Since regulators and governments do not have the power to crack down on money laundering effectively, the onus is on companies and software providers to step up.
An excellent way to make rule coordination more effective is to encourage the testing of AI-based AML platforms. This will allow companies to test their AI analysis against historical data without fear of prosecution, even if the new data reveals past offenses.
All financial professionals should receive certificates to comply with regulations. AML certification is given by the first and only company, offering online AML/CTF training specific to different business areas and jurisdictions.
General Information
The company considers the laws and peculiarities of doing business in specific countries and works to create courses that meet the requirements of regulatory bodies. A team of professional trainers is involved in the creation of courses. The company has a unique technology for finding trainers who are most specialized in specific business areas.
Several large banks have already set up labs to test AI-based technologies. To make a real difference, artificial intelligence must become a universal solution. A rapid transition to AI-based solutions, backed by a technological amnesty, will help countries fight financial criminals more effectively.
Ways AI Can Take on Money Launderers More Effectively
Compliance monitoring
AI is more than a set of organized rules; AI-based platforms can sift through more data and adapt without human guidance.
Money laundering techniques are constantly changing. Compliance officers often need to learn precisely what they are looking for. With the sheer number of transactions going through bank accounts every day, there needs to be more time to identify patterns of illicit transactions.
Machine learning algorithms could change the approach to finding suspicious transactions. And artificial intelligence programs can do it instantly, testing and adapting thousands of times faster than humans.
Reduce false positives
Legacy systems were well suited to deal with the mountains of paperwork compliance officers had to do. Their job was to sift through all the suspicious computer-generated transactions.
The problem is that each “red light” has to be individually verified by a natural person, even if most transactions are perfectly normal. This task is not impossible. But it is certainly time-consuming. Even worse, because of the sheer volume of work, real threats can go undetected for days or weeks.
We know that there are better ways to analyze transactions to identify suspicious behavior. Modern technologies, such as AI, are much more advanced and, if used correctly, can significantly reduce false positives.
Improved Analytics
This problem applies to older systems that currently have fewer false positives. Information from accurate transaction monitoring analysis is at the forefront of the fight against financial crime. However, law enforcement agencies receive more suspicious activity reports (SARs) than they can process.
The rise in SARs may be attributed to the following factors:
- The desire of those responsible for enforcing anti-money laundering laws to demonstrate their efforts in detecting fraudulent activity.
- Ineffective trade monitoring systems generating excessive alerts.
- Investigation and reporting of many (false) indications, suggesting over-reporting.
SARs are essential to help law enforcement agencies fight financial crime, but over-reporting hinders efforts on all fronts.
AI will allow companies to dismiss unnecessary SARs confidently, allowing analysts to focus their attention on transactions that are more worthy of investigation.
In other words, fewer alerts will allow more time spent on deeper investigations. In short, more accurate information will be shared, strengthening law enforcement’s position in the fight against financial crime.
Conclusion
Banks, as gatekeepers of the financial system, must develop and implement policies and practices to mitigate the money laundering risks applicable to their banks. AI enables banks to take anti-money laundering (AML) measures faster, cheaper and more efficiently. AI identifies risks and helps respond to, report, and monitor suspicious activity more effectively, ultimately allowing banks to maintain AML compliance.
