High-precision anonymization of unstructured data promotes data utilization within the bank
MUFG Bank (Headquarters: Chiyoda-ku, Tokyo; President and CEO: Junichi Hanzawa; hereinafter, “the Bank”) has officially adopted the data anonymization solution from Toronto, Canada-based Private AI for its MUFG big data platform, “OCEAN.”
This implementation enables highly accurate automatic detection and anonymization of personally identifiable information (PII) and confidential information contained in previously difficult-to-use unstructured data, such as emails, call center call logs, internal documents, PDFs, and chat logs, before securely linking the data to “OCEAN.” This enables cross-sectional analysis on the company-wide data lake, as well as the use of generative AI and operational AI using unstructured data itself, all while ensuring security.
While financial institutions have seen advances in generative AI and data utilization in recent years, ensuring personal information protection and data governance have become important management issues. Private AI’s solutions are unique in that they can anonymize personal information in real time within an on-premises, closed environment without sending data to the cloud. MUFG will continue to fully deploy its generative AI and advanced data analysis platform while maintaining the strict governance of “OCEAN.”
In addition, prior to the official adoption, technical testing was conducted on a variety of unstructured files, simulating actual business operations, and high-precision anonymization performance sufficient for practical use was confirmed. Even for information categories that are particularly important to financial institutions, such as names, addresses, telephone numbers, account numbers, and health insurance card numbers, performance at a practical level was confirmed.
Starting with the application of this technology to “OCEAN”, the company plans to expand into multiple areas, including enhancing call center operations, enhancing fraud detection and risk management, and promoting the use of in-house knowledge, with the aim of building a next-generation data infrastructure that combines high levels of security and productivity.
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