International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
TRANSFORMING ANTI-MONEY LAUNDERING COMPLIANCE IN BANKING WITH AI-DRIVEN ROBOTIC PROCESS AUTOMATION Ramesh Pingili ItechUS Inc., USA ----------------------------------------------------------------------***---------------------------------------------------------------------------
ABSTRACT Anti-Money Laundering (AML) compliance can be quite a challenge for banks. The regulations are complex, and they must handle many transactions, making everything even trickier. Traditional anti-money laundering (AML) processes can be demanding and often depend heavily on human input, making them susceptible to mistakes and leading to many false positives. This paper looks at how AI-driven Robotic Process Automation (RPA) can change the game for AML compliance. By automating the detection of suspicious activities, AI makes operations more efficient and helps lower the number of false alarms. With tools like machine learning (ML) and natural language processing (NLP), AI can sift through enormous datasets in real-time, effectively spotting potential risks with much greater accuracy. The study investigates how some banks have successfully adopted AI-driven robotic process automation (RPA), which has helped them reduce compliance costs and improve their adherence to regulations. Keywords: Anti-Money Laundering (AML), AI-driven RPA, Banking Compliance, Machine Learning, Natural Language Processing, Transaction Monitoring, Financial Crime Detection, Data Privacy
1.INTRODUCTION The banking sector is under increasing pressure to improve compliance processes due to ever-changing anti-money laundering (AML) regulations [1], which are essential to safeguard the financial system against money laundering and terrorist financing. Traditionally, banks have relied on manual methods such as transaction monitoring and customer due diligence. However, these approaches often produce a high number of false positives, which can increase compliance costs and regulatory risks.
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