Much of the conversation to date, however, has centred around how technology can help us, as an industry, overcome challenging requirements in specific articles.
For example, the need to launch tech portals which provide clients with an easy way to access information about reports, costs, and charges to comply with Article 24(4), and retrofitting IT systems to meet the provisions around algo-trading and trade execution to comply with Article 17.
But this doesn't solve the bigger and broader problem: making real-world sense of the legislation in the first place. It doesn’t tell us where to start and what’s important.
It doesn’t help us get to a place where we feel comfortable that everything that needs to be implemented has been implemented, as well as being implemented in line with regulator expectations.
It is this bigger problem: building a deep understanding of the legislation, so that compliance routes can be effectively plotted, that AI can solve for managers. AI can help us with this because all the primary and secondary legislation for Mifid II take the form of structured documents.
This means that we can train computers to parse and analyse every word and every phrase. With background knowledge of the business, this AI can then provide compliance officers with only the most relevant information – or restructure it in a way that is easier for them to understand.
For example, it is possible for AI to digest Mifid II, present us with only the articles within the legislation that apply to our company, order them by deadline on implementation, and present step-by-step guidance on ensuring compliance.
This use of AI technology transcends merely helping us with the implementation of articles on a case-by-case basis, and instead provides managers with a bird's-eye-view of the legislation, making the implementation process quicker, more manageable and a great deal more successful.
Case study of AI in action
AI is an often-misunderstood technology. Although it is more widely accepted than ever before, to many people it still conjures up a future where humans are not entirely in control. But AI is actually a great deal simpler, more prosaic and less dystopian than that.
To see its true potential, it helps to see how AI works in practice with an example to recognise why this is useful for regulatory officers.
At the core of many AI programmes is Natural Language Processing. NLP is nothing more sinister than using different steps to develop a way for computers to understand human speech, and has been a field of computer science since the 1950s.
Examples of the evolution of NLP include Google Translate, automatic email spam detection, and the condensing of newspaper extracts online.