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Research Article Peer ReviewedAt least two reviewers Open Access

Predictive Analytics for Regulatory Risk in Open Finance: A Data-Driven Framework

1Western New England University
✉ Corresponding author: Madhu Thota · madhu.thota@wne.edu
Proceedings International Conference on Open Finance Volume 24 Pages 30–38 Year 2025
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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.

Research Contributions (2019+)Peer reviewed by at least two reviewers
Open AccessCC BY 4.0
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