Data analytics is increasingly seen as a promising tool for effective integrity and corruption risk management, as it can support a robust due diligence process and early identification of potential integrity-related risks. Consequently, companies are interested in making better use of their internal data to monitor integrity risks and identify new threats.

With this in mind, the Internal Integrity Risk Warning System (IIRWiS) project aims to advance integrity management by applying methods developed for Deep Learning and natural language processing to the integrity domain. The project will develop machine learning models based on text-based data sources (internal documents and digital communications) that are capable of automatically recognising integrity-related behaviours. In addition, the project will evaluate the challenges of analysing internal company data and explore options for action for an ethical approach.

Parties involved

The project has been implemented by the Swiss Institute for Entrepeneurship (SIFE), in cooperation with the Swiss Institute for Information Science (SII) and the PRME Business Integrity Action Center