5 Misconceptions About AI-Based AML Programs

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Idan Keret

Written by:

Idan Keret

Chief Customer Officer ( CCO )

The industry is on a search for new technologies to manage AML compliance, with AI top of mind now that fintech events are taking place again around the world. Hesitation around making the transition from a rules-based to an artificial intelligence-driven solution can be caused by misconceptions about the methodology of an AI-based program. 

Here are 5 misconceptions that can stand in the way of making the transition and explanations that can be used when presenting AI as a solution to regulators.

Too much dirty data will prevent the transition.

Much like a human brain that operates non-linearly, advanced AI solutions are programmed to analyze data from multiple angles and sources. This means that small anomalous or incorrect data present no real deterrent for advanced AI. In fact, advanced AI can bypass dirty data issues such as duplicate entries, misspelled words, or outdated data and enable financial institutions to make the transition to more effective AML systems.

My regulator will object.

The FATF, the global anti-money laundering watchdog, actually encourages regulators and financial institutions to adopt new and advanced technologies for AML/CTF such as artificial intelligence and machine learning for more efficient and effective use of resources to detect financial crimes. In fact, top 100 banks and top 100 payment fintechs around the world have already replaced rules with machine learning approaches technology.  

And of course, AI can improve the quality of SAR submissions. The FATF reports that machine learning is offering the greatest advantage to users “through its ability to learn from existing systems, reducing the need for manual input into monitoring, reducing false positives and identifying complex cases, as well as facilitating risk management.”

AI is not explainable.

Every AI method has its methodology. The difference is that AI is math driven, meaning it is precise, logical, and driven by facts that can be verified and validated.

In unsupervised AI, algorithms are programmed to identify abnormalities. When applying this methodology to risk factors, the system can detect suspected cases of ML that deviate from norms for the given typology. Therefore, AI cannot only be explained and is far more accurate than rules will ever be. In AI, the computer takes over to intelligently solve problems with no human bias about how things should be. In this way, new and unknown threats can be detected.

Going to the cloud is cumbersome and requires extra effort.

Cloud-enabled services are typically API-based and fast methodology. Users of cloud-native services gain benefits such as scalability and the ability to receive fast system updates. Having said that, once you make the migration, there are lots of benefits. That is why financial institutions are transitioning customer data to cloud-native. Cloud-based solutions accelerate time-to-value, reduce the cost of acquisition, and enable companies to increase revenues quickly, without having to worry about the maintenance of additional infrastructure.

I would need scientists to work my alerts as regular investigators would struggle.

The contrary is true. Results from AI systems are far more RICH with information, far more targeted, and explain to an investigator how and why they were generated from the get-go. The best AI feeds investigators full stories which make their work easier. An advanced AML software system with integrated case management based on a risk-based approach will paint a picture of the type and severity of an alert. In this way, analysts will be guided through the process and know which alerts warrant higher attention and submission of SARs instead of passing everything for further investigation, thereby preventing unnecessary blocking of legitimate customer transactions.

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