SynDEc: A Synthetic Data Ecosystem
Journal
Electronic Markets
Type
journal article
Date Issued
2025-01-25
Author(s)
Research Team
IWI6
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.
Language
English
Keywords
Synthetic Data
Data Sharing Platform
Data Ecosystem
Financial Services
Data Scarcity
HSG Classification
contribution to scientific community
Refereed
Yes
Volume
35
Number
1
Pages
28
Division(s)
File(s)![Thumbnail Image]()
Name
JML_1007.pdf
Size
2.33 MB
Format
Adobe PDF
Checksum (MD5)
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