setsci

SETSCI Conference Proceedings

2687-5527

SET Technology

10.36287/setsci.24.4.030

aaf5b637-5e02-45bd-99a8-de6e9994687e

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

Thota

Madhu

Bayrak

Tuncay

Western New England University

08 09 2025

24 30 38

2025

SET Technology

CC BY 4.0

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.

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

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