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Informative Article | Computer Science & Engineering | India | Volume 9 Issue 9, September 2020 | Popularity: 4.8 / 10

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Comprehensive Review on Automated Suspicious Activity Report Generation (SAR)

Ankur Mahida

Abstract: The focus of this detailed assessment is to offer insightful information to financial institutions in the process of detecting and reporting suspected cases of money laundering and terrorist financing through automated suspicious activity reporting (SAR). Financial institutions have the legal responsibility of monitoring and reporting suspicious financial activities to regulatory authorities to keep the financial system safe from money laundering, terrorist financing, and other financial crimes. But the hand - on approach from earlier times to identifying and reporting the suspicious activities has become increasingly difficult due to the huge volumes of financial transactions, the complexity of money laundering schemes, and the constantly changing techniques used by criminals. Automated SAR Generation deals with these problems using big data analytics, machine learning, and artificial intelligence that analyze financial data which reveal patterns of suspicious activities and generate SAER promptly and accurately. The automation approach in reporting not only improves the efficiency and effectiveness of the process but also increases compliance with AML regulations and decreases the possibility of fines and reputational damage for financial institutions. The review will deal with the problem statement, solutions, uses, impact, and scope of Automated SAR Generation. The review will illustrate the significance of Automated SAR Generation in fighting financial crimes and ensuring compliance with anti - money laundering (AML) regulations.

Keywords: Automated Suspicious Activity Report (SAR) Generation, Anti - Money Laundering (AML), Financial Crimes, Compliance, Suspicious Activity Detection

Edition: Volume 9 Issue 9, September 2020

Pages: 1607 - 1610

DOI: https://www.doi.org/10.21275/SR24314131130

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