A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry.
SN Comput Sci
; 2(3): 215, 2021.
Article
en En
| MEDLINE
| ID: mdl-33880451
ABSTRACT
This paper presents a real-time fully autonomous prescriptive solution for explainable cyber-fraud detection within the iGaming industry. We demonstrate how our solution facilitates the time-consuming task of player risk and fraud assessment through prescriptive analytics. Our tool leverages machine learning algorithms and advancements in the field of eXplainable AI to derive smarter predictions empowered by local interpretable explanations in real-time. Our best-performing pipeline was able to predict fraudulent behaviour with an average precision of 84.2% and an area under the receiver operating characteristics of 0.82 on our dataset. We also addressed the phenomenon of concept-drift and discussed our empirical and data-driven strategy for detecting and dealing with this problem. Finally, we cover how local interpretable explanations can help adopt a pro-active stance in fighting fraud.
Texto completo:
1
Bases de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
SN Comput Sci
Año:
2021
Tipo del documento:
Article
País de afiliación:
Malta