Thomas Havranek
Thomas Havranek
Partner CIN Consult, Vienna, Austria
Tax lawyer, CFE, Court Expert Witness,
first Austrian firm for white collar crime investigation, first project in Russia 1999, forensic & fraud investigations for major clients in finance, construction and technology industry. Assisting white collar crime investigations for prosecutors as cert. court expert witness since 25 years. Investigated for and with GA office in major bank fraud and money laundering case with AI and visualization technologies. Investigating OC related online gambling organizations.
Sessions
The term ‘human factors’ covers extra-legal, social, psychological, institutional and organisational aspects affecting the behaviour of citizens, including the fight against financial crime by competent authorities. In addition to the complexities imposed by different legal approaches across EU Member States and the limited resources available for countering financial crime, managing and converting big data into meaningful law enforcement action have also become major challenges for LEAs. As the recent Pandora Paper demonstrate, it is not only the volume and type of data which can overwhelm the authorities but also the sheer number of people, companies, business operations and assets that are scattered across the globe. This is where AI informed data analytics can be of use in analysing the vast amount of structured and unstructured data as well as connecting the dots between entities involved in a given scenario. Based on real case studies, this paper presents the potential benefits as well as challenges in adopting AI solutions to financial crime investigation models. In doing so, it offers a critical overview of the implications of some of the salient aspects of natural language process, entity extraction, correlation analysis, visualization & crowd knowledge. The paper also presents a some of the core legal and ethical principles that shall inform the development AND use of AI in law enforcement practices in Europe.