Combining Automation and Analytics to Detect Anti-Money Laundering
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Date
2022
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Publisher
UMT, Lahore
Abstract
The methods of money laundering are evolving and getting sophisticated day by day. With the advent of cryptocurrency and other methods to easily transfer money around the world, the Anti Money Laundering (AML) professionals are becoming increasingly shorthanded. The financial industry is under pressure to detect and prevent Anti-Money Laundering (AML) due to increasing strictness by Financial Action Task Force (FATF) to increase transaction scanning. Hence they are looking for ways to automate the process and make it more efficient. One way to achieve this is by combining transaction analytics with machine learning. This approach can be used to identify patterns that may indicate money laundering. The machine learning algorithm can be trained to recognize suspicious activity, and then the results can be reviewed by human analysts. In this way, the majority of transactions can be processed quickly and efficiently. Almost all banks now use some form of automation to help detect Anti-Money Laundering (AML). The goal is to combine automation with analytics to get the most accurate results. Financial institutions have been trying to do this for a few years now, but it has been a challenge. The main reason it has been difficult is that the data is coming from different sources and needs to be cleansed and normalized before it can be analyzed. This study aims to detect anti money laundering and suspicious transactions from transaction logs using machine learning algorithms and neural network models to provide an optimal solution for suspicious transaction decisions in financial institutions (FIs). In this paper, we have detected suspicious transactions using machine learning and artificial neural network algorithms along with using some network analytics and natural language processing that has achieved more than 95% accuracy. Thus we have tried to implement a solution that can be used along with rule-based models and as a result, reduces false positive suspicious transactions.