Open Access

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

Madhu Thota1*, Tuncay Bayrak2
1Western New England University, Springfield , USA
2Western New England University, Springfield , USA
* Corresponding author: madhu.thota@wne.edu

Presented at the International Conference on Open Finance (ICOF2025), Springfield, USA, Aug 28, 2025

SETSCI Conference Proceedings, 2025, 24, Page (s): 30-38 , https://doi.org/10.36287/setsci.24.4.030

Published Date: 08 September 2025

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.

Keywords - Open Finance, Regulatory Risk, Predictive Analytics, Financial Compliance, Risk Management Framework, Data-Driven Decision Making

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