How AI could change the AML landscape - Part Three

An Irish solution for a global problem

Parts one and two available here.

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AI is clearly impacting many human endeavours and industries, and the anti-money laundering (AML) and Know-Your-Customer (KYC) space is no exception. AI has the potential to greatly improve the effectiveness and comprehensiveness of AML & KYC processes. It can do this by further automating tasks and identifying patterns and trends that would be difficult or impossible for humans to spot and by improving the accuracy and efficiency of KYC processes and AML monitoring and detection.

This is very exciting and there are - theoretically - a great number of ways in which AI could positively  impact the AML / KYC landscape. These include many of the following:

Automated risk assessments: AI can be used to automate ever more complex risk assessments of customers and transactions, thereby helping regulated firms and financial institutions to identify high-risk customers and transactions more quickly and efficiently.

Real-time monitoring: AI can be used to monitor vast numbers and combinations of transactions in real time for suspicious activity, helping to detect and prevent money laundering and terrorist financing more effectively.

Pattern detection: AI can be used to identify patterns and trends in massive amounts of structured and unstructured data that would otherwise be difficult or impossible for humans to spot. This can help agencies, regulated firms and financial institutions to identify new and emerging money laundering schemes.

False positive reduction: AI can be used to reduce the number of false positives generated by AML monitoring systems. While any automation implemented to solely reduce work needs to be done very, very carefully, reducing false positives can free up resources to focus on investigating exceptions, true positives and prosecuting real money laundering cases.

Automated document verification: AI can be used to automatically cross check and verify the authenticity of a wide range of government-issued IDs, certificates and other commonly-used KYC documents. This can be done by using OCR to extract information from both structured (ID cards, certificates etc.) as well as unstructured (utility bills, annual reports etc.), and then using machine learning to compare that information to known databases and online registry information.

Biometric authentication: AI can be used to authenticate customers by better use of often imperfect biometric signatures such as fingerprints, facial recognition, and voice recognition. In this way, AI can ease the onboarding by minimising failure-rates while quickly ensuring that customers are who they say they are.

Improved accuracy and efficiency: AI is tireless and can maintain the highest level of accuracy and efficiency in AML monitoring and detection. Accuracy and efficiency, as with the other examples, can help institutions comply with AML regulations more effectively and reduce their risk of fines and penalties.

Overall, AI has the potential to make a significant positive impact on the AML & KYC landscape. In theory, AI can help financial institutions to identify and prevent money laundering more effectively, reduce their risk of fines and penalties, and improve their compliance with AML regulations. That’s the theory and some of the above use-cases can be implemented quite quickly, others may take longer.

All of the above use cases can be seen as parts of a more comprehensive AML / KYC process. In order to get the best out of these use cases, firms need to ensure that they have suitable policies in place and that these policies are transposed into defined, repeatable and measured processes. Only by doing so can the benefits of AI be clearly demonstrated in not just doing due diligence faster, but also doing due diligence better - minimising false positives and false negatives and zeroing in on real issues.

As discussed earlier, AI needs lots of appropriate and more timely data. AI in AML even more so. Firms, however, face great difficulty in having all of the right information from the right sources at the right time. Customers want to lock away and protect their confidential information. Privacy policies, encryption, heightened security and regulations with regard to financial and PII (personally identifiable information) all add to the complexity. 

However, in meeting these seemingly at odds requirements, the industry can embrace a more modern, customer-controlled, data sharing scheme, one with permissioned, secure and convenient access to customer data, while still employing high levels of data security.

I’d be remiss in failing to point to Ireland’s valid8Me and their onboarding and due diligence solutions as leading edge in firstly, addressing many of the above use cases, secondly, doing so by means of highly configurable and reportable process flows and thirdly, employing very advanced and secure “wallets” to request, remind and allow users to share and control their confidential AML/KYC information, documentation, evidences and so forth. 

AI, in combination with, and implemented within, the valid8Me solution is a very exciting and very compelling new business value and due diligence driver.