Year: 2025
Type: article
Abstract: This paper provides insight into typical money laundering typologies used in the financial crime domain and provides a concrete set of methods through the use of which fraudulent transactions may be classified using traditional machine learning algorithms and proving the efficacy of tree-based models in not only predictive power, but also explainability and ease of interpretation of results.
Source: Informatica Economica
Authors: Petre-Cornel GRIGORESCU, Antoaneta AMZA
Institution: Bucharest University of Economic Studies
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412199963