Changes to the regulatory framework provide an interesting backdrop to the discussion our panel of industry experts and practitioners had around the opportunities for change in the economic crime detection system.

The need to integrate approaches across anti-money laundering (AML) and fraud rather than continuing to operate within siloes was backed up by both theory and practice. There was a recognition of the need to act now rather than wait for a new regulatory framework. The complexity of merging, interrogating and creating meaningful insight from disparate data systems was recognised. However, the potential to identify potential fraud much earlier in a customer's life cycle was seen to far outweigh these challenges.

The Treasury has announced a review of UK money laundering regulations to take place later this summer. Encompassing a ?back to first principles? review, this is a fairly unique opportunity to step back and assess the economic crime landscape today and assess potential areas of material improvements to the system as a whole.

One of the main themes that typically arises during wholesale economic crime regime discussions like this is around streamlining existing processes and the introduction of more efficient options that are tailored to the actual risks within the system. And in terms of AML and fraud (which have historically been very siloed within institutions) there's a need to fully understand the lessons learned from all approaches and bring these together into a more integrated and effective system.

The discussion that takes place between our panellists looks at some of the current processes within both AML and fraud, the lessons being learned from these, and how these learnings could be applied to bring us closer towards the desired and more active economic crime ecosystem which reduces the harm of both fraud and wider economic crime.

The webinar, available to download here, highlights that the finance industry needs to adopt a proactive stance when it comes to bringing fraud and AML operations together. It's widely appreciated that fraud is the primary predicate offence to money laundering and wider economic crime, so having an end-to-end view of the financial crime life cycle - with real rigour around the risk assessment throughout that life cycle - should enable organisations to put robust controls in place without waiting for the regulator to tell institutions what to do.

Data of course are key to both fraud and AML operations but as our panel discusses, organisations have to be selective around the databases used to build a financial crime ecosystem. This comes down to understanding the risks that individual businesses face, and both the quality and the value that each kind of data feed or data item will provide. This is crucial to ensure that organisations mitigate against false positives and manage referral volumes for investigation. Our panel also discusses how there is a need to aggregate data if you?re using disparate or multiple data sources, and how the use of machine learning and AI is crucial to effectively consume multiple strands of data and orchestrate it to get an optimised risk outcome.

Teams working across fraud and AML are often tracking the same individuals and the same types of criminality. By conflating the two and looking at the crossover between fraud and financial crime you can reduce false positives, increase the speed of customer onboarding, and more importantly better classify risk early on the customer life cycle. By leaving teams and data sources siloed the converse is true, which can only lead to negative results for the industry.