By Geraldine Gibson, CEO, AQMetrics
Artificial Intelligence (AI), long the subject of science fiction, is now becoming more and more widespread and is seen as an increasingly important computer science across multiple industries. In Financial Services in particular, Machine Learning and Natural Language Processing is increasingly used today to make sense of big, complex data in a wide range of areas.
One such area is regulatory compliance. The use of AI – particularly Natural Language Understanding (NLU), a subset of Natural Language Processing – can help firms to realise a number of benefits, including improving the speed and efficiency with which they achieve compliance, and making that compliance much more robust.
The Challenge of Interpreting Regulation
As we’ve seen over just the last couple of years with the introduction of MiFID I & II, UCITS, AIFMD and the like, there is a constant stream of documents being issued by regulators, which can each run to hundreds, or even thousands, of pages. Wading through those documents and trying to pick out the pieces that are important so that appropriate rules can be built, code can be written and reporting systems can be automated (for example), is an onerous task for human beings.
In order to achieve compliance, many regulated firms take the approach of partnering up with third party consulting firms, paying large sums to them to help interpret the regulations, employing people to write up what everything means from a rules perspective, then attempting to code those rules into their systems.
This process is both operationally inefficient and unnecessarily expensive, when compared with using Artificial Intelligence from the very beginning of the process.
Today, AI can be – and is being – used to interpret regulations, to comprehend what needs to be done from a technology perspective, and then to codify the necessary rules. Those rules can then be automated into the relevant reporting and risk systems to make sure that the firm is not only staying compliant but also better managing its risk.
Natural Language Understanding
The key element here is the use of NLU, which is the component of natural language processing that reads – and more importantly interprets – text. By automatically translating the written regulations as they come out into usable code, NLU offers the benefits of bringing down the cost, the effort and the timescale of interpreting and implementing new and updated regulations.
Putting this into practice, we have worked together with our clients to develop a Regulatory Risk Analysis module based around NLU. This module takes in multiple sources of data from various business units and interprets it into a common language. That common language is then used to analyse and aggregate the data, distributing it back into one single, holistic view of risk across the organisation, which can then be analysed using various factors.
This approach, where all the risk factors are stored, analysed and fully assessed using machines, substantially increases the probability of the firm being compliant because it does away with the risk of cognitive bias, i.e. the risk that a group of compliance or risk officers come up with subjective and inaccurate rules regarding what they think the system should be doing, and maybe miss something.
Of course, risks will always exist. However, what this AI approach does is ensure that every ‘i’ is dotted and every ‘t’ is crossed during the risk assessment process.
Greater Speed, More Accuracy, Lower TCO
Although there are various different approaches to ensuring regulatory compliance, the whole process can only be fully optimised if AI is included in the journey.
By adopting platforms with AI at the very beginning of the process, with NLU taking the emerging regulations in and transforming them into a technological solution, firms will not only gain the speed, the efficiency and the accuracy that is needed to meet emerging regulations, but they will also hit regulatory deadlines – which otherwise can be quite challenging – and do it all at a lower total cost of ownership and with less overall risk than is otherwise possible.