According to a report by the Police University College of Finland, the fight against corruption and money laundering is still quite decentralised, although the two phenomena are clearly interlinked and form a "vicious circle". Furthermore, anti-corruption measures also differed between the Nordic countries and between authorities and companies.
The research project, funded by the Nordic Council of Ministers and commissioned by the Ministry of Justice, examined the links between corruption and money laundering in the Nordic countries. According to the study, Nordic corporations or financial institutions can be exploited in corruption or money laundering schemes in order to whitewash corrupt funds, even though there is relatively little visible corruption, such as bribery, in the Nordic countries. There are few convictions for corruption in international business and identifying cases is very challenging.
The project also explored the potential to integrate good anti-money laundering practices in the fight against corruption. The research found that the obliged entities mentioned in the anti-money laundering regulation are key identifiers of corruption. The financial sector, and financial management in particular, has potentially a wide range of tools available to combat and detect misconduct. These tools could be used more effectively if the fight against corruption was more risk-based and more specifically stated in the law.
In addition, the project made recommendations to the Nordic countries for improving the fight against corruption. The recommendations concern the harmonisation and clarification of the fight against corruption, more efficient reporting and whistleblowing on suspected misconduct, consideration of administrative sanctions and official controls as part of the fight against corruption, development of training and cooperation and the use of new technologies.
The final seminar brought together European experts to discuss the challenges of identifying corruption. Participants saw especially new technological solutions using artificial intelligence as very promising, provided that the quality of the data they use is improved. In addition, there is a need to lower the reporting threshold and provide education on the importance of ethical responsibility.
