Using AI in client due diligence

Property practitioners will know that advising clients in different jurisdictions is not only challenging, but can be a strain on resources as well. As anti-money laundering (AML) regulations evolve independently in the UK, Europe and the USA, many firms are adopting a global approach and applying the same rigorous AML standards to every client they onboard.

Although a standardised approach streamlines jurisdictional issues, it can create further challenges as it:

• can take days or weeks to vet a client, leading to lost opportunities as clients favour more agile firms
• adds pressure on compliance teams, increasing costs for firms, and
• sits in tension with applying a risk-based approach to client due diligence (CDD).

As a result, fee earners and compliance teams end up spending excessive time on vetting clients who are low to medium risk. Without a way to triage them effectively, every client receives the same scrutiny, diverting resources away from higher-risk cases.

The role of compliance is to have adequate systems and controls to identify misconduct, not specifically to prevent misconduct itself. Applying a risk-based approach dictates that firms assess the risk profile of all new clients and matters and implements appropriate AML measures tailored to the level of risk that a client or matter poses. However, traditional risk assessment systems are insufficient to gain a full understanding of a client’s risk profile.

To prevent bad actors, compliance teams need to keep pace with advances in technology in updating their systems and processes.

Screening for politically exposed persons (PEPs) and organisations on the watchlist is limited. With 2,200 individuals and entities subject to European and UK sanctions against Russia, and 8.2 billion people in the world, a screen check against sanctions lists is clearly insufficient. Databases don’t cover all individuals and to be reliable they need to be up to date. Just because someone isn’t in a screening database today, doesn’t mean they won’t pose a risk tomorrow.

Running keyword searches in Google is also limited. To have truly scalable processes, firms set limits on analysts’ research on Google. Analyst teams might be told to stop at page 10 of Google when reviewing results for example, in order that they can move on to the next review case without spending all day on one individual search. Keyword searches in themselves are restrictive as well. Just because a person’s name doesn’t appear against ‘fraud’ as a key search term it doesn’t mean they haven’t otherwise displayed fraudulent behaviour. Ultimately, this means that most firms are unable to uncover all the information available about their customers to inform a risk assessment.

We hear a lot about the risks of artificial intelligence (AI), but in this case it can help to search, analyse and document all publicly available information about third parties in minutes. In fact, firms that don’t adopt this technology will soon be seen as falling behind in applying the stringent systems and controls needed to prevent economic crime.

Restructuring compliance processes may not be welcomed by many firms, but efforts will be rewarded. With rapid, automated due diligence reports on all third parties, firms will be able to apply a truly risk-based approach, by dedicating more resources to high-risk clients, and processing low-risk clients much more quickly.

Beyond efficiency savings, understanding a client’s business and activities means you can provide a more tailored onboarding experience, and enhance the services you offer.

AI technology isn’t just about speeding up processes or cutting costs – there’s more room for transformation than that. AI can be a catalyst for transforming how firms adopt a truly risk-based approach to all their compliance.