Anti-money laundering (AML) regulations refer to regulations that protect institutions from money launderers who obtain funds illegally and show them as legitimate income. Once a money launderer has obtained funds illegally and converted them into legitimate income, there is not much to be done. Retail and commercial banks are the most vulnerable to money laundering. Financial institutions often rely on AML analytics to minimise associated risks. In this article, we discuss ways to reduce AML risk to your organisation.
Beware Of Money-laundering Techniques
Traditional money-laundering techniques have been identified and addressed, and measures taken to safeguard institutions from them, making criminals come up with new techniques for money laundering. However, the more you are aware of these, the better your chances of neutralising a money-laundering attempt. For example, a launderer could deposit illegal funds in a bank account or convert them into financial instruments.
Money laundering may involve businesses that have little paperwork. Examples of such types of business are online auction sites, currency exchange firms and art galleries. The best point to stop a money-laundering operation is before it happens. Once the launderer has claimed the funds legally, more investigative effort is required to expose it. Knowing these techniques can help safeguard your organisation.
You could use judiciary and regulatory resources to educate your team about money-laundering techniques. Police websites offer ample resources on the latest money-laundering techniques. If you run a commercial bank, educate your team on how to identify money launderers on sight.
Focus on AML Analytics
Money laundering is one of the biggest threats to financial institutions. Many countries have compiled AML compliance guidelines for companies to follow. A reliable surveillance system needs to be established to ensure compliance with such guidelines. The importance of AML analytics is increasing as AML risks rise. Advanced AML analytics can help find anomalies much faster than a human can and reduce associated risks.
Data analytics is used to find outliers in any particular dataset. In terms of finance, illegal or suspicious transactions can be identified by an AML analytics system. Other new-age technologies such as artificial intelligence and machine learning are also used to automate the AML analytics system. Once a money-laundering event is discovered, AML analytics can help employ better investigative efforts. The AML analytics system is compiled after model testing and validation. The analytics system studies a number of real-life scenarios to understand patterns and identify a money-laundering event quickly.
Installing a reliable surveillance system within an organisation is of utmost importance. Predictive analysis can be used to identify anomalies and take safety measures. For example, a retail bank could ensure that each customer applying for a loan pass an AML risk assessment first.
Outsource Aml Process To A Reliable Firm
Hiring experts in AML compliance and analytics would require spending on recruitment and training. Outsourcing your AML processes to a reliable third party with subject experts experienced in identifying money-laundering activity would optimise cost.
A third-party firm could help you with AML processes such as flagging transactions, documenting findings and post-transaction review. It could help you conduct due diligence or background checks to identify the authenticity of the client or customer, especially if they are depositing large sums of money in your commercial bank.
