The Effects Of Artificial Intelligence In The Anti-Money Laundering

Blog / Artificial Intelligence and Anti-Money Laundering

Money laundering is the process of making large amounts of money generated by criminal activities as if it appears to have come from a legitimate source. Anti Money Laundering is a complex issue for financial institutions.

Relationship Between Artificial Intelligence and Anti-Money Laundering?

AML compliance is mandatory and there are sanctions such as fines for non-compliance institutions. AML compliance processes have been costly and complex for large companies and banks. The large amount and complexity of data and AML regulations cause time loss and increased costs. Despite the precaution taken, financial crimes are increasing. Financial institutions need more sophisticated tools to minimize the increased risks. The new technology discovered for this need is artificial intelligence. New technologies that continually learn, collect new information, and regulate processes can be developed with machine-learning-based artificial intelligence. It is expected to overcome the ever-increasing data density problem in AML. It can help find inconsistent customer transactions and suspicious transactions. AI can detect relationships that are too complex to be understood by rule-based monitoring or the human eye.

Even if the regulators support artificial intelligence, they are concerned about what can happen in the application part. They have not taken advantage of AI because of concerns with the “black box”. It is difficult to understand its transparency and what it does and why, because its inner workings are not fully understood. Regulators require a compliance officer to better interpret the outputs.

What Is The Machine Learning in Anti-Money Laundering?

Artificial intelligence and machine learning technologies have been in our lives for a while, recently these technologies have come to the fore in the world of financial services. Banks and financial services institutions have to comply with ever-more stringent regulations and the AML Compliance Program to anti-money laundering and terrorist financing. Machine Learning is believed to be a miracle solution for AML, according to the regulators. It tries to update and improve its obligations in the EU's Fifth Anti-Money Laundering Directive (5AMLD) and the US Patriot Act regulations.

The usefulness of machine learning is particularly important to reduce the number of false positives produced through conventional AML devices. In addition, reducing the false positives produced by Transaction Monitoring Software also benefits machine learning. AML / CFT areas are likely to be covered by these new technologies. As a result, new technologies such as artificial intelligence and machine learning play a potentially large role in the field of AML / CFT. For this reason, financial institutions need to enrich their AML / CFT devices with various artificial intelligence (AI) technologies and complete their transformation with new technologies in order to fight their competitors.

The Sanctions Scanner's AML Transaction Monitoring Software provides companies with end-to-end features to meet their anti-money laundering and terrorism financing obligations. Businesses can create their own rules and scenarios without the need for any coding knowledge, and through this feature, high-risk and suspicious activities can be automatically detected.

The Effects Of Artificial Intelligence On The Anti-Money Laundering

Continuously monitor customer activities. By learning normal customer behavior, he can easily notice unusual behavior and transaction. It can even find the differences that can be unnoticed by the human eye.

  •  It can reduce the cost of AML compliance audits.
  •  It can reduce the number of false-positive alerts.
  •  It can provide to find money laundering methods that are too complex to be understood by the human eye.

AI is still a developing technology and it is unknown what will be happened and how will be used, by the banking and financial services sector. As a result, financial institutions are turning to new technological solutions to increase cyber and financial crimes.

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