Anti-Money Laundering (AML) - Open source Libraries and Solutions

In August 2021, the U.S. Department of Justice repatriated an additional $452 million in 1MDB funds to Malaysia. This repatriation took the total amount recovered from the corruption scandal to $1.2 billion. The funds had been laundered through major financial institutions worldwide, including in the United States, Switzerland, Singapore, and Luxembourg.

While there are multiple global regulations targeting money laundering, multiple estimates peg the size of money laundering between $800 billion to $2 trillion annually. While there are multiple commercially available AML packages, the core technologies are the ability to stream-process vast sources of information and intelligent algorithms to identify money laundering patterns.

kandi collection on Anti-Money Laundering covers open source libraries across data scraping, stream processing, open source intelligence (OSINT Framework), anti-money laundering algorithms, and case management for anti-money laundering.

Scraping and Stream Processing

Key inputs for an AML solution are processing a variety of sources of information. Web scraping and real time streaming solutions help achieve this. For more insights on streaming refer to the kandi kit - Building a Stream Processing Application using open source libraries . For more insights on web scraping refer to the kandi collection - 13 best Python Web Scraping libraries in 2021.

Open Source Intelligence

A variety of open source intelligence tools enable targeting information gathering from multiple sources.

Anti-Money Laundering Algoritms

AML algorithms are key in identifying fraud patterns and flagging them.

Case Management for Anti-Money Laundering

Case management libraries process the alerts raised by the AML algorithms. Below are libraries spanning across general process management to ones tailored to legal and fraud management.