financial players increasingly need accurate and granular data to analyse companies' sectoral and geographical exposure. This webinar provided an in-depth exploration of two key datasets offered by FactSet: RBICS (Revere Business Industry Classification System) and GeoRev (Geographic Revenue Exposure).

GeoRev: standardising geographic data

One of the major challenges for investors is that companies report their geographic revenue breakdowns in a non-standardised manner. As Pascal Naim-Schmidt explained: ‘Companies are required to report revenue breakdowns by geographic region, but how they define those regions is entirely up to them.’

GeoRev provides a solution to this problem by:

• Collecting geographic data reported by companies
• Using a proprietary algorithm based on GDP weighting to estimate revenue exposures down to the country level

This tool allows investors to quickly assess, for example, which companies in their portfolio might be most affected by geopolitical risks specific to certain regions.

RBICS: a multi-level sector classification

The second flagship dataset presented during the webinar is RBICS, which solves another major challenge: the sector classification of companies. Unlike traditional classification systems, which generally assign a single sector to a company, RBICS offers a multi-sector approach.

Key features of RBICS include:

• A six-level taxonomy, ranging from 14 ‘economies’ at the top level to more than 1,900 sub-industries. FactSet uses its own classification.
• Two main datasets: RBICS with revenues (mapping of reported revenues) and RBICS with trade names (mapping of products and services)

A rigorous approach to data collection

One particularly important aspect discussed during the webinar concerns the methodology for collecting and verifying data. FactSet relies exclusively on primary sources: annual reports, regulatory filings, press releases and company websites.

The process remains largely manual, with around 250 sector specialists analysing the information and mapping it as granularly as possible. As Pascal Naim-Schmidt explained: ‘Each sector is very specific in itself. And if you know the companies in the sector, you know what a certain item or footnote means – it's really nuanced information that only experts can truly understand.’

This approach ensures data reliability, with a confidence score assigned to GeoRev data in particular to indicate the degree of reliability of the estimates.

Applications for sustainable finance

These datasets can be used in various ways in the field of sustainable finance:

1. ESG exclusions and inclusions: Identify companies with exposure above a certain threshold in controversial sectors or, conversely, those that contribute positively to the sustainable transition.
2. ESG rating frameworks: Define which sectors are considered green, brown or neutral and build scoring methodologies.
3. Regulatory compliance: Determine whether a company is active in a specific sector, which is often required by regulatory frameworks.
4. Value chain analysis: Understand not only a company's direct activities, but also its network of suppliers and customers
   

This webinar demonstrated once again the importance of having accurate and granular sectoral and geographical data for effective and compliant sustainable finance. As Marion Aubert pointed out in her conclusion, these tools enable investors to make ‘more informed decisions and be more transparent themselves to their clients.’

FactSet's RBICS and GeoRev datasets aim to provide a robust solution to these challenges, with an approach that combines human expertise and advanced technologies to ensure data quality and relevance.