Artificial intelligence a key component in fighting money laundering in region

In recent years, there has been a growing recognition of the need to combat money laundering in the Middle East and North Africa (Mena) region.

As a result of this growing recognition, many governments in the Mena region have enacted new AML laws and regulations. These laws and regulations have increased the reporting requirements for financial institutions and other businesses, and they have created new enforcement mechanisms. Additionally, many governments in the region have established financial intelligence units (FIUs), which are responsible for collecting and analyzing financial information that may be related to money laundering.

Saudi Arabia’s Mozn, a market leader in enterprise artificial-intelligence technologies, last month announced a regional expansion that will take the company into the AI, finance and fintech markets of the UAE and the GCC.

Dr. Mohammed Alhussien, CEO & Founder, Mozn, spoke to Khaleej Times on the the anti-money laundering (AML) steps being taken in the region how the company is facilitating businesses on this front.

Excerpts from the interview:

How do you see the current AML measures being taken by governments in the region?

Anti-Money Laundering (AML) measures are an integral part of any financial system’s integrity and security.

Here are some of the specific measures we see strengthening any financial ecosystem when it comes to AML:

● The enactment and enforcement of comprehensive AML laws and regulations.

● Increasing cooperation between different markets and understanding the local and global standing when it comes to AML measures.

What is the impact of AI on AML compliance?

Artificial Intelligence (AI) has been a significant game-changer in the field of AML compliance.

● Enhanced Transaction Monitoring: AI can analyse vast amounts of transactions in real-time, identifying patterns and flagging suspicious activity much more quickly and accurately than traditional methods. This enables financial institutions to detect potentially illicit activities promptly and respond appropriately.

● Reduced False Positives: Traditional AML systems are known for generating a high number of false positives, which are legitimate transactions mistakenly flagged as suspicious. This leads to a significant workload for compliance teams who must investigate these alerts. AI, through machine learning algorithms, can improve the accuracy of these systems, leading to fewer false positives and more efficient use of resources.