A big topic for this Sibos (and most past ones too) is how to stamp out financial crime without disrupting or degrading high quality service experiences for the vast majority of honest (and very important and valuable) customers.

As it stands, financial crime is skyrocketing with banks facing more and more challenges to manage risks effectively. Although this isn’t exactly a new trend for financial services, the speed in which fraudsters are changing their tactics has pushed organisations to rethink their security procedures and response to fraudulent activities. 

So how can banks get ahead?

Financial crime specialists are used to applying rules engines to detect cases, and increasingly artificial intelligence (AI) and machine learning are further improving detection and management. Applying AI and machine learning to financial crimes alert management has led to significant results, including reductions in false positives, improved risk detection, and increased automation at scale.

One operational challenge is how fraud and financial crimes functions, sometimes operate independently within financial firms. This model may have been appropriate years ago when fraud and financial crime schemes were dissimilar and managed accordingly, but current factors like channels, payment rails, and decentralisation have blurred the line between fraud and financial crimes. 

In the last few years, financial institutions have invested heavily in enhanced detection monitoring systems, taking advantage of capabilities from FinTech’s that specialise in AI and machine learning. This trend is a prime example of financial institutions incorporating a best-of-breed approach that marries investments in legacy systems with newer, AI-based technologies. 

The big question to ask is how do banks effectively spot and stop money laundering schemes without messing up customer service experience for customers? The key is to stay agile. It’s all well and good to have the right technologies in place but what is equally important is being able to triage an incident accurately and efficiently. Whilst no one wants to be exposed to fraud, either customer or bank, it’s important that the customer experience does not suffer at the expense of it. 

So ultimately what you can do is route what you can to the right person, keep the client up to date and minimise your losses on the client side as well as on the bank side. The struggle for effectiveness and efficiency increases even more if you consider the effects of disparate detection systems with differing levels of automation within their case management workflows. This does not provide a harmonised user experience for bank employees responsible for these outcomes. 

As financial institutions continue to look to reduce operational costs, exposure to risk cannot be sacrificed in the process. Whether investigative units are operating on an onshore, onshore/offshore, or some other hybrid model basis, the goal is to effectively direct an alert and/or case to the analyst and/or investigator best suited for its complexity, risk, or other factors. This allows firms to properly manage risk while controlling operating costs.