Information extraction of regulatory enforcement actions: From anti-money laundering compliance to countering terrorism finance

Financial fines imposed by regulatory bodies to penalize illegal activities and violations against regulations (cases of non-compliance) have recently become more common, and the sizes of fines have increased. This development coincides with the ongoing increase of complexity of regulatory rules. Huge fines have been imposed on banks for financial fraud and regulations have been made more stringent after 9/11 to curb funding of terrorist groups. Market players would also like to have available a database of fine events for a range of applications, such as to benchmark their competitors performance, or to use it as an early warning system for detecting shifts in regulators' enforcement behavior. To this end, we introduce the task of extracting fines from regulatory enforcement actions and we present a method to extract such fine event instances from timeline-like descriptions of regulatory investigation activities authored by legal professionals for a commercial product. We evaluate how well a rule-based method can extract information about fine events and we compare its performance to a machine-learning baseline. To the best of our knowledge, this work is the first one addressing this task.