Banks need to overcome many problems where anti-money laundering (AML) and assurance are concerned. But what are the main challenges and how can banks use emerging RegTech solutions to close the gaps? A panel discussion at Sibos Toronto reviewed the most significant developments and explored the role collaboration can play in supporting innovation.
- Analysts spend as much as 80% of their time finding data instead of evaluating and fixing problems.
- Other AML and assurance challenges include obstacles to information sharing, high volumes of data and large numbers of false positives.
- Emerging technologies can help to address these problems. Areas of opportunity include AI/machine learning, blockchain, identity verification and technology focused on transaction pattern detection.
- Collaboration is paramount in achieving more effective financial crime compliance.
From my perspective, the primary focus within financial crime compliance is not to take out cost – it’s to be more effective and efficient.
Panellists cited manual and expensive processes and scarcity of skilled resources as challenges associated with AML and assurance.
A large global bank’s transaction monitoring system may generate hundreds of thousands of alerts each month that require investigation. According to one panellist, the existing technology is “simply old” and is not effective – “hence why we’re seeing over 90% false positives.” Meanwhile, analysts spend as much as 80% of their time finding data, rather than evaluating and fixing the problems.
Banks are spending over $8 billion a year on financial crime compliance, and seeing a gap between the money they spend on their financial crime compliance programmes and the output.
It can be difficult for banks to share information across borders – “even within your own group between different subsidiaries, or with the government.” Another challenge is for banks to get feedback from governments about suspicious activity reports that they have filed.
The experts discussed how emerging RegTech solutions can be harnessed to maximise the effectiveness of banks’ transaction monitoring and screening programmes.
One panellist described a machine learning pilot that allowed analysts to devote 80% of their time to analysis rather than information gathering. They noted however that AI is not yet ready to make a major difference and there is little evidence that banks are ready to replace current transaction monitoring and screening programmes with a pure AI-based solution. As Isford explained, “AI is not the panacea here at all, but it is a part of the solution.”
- Optimisation layer. Banks can introduce an ‘optimisation layer’, allowing analysts to look at alerts only if there are other risk indicators on top of the traditional AML alert.
- Identity resolution or verification. Technology can be used to verify that people are who they say they are, and who is really behind a transaction.
- Blockchain. Developments include a pilot currently underway with the regulator in Singapore around shared KYC
- Patterns. Looking at obvious patterns based on behaviours may provide an opportunity to address the underlying problems.
A number of obstacles may prevent clients from adopting these technologies, including banks’ limited capabilities when it comes to understanding and managing the technology. One expert noted that hiring military veterans can be a successful approach. And regulators may also face capability issues of their own.
Can collaboration support innovative solutions?
The panel discussed the importance of collaboration when it comes to running a more effective financial crime compliance framework, agreeing that collaboration includes understanding law enforcement’s priorities, as well as sharing information safely between banks while respecting data privacy requirements.
Banks are working with law enforcement on cases leading to arrests and prosecution. The panel also touched on the Wolfsberg Group’s new enhanced due diligence questionnaire for correspondent banking, which is now included in SWIFT’s KYC Registry.