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.
In addition to these sessions, Matthew Redhead, Managing Director – Policy & Campaigns at Fintrail, hosted a thought-provoking panel interview with the Napier team: The Next Step with RegTech.
If you missed it, you can catch up with the interview here.
Alternatively, here’s our summary of the most exciting next steps for RegTech:
1. Adopting a customer centric view
Understanding customer behaviour is really important for detecting and unlocking financial crime more effectively. A holistic, customer centric view combined with explainable artificial intelligence (AI) will reveal anomalies and provide an indication as to why a customer is behaving in such a manner.
That said, achieving the potential from AI-enhanced anti money laundering (AML) systems requires good quality data that can be easily separated and merged to give a holistic view. For many, this means finding a way to breakdown internal walls, which can be particularly challenging.
Establishing a well-understood data model is essential for not only reducing the length of the RegTech implementation process but for increasing system performance.
2. Explainability
Explainability is an increasingly important consideration for AI-enhanced systems, not least because the latest regulation is demanding it in layman’s terms. RegTech needs to be there to enable but it also needs to be simple, clear and easy to use and understand.
Napier has set out from the beginning to ensure clients can use its tech without the assistance of a data scientist, a process Napier describes as democratising AI. AML tech needs to empower its users to make a difference.
3. Graph-based databases and networks
The ease with which analysts and compliance officers can use an AML system is paramount. AI is essential but the AML system needs to be easy to use for all. This is why improvements in database design and architecture like graph-based databases are so important: analysts can easily see patterns and relationships in data. Graph networks provide a clear visualisation to trace the movement of funds across silos and geographical boarders.
4. Reducing false negatives
The industry as a whole is missing almost all of the dirty cash being washed. The number of false negatives clearly needs to be addressed but the issue is we don’t know what patterns of anomalous behaviour we’re looking for.
To overcome this problem, we can:
- Use synthetic data to give us some clues
- Use unsupervised machine learning to detect the unknown unknowns
- Adopt a more coherent approach to datasets and data sharing both internally and externally.
Above all, an intelligence-led approach is essential.
At the same time, we need regulators to set a moonshot target, so everyone pulls together to increase the amount of crime being stopped. We need to do more to reduce false negatives and effectively detect money laundering. Yet without clear direction and support from the regulator, we are unlikely to see any significant improvements.
5. RegTech making a huge difference in the regulatory space
The difference RegTech can make to regulators operating in the public sector is huge and currently untapped. Napier is currently reaching out to regulators, offering license-free use of its wide range of multi-award winning AML software. Hopefully the adoption of AI-enhanced anti money laundering tech by the public sector is not a matter of if, but when.
Similarly, Napier believes regulators should also take the responsibility for ensuring all regulated entities adopt AML modern tech. Afterall, we are only as strong as the weakest link.
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