A synthetic fraud data generation methodology
Paper in proceedings, 2002

In many cases synthetic data is more suitable than authentic data for the testing and training of fraud detection systems. At the same time synthetic data suffers from some drawbacks originating from the fact that it is indeed synthetic and may not have the realism of authentic data. In order to counter this disadvantage, we have developed a method for generating synthetic data that is derived from authentic data. We identify the important characteristics of authentic data and the frauds we want to detect and generate synthetic data with these properties.

data generation methodology

fraud detection

system simulation

user simulation

synthetic test data

Author

Emilie Lundin

Chalmers, Department of Computer Engineering

Håkan Kvarnström

Chalmers, Department of Computer Engineering

Erland Jonsson

Chalmers, Department of Computer Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 2513 265-277

Subject Categories

Computer Engineering

DOI

10.1007/3-540-36159-6_23

ISBN

3-540-00164-6

More information

Created

10/7/2017