Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/319044 
Year of Publication: 
2025
Citation: 
[Journal:] Electronic Markets [ISSN:] 1422-8890 [Volume:] 35 [Issue:] 1 [Article No.:] 7 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2025
Publisher: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Abstract: 
Abstract Given the critical role of data availability for growth and innovation in financial services, especially small and mid-sized banks lack the data volumes required to fully leverage AI advancements for enhancing fraud detection, operational efficiency, and risk management. With existing solutions facing challenges in scalability, inconsistent standards, and complex privacy regulations, we introduce a synthetic data sharing ecosystem (SynDEc) using generative AI. Employing design science research in collaboration with two banks, among them UnionBank of the Philippines, we developed and validated a synthetic data sharing ecosystem for financial institutions. The derived design principles highlight synthetic data setup, training configurations, and incentivization. Furthermore, our findings show that smaller banks benefit most from SynDEcs and our solution is viable even with limited participation. Thus, we advance data ecosystem design knowledge, show its viability for financial services, and offer practical guidance for privacy-resilient synthetic data sharing, laying groundwork for future applications of SynDEcs.
Subjects: 
Synthetic data
Data sharing platform
Data ecosystem
Financial services
Data scarcity
Persistent Identifier of the first edition: 
Additional Information: 
M15
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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