FinDEx: A Synthetic Data Sharing Platform for Financial Fraud Detection

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dc.contributor.author Karst, Fabian
dc.contributor.author Li, Mahei
dc.contributor.author Leimeister, Jan
dc.date.accessioned 2023-12-26T18:43:32Z
dc.date.available 2023-12-26T18:43:32Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other 35ff90db-8c26-412c-ace0-e0cf663c196b
dc.identifier.uri https://hdl.handle.net/10125/106897
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Designing Data Ecosystems: Value, Impacts, and Fundamentals
dc.subject data ecosystem
dc.subject data scarcity
dc.subject financial services
dc.subject fraud detection
dc.subject synthetic data
dc.title FinDEx: A Synthetic Data Sharing Platform for Financial Fraud Detection
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract The rising number of financial frauds inflicted in the last year more than 800 billion USD in damages on the global economy. Although financial institutions possess advanced AI systems for fraud detection, the time required to accumulate a sufficient volume of fraudulent data for training models creates a costly vulnerability. Combined with the inability to share fraud detection training data among institutions due to data and privacy regulations, this poses a major challenge. To address this issue, we propose the concept of a synthetic data-sharing ecosystem platform (FinDEx). This platform ensures data anonymity by generating synthesized training data based on each institution's fraud detection datasets. Various synthetic data generation techniques are employed to rapidly construct a shared dataset for all ecosystem members. Using design science research, this paper leverages insights from financial fraud detection literature, data sharing practices, and modular systems theory to derive design knowledge for the platform architecture. Furthermore, the feasibility of using different data generation algorithms such as generative adversarial networks, variational auto encoder and Gaussian mixture model was evaluated and different methods for the integration of synthetic data into the training procedure were tested. Thus, contributing to the theory at the intersection between fraud detection and data sharing and providing practitioners with guidelines on how to design such systems.
dcterms.extent 10 pages
prism.startingpage 4258
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Designing Data Ecosystems: Value, Impacts, and Fundamentals