Computational Discovery of Transaction-Based Financial Crime via Grammatical Evolution

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

Fratric, P.
Sileno, G.
van Engers, T.
Klous, S.

Ajmeri, N. (editor)
Morris Martin, A. (editor)
Savarimuthu, B.T. (editor)

2022

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. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Computational grammars
Genetic algorithms
Automated discovery
Financial crime
Financial sectors
Grammatical evolution
Illegal activities
Large amounts of data
Methodological frameworks
Non-compliance
Proof of concept
Social domains
Crime

http://resolver.tudelft.nl/uuid:5b1e6a49-ec79-49a5-ae41-7840d3a43c23

980924

Springer Science and Business Media Deutschland GmbH

9783031208447

0302-9743

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13549 LNAI (13549 LNAI), 109-120

conference paper