Balancing Data Privacy and Software Testing Efficacy in AML Financial Systems

Praveen Kumar

Abstract: This paper examines the inherent tension between comprehensive software testing and data privacy requirements in Anti-Money Laundering (AML) financial systems. With increasing regulatory scrutiny on both financial crime prevention and data protection, financial institutions face the dual challenge of ensuring robust software quality while maintaining strict compliance with privacy regulations like GDPR, CCPA, and financial data protection laws. Through analysis of current practices, regulatory frameworks, and emerging technologies, we propose a balanced approach that leverages privacy-preserving testing methodologies without compromising testing efficacy. Our framework incorporates synthetic data generation, dynamic data masking, containerized test environments, and privacy-by-design principles to enable thorough testing while minimizing privacy risks. Case studies demonstrate how leading financial institutions have successfully implemented these strategies, achieving both robust AML system performance and stringent data protection compliance.

Keywords: AML software testing, data privacy compliance, synthetic data, privacy-by-design, financial regulations

How to Cite?: Praveen Kumar, "Balancing Data Privacy and Software Testing Efficacy in AML Financial Systems", Volume 14 Issue 5, May 2025, International Journal of Science and Research (IJSR), Pages: 619-623, https://www.ijsr.net/getabstract.php?paperid=SR25505094550, DOI: https://dx.doi.org/10.21275/SR25505094550