The Special Investigation Service (STT), after completing an international project on Big Data analysis for fraud and corruption investigations in 2022, is launching a publication that provides a structured overview of the training courses on identifying corruption and fraud risks using big data analysis. It focuses on strategy development, data availability and other measures needed to better protect the financial interests of the European Union (EU).
The successful implementation of the European Anti-Fraud Office (OLAF) project “Enhancing the analytical capacity of law enforcement authorities to detect and prevent fraud and corruption affecting the financial interests of the EU” has provided the participants with the necessary knowledge to understand the Big Data analysis strategies, structure, quality assurance, when processing and analysing various types of big data, which has improved the capacity of law enforcement officers in ensuring the protection of the financial interests of the EU and in the investigation of corruption-related offences.
“This project allowed analysts from Lithuanian and foreign law enforcement agencies not only to deepen their knowledge and exchange best practices, but also to analyse in more detail the possibilities offered by Big Data analysis tools in detecting corruption-related crimes. 44 participants from 18 countries listened to lectures by competent lecturers and were able to put their knowledge into practice. We are delighted that we can now share key insights and training material”, said Rūta Kaziliūnaitė, Deputy Director of the STT and Project Manager.
The project contributed to the activities of the EPAC/EACN Working Group on Big Data, an international organization uniting more than 100 European anti-corruption authorities and police oversight bodies The project is funded by the OLAF-Hercule III programme, which promotes the exchange of knowledge and good practice in the identification and management of corruption and fraud risks detrimentally affecting EU financial interests.