Today, Financial Institutions (FIs) face significant legal and reputational risks when it comes to complying with anti-money laundering (AML) requirements (including anti-terrorist financing and obligations to conform). Failure can lead to serious sanctions imposed by regulatory bodies (Recently, Societe Generale fined $5.83 MM for a number of shortcomings in its control for preventing money laundering).

Today’s financial markets are truly global. Transactions and flow of funds take place through a web of interactions across nations and systems. This makes it difficult to be compliant with thousands of regulations and norms across a large number of jurisdictions. It calls for a deep understanding and analysis of each legal or regulatory text and its impact on the company. Banks must undertake and manage the necessary actions needed to ensure compliance. Simple compliance to all these rules involves massive investment and resources for the banks, and they continue to sink billions of dollars into this each year. Huge investments in process refinement for large diversified financial institutions through analytics, has yielded limited benefits.  While many argue that there is merit in existing analytical tools, the question remains - do they exploit the full potential of what is possible, to ensure full regulatory compliance and the curbing of money laundering?

Due to increasingly stringent regulations, there is a growing volume of structured and unstructured data (descriptions, sanctions lists, narratives, social identity, transaction data etc.). And that’s where artificial intelligence (AI) – particularly machine learning and Natural Language Processing (NLP) can play a pivotal role. They can extract the metadata (data that serves to provide context or additional information about other data), detect ‘entities’ that are referred to and establish the purpose of a specific part of any regulation.

We also see more companies embarking on the journey to use AI through machine learning and NLP as current systems struggle to identify and report suspicious activity adequately. Recently, Oversea-Chinese Banking Corporation Limited (OCBC) has piloted AI-based AML solutions to enhance its competency in fighting money laundering and terrorism financing. HSBC is also implementing AI technology to automate AML investigations that have traditionally been conducted by thousands of employees.

AML systems are overwhelmingly rule-based. As regulations become more stringent, the rule-based systems grow more complex. Hundreds of rules drive know your customer (KYC) activity and Suspicious Activity Report (SAR) filings. As more rules get added, more cases get flagged for investigation and hence, false positive rates increase. The chase becomes ineffective. Criminal transactions (including terror funding) seem to outsmart the current system. However, unsupervised Machine Learning (UML) can address this by automatically detecting hidden patterns in large sets of data. AI in AML is still evolving. A more developed system in the future will save billions of dollars spent on inaccurate identification and chasing spurious transactions.

AI still has a long way to go to be able to make a real difference here.  We see some smart deployment of AI in SIRI, Alexa, Tesla, Boxever, Cogito, but Ironman’s Jarvis is still for the future.

Larger FIs will find it easy to experiment with AI in AML due to their deep pockets and technological expertise. Smaller institutions with tighter budgets may find it difficult. This gap will shift money laundering risk to smaller banks, credit unions and payment services companies, as well as financial institutions in developing nations. Thus, it is crucial for firms, irrespective of size, to start thinking about the standardizing, consolidating and integrating legacy systems. This will be the critical preparatory step for AI AML implementations, as they become more reliable and commercially viable for a perfect future.