Money Laundering Identification Using Risk and Structural Framework Estimation

Abstract

Money laundering refers to activities that disguise money receive through illegal operations and make them legitimate. It leaves serious consequence that may lead to economy corruption. One such problem consisting large amounts of money transferring through various accounts by the same person or entity is Money Laundering. Money laundering scheme is quite a complex procedure. It receipts some empathetic of the deposit transporting actions at many phases. Detecting money laundering activities is a challenging task due we propose a risk model framework in Structural Money Laundering based on Risk Evolution Detection Framework (SMLRDF). The connection deceitful deal trails a sequence of connected money laundering arrangements, structural money laundering uses sequence matching, social network investigation, and multifaceted happening processing, case-based examination. The context that put on case discount approaches to increasingly lessen the input data set to a knowingly minor size. The context images the summary data to discovery couples of communications through common qualities and performances that are possibly complicated in ML actions. It then applies a clustering method to detect potential Money Laundering (ML groups), then the risk model is used to create a valid and accurate transaction scoring system to be utilized in an ML prevention system. SMLRDFdependent risk modeling, which captures the hidden, and dynamic, relations among none-transacted entities. SMLRDF has components to collect data, run them against business rules and evolution models, run detection algorithms and use social network analysis to connect potential participants.