ENTERPRISE-GRADE AML THREAT DETECTION USING TIME FREQUENCY SIGNALS AND SPRING BOOT MICROSERVICES
Keywords:
AML detection, time–frequency analysis, wavelet transform, Spring Boot microservices, Oracle database, STFT, financial anomaly detection, enterprise compliance systems.Abstract
The increasing sophistication of money launderingschemes requires financial institutions to adopt advancedanalytical systems capable of uncovering hidden, rapidlyevolving suspicious activity patterns. Traditional rule-based Anti-Money Laundering (AML) systems
References
R. Colladon and E. Remondi, “Monitoring money laundering through unsupervised learning,” Expert Systems with Applications, vol. 67, pp. 49–58, 2016.
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Published
2019-02-05
How to Cite
Naga Charan Nandigama. (2019). ENTERPRISE-GRADE AML THREAT DETECTION USING TIME FREQUENCY SIGNALS AND SPRING BOOT MICROSERVICES . Journal of Computational Analysis and Applications (JoCAAA), 26(2), 1–6. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4418
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