Authors:
Fabien Vilar
1
;
Marc Le Goc
1
;
Philippe Bouche
2
and
Pierre-Yves Rolland
1
Affiliations:
1
Laboratory for Sciences of Information and Systems (LSIS), France
;
2
TOM4, France
Keyword(s):
Data Mining, Knowledge Engineering, Online and Real Time Fraud Detection, Fraud Modelisation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Representation Techniques
;
Soft Computing
;
Symbolic Systems
Abstract:
Internal frauds in the banking industry represent a huge cost and this problem is particularly difficult to solve
because, by construction, swindlers being very imaginative persons, the fraud schemata evolves continuously.
Fraud detection systems must then learn from the continuously new fraud schematas, making them difficult to
design. This paper proposes a new theoretical and practical approach to detect internal frauds and to model
fraud schematas. This approach is based on a particular method of abstraction that reduces the complexity of
the problem from O(n2) to O(n) making its implementation in a an Java program that detects and models the
frauds in real time and online with a simple professional personal computer. The results of this program are
presented with its application on a real-world fraud provided by a world wide French bank.