The FinCrime World Forum took place on the 22 and 23 June, during which Napier’s team were delighted to participate in two virtual sessions alongside some of the very best professionals in the fields of RegTech and AML. The sessions were:
This session looked at the problem of increasing risk exposure as a result of fragmentation, and how silos in AML systems and processes can be closed. Napier’s Chief Operating Officer, Greg Watson, took part.
This session looked at how AI is being successfully applied in other sectors with consideration to the possible applications for tackling financial crime. Napier’s Chief Data Scientist, Dr Janet Bastiman, took part.
Both sessions are available on demand but if you don’t have time to catch up, here’s a quick summary of what you need to know:
1. Money laundering will persist for as long as fragmentation exists
Old anti money laundering (AML) systems were never designed to link up and talk to each other, yet fragmentation in organisations, systems and processes creates cracks in which criminals can hide. That said, fragmentation is the way of the world. We need to accept it will persist and use innovative AML products that overcome it.
It’s really important that the responsibility to improve financial AML ecosystems has to be elevated to the board of directors to ensure real progress is made.
2. AML is all about data – standardisation, sharing and more
Standardised data is so important to fighting financial crime. While not forgetting regulatory restraints around data privacy and sharing, the AML industry as a whole needs much more open, standardised data in order to be able to effectively detect financial crime.
AML needs to be about intelligence and data sharing. Industry-wide cooperation and collaboration is essential for eliminating the fragmentation and silos which hamper the ability to detect criminal activities.
Above all, AML processes cannot be enhanced with artificial intelligence (AI) without good quality data. Without data there is no AI, so it’s paramount to get and keep data in order.
The healthcare industry has been successfully using AI for almost a decade but in the financial industry we are seeing data silos and basic data quality challenges which are hampering progress. There are certainly best practices to be learned for data management from other industries.
Part of the solution may rest in increasing the diversity of people in the Fintech industry. Bringing in more data management experience from other sectors will be of great benefit to the financial sector.
3. Regulators must do more
If there was one key theme that came out of these sessions, it was that regulators need to do more – a lot more – if industry is going to improve its performance in detecting financial crime.
Industry needs more guidance from regulators
Regulators advise on what you should or must do, but crucially, don’t tell you how to do it. This creates uncertainty over what AML practices are acceptable. For example, many organisations fear implementing artificial intelligence (AI), in case it is not met with regulatory approval.
Regulation around intent would be enabling rather than controlling and give industry the confidence to use new tech.
Regulators need to provide more encouragement to be innovative and adopt new tech
There was wide consensus that some regulators have no concept of the new tech to detect financial crime, and as a result are not knowledgeable enough to drive innovation. There is a major knowledge gap and we need to convince the regulator to come on this AI journey with us.
Regulators need to drive the establishment of goals to improve the detection of financial crime
Only 2% of all money laundering is being detected, which is simply not acceptable. All stakeholders need to unify to set a goal that incrementally improves this figure, and this needs to be driven by the regulator. A goal would not only give the financial industry the key purpose which it is currently lacking, but provide scope for accountability should it not be reached.
The airline industry is a great example of how effective visible goals are in ensuring the performance and control of all those operating in this safety critical space. It’s notable that this industry is also using AI very successfully with zero tolerance for error.
AI holds huge potential for the financial industry but more work is needed by all stakeholders to turn this potential into the reality of not only increased detection of suspicious activity but ultimately, the prosecution of those responsible for money laundering.
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