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As GLBA has functioned, it is scalable, so the risk that a multinational institution has is going to much different than a small credit union and the risk assessment is much different, but everybody is on the same page.”.
Interaction with other laws The final rule includes a discussion on how it interacts with other existing laws, such as the Fair Credit Reporting Act (FCRA) and the Gramm-Leach-Bliley Act (GLBA).
In short, concerns with GLBA, GDPR , PCI, reputational and headline risks tied to raw data can literally “all go away with the use of synthetic data.”. “When you talk about data monetization,” he said, “everyone thinks of raw data,” and the risk inherent with that data. Those fears, he added, can be solved through the use of synthetic data.
USA: Stricter data-sharing laws, including the Gramm-Leach-Bliley Act (GLBA) and inter-agency reporting for suspicious activity over $3,000 USD. Data Privacy & Cross-Border Compliance Australia: The Privacy Act 1988 applies, with additional AUSTRAC reporting for cross-border transactions over $10,000 AUD.
Examples include the General Data Protection Regulation (GDPR) in the European Union and the Gramm-Leach-Bliley Act (GLBA) in the United States. Regulatory Compliance: Payment systems may also need to comply with specific regulations and laws related to financial transactions and data protection, depending on the region and industry.
“Additionally, the Gramm-Leach-Bliley Act (GLBA) has been a long-standing concern for FIs and will continue to be for FIs using NPI to develop and train models. Navigating these regulations will be important from a deployer and developer perspective as banks balance efficient decision-making with demonstrable fairness.
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