Options
Synthesizing Training Data with Generative Adversarial Networks: Towards the Design of a Data-Sharing Ecosystem Platform for Fraud Detection
Journal
International Conference on Design Science Research in Information Systems and Technology (DESRIST)
Type
conference paper
Date Issued
2023-06-02
Author(s)
Research Team
IWI6
Abstract
Financial fraud has a severe impact on the general population. While financial institutions have technological capabilities for fraud detection using intelligent AI systems, the delay until they have collected a sufficient size of fraudulent data to train their fraud detection models creates a costly vulnerability. One major challenge for quickly training data lies in the inability to share fraud detection training data with other financial institutions, due to data and privacy regulations. Thus, we create the concept for a data-sharing ecosystem platform that addresses data anonymity concerns by creating synthesized training data based on each institution’s fraud detection training data sets. We rely on the advantages of generative adversarial networks (GAN) to quickly construct a shared dataset for all ecosystem members. Applying design science research, this paper derives design knowledge based on financial fraud detection literature, data sharing between financial institutions, GANs and modular systems theory for the design of a plat-form architecture for data-sharing ecosystems.
Language
English
Keywords
data sharing
fraud detection
platform
ecosystem
generative adversarial networks
synthetic data
training data
HSG Classification
contribution to scientific community
Publisher place
Pretoria, South Africa
Event Title
International Conference on Design Science Research in Information Systems and Technology (DESRIST)
Event Location
Pretoria, South Africa
Event Date
31 May - 02 Jun 2023
Subject(s)
Division(s)
Eprints ID
269455