Predictive Analytics for Regulatory Risk in Open Finance: A Data-Driven Framework
Abstract
The emergence of Open Finance is reshaping the global financial landscape by enabling greater data accessibility, interoperability, and innovation. However, this expanded connectivity introduces new layers of regulatory complexity, raising urgent concerns regarding compliance and systemic risk. This paper proposes a data-driven predictive analytics framework to assess and mitigate regulatory risks within Open Finance environments. Leveraging empirical data from the World Bank’s GFDD, WDI, and WGI datasets, the study applies an integrated suite of analytics—descriptive, diagnostic, predictive, and prescriptive—to examine interdependencies among financial, macroeconomic, and governance indicators.
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Cite this article
Thota, M., & Bayrak, T. (2025). Predictive Analytics for Regulatory Risk in Open Finance: A Data-Driven Framework. In International Conference on Open Finance (pp. 30–38). SET Technology. https://doi.org/10.36287/setsci.24.4.030
Article History
- Published
- 8 Sep 2025
License
© 2025 SET Technology. This article is distributed under the terms of the CC BY 4.0.