Computational Discovery of Transaction-Based Financial Crime via Grammatical Evolution The Case of Ponzi Schemes

The financial sector continues to experience wide digitalization; the resulting transactional activity creates large amounts of data, in principle enabling public and private actors to better understand the social domain they operate on, possibly facilitating the design of interventions to reduce illegal activity. However, the adversarial nature of frauds and the relatively low amount of observed instances make the problem especially challenging with standard statistical-based methods. To address such fundamental issues to non-compliance detection, this paper presents a proof-of-concept of a methodological framework based on automated discovery of instances of non-compliant behaviour in a simulation environment via grammatical evolution. We illustrate the methodology with an experiment capable of discovering two known types of Ponzi schemes from a modest set of assumptions.