Monetary Smurfing Analytics Model using ML

Abstract

The act of taking money obtained from illegal operations, such as drug trafficking, and disguising it as profits from a legitimate business venture is known as money laundering. The money obtained through illegal action is regarded as dirty, and to make it appear clean, it is "laundered." Money laundering is a major concern to the country these days, as well as financial institutions. The sophistication of this illegal activity is growing; it appears to have beyond the tired old trope of drug trafficking to include financing terrorism and, of course, personal benefit. Research on anti-money laundering is crucial since money laundering is a global issue that seriously jeopardizes international security and financial stability. Furthermore, it's estimated that the banking system seizes only 0.2% of the money that has been laundered. The crime itself is growing more sophisticated and intricate, and banks are becoming more vulnerable as a result of its ongoing volume amplification. Considering the role that banking institutions play in the money-laundering industry, practitioners and researchers are becoming more interested in creative ways to solve problems and enhance anti-money-laundering efforts. Researchers are starting to look into the viability of artificial intelligence methods in this setting. A systematic knowledge deficit concerning a thorough study that meticulously examines and combines artificial intelligence methods for countering money laundering in the banking industry was discovered, though. To combat investment fraud, the majority of global financial institutions have been putting anti-money laundering measures into place. Data mining tools have emerged recently and are thought to be effective methods for identifying instances of money laundering.