Discussion of artificial intelligence (AI) , NLP and machine learning, in general, but also specifically in compliance, often revolves around how either or both technological developments will take over jobs carried out by humans. But it doesn’t have to be this way. In fact, AI and machine learning in compliance could actually go the other way by enabling compliance personnel to become even more important in combating fraud on the trading floor.
In this article we discuss how technology can give compliance professionals superpowers and look at the enormous benefits for financial organisations.
Regulatory interest in using AI and machine learning in compliance
The Financial Conduct Authority (FCA) has said that implementation of AI and machine learning technology will lead to “far more efficient regulatory compliance”. The UK regulatory body want to see a widespread implementation of machine learning and AI in compliance and are leading the way in their implementation.
Financial organisations already use machine learning and Natural Language Processing (NLP) capabilities to detect fraudulent activity and attempts at market abuse through trading floor communication surveillance of various communication channels, including email, instant message and voice. Through voice however, organisations have always been particularly vulnerable to fraudulent activity.
It is a communication channel that compliance teams have long sought to gain better control over and with the “superpowers” that machine learning can bring, such problems will be solved. AI and machine learning meets the challenge of effectively surveilling voice.
What superpowers do AI, NLP and machine learning offer compliance teams?
Adoption of these technologies can create many benefits for financial organisations. Here are the superpowers that compliance teams will receive:
Automated digitised compliance
Machine learning will bring machine readable and fully machine-executable functions. This will take much of the manual processing responsibility away from compliance teams, allowing them to focus more on identifying rogue traders and detecting and eradicating fraud.
Advance speech analytics tools for better voice surveillance
Accurate and timely voice analysis and transcription of speech will go a long way towards greater surveillance success. These tools would be complemented by media analytics and social media analysis, giving compliance teams stronger means to detect suspicious and fraudulent activity.
Communication and financial data tools to automate compliance protocols
This would lead to cost savings. more efficient reporting and easier compliance with regulatory demands.
Learning algorithms that detect suspicious behaviour fast
These implementations will help detect financial irregularities and rogue trading behaviour
How will compliance teams benefit from these machine learning “superpowers”?
Massively reduced costs
Compliance is a large cost for financial organisations. AI and machine learning will lead to huge cost reduction as a result of automation and more accurate big data analysis.
More efficient regulatory compliance
This efficiency comes from near real-time risk detection capabilities as well as the automation and digitisation of manual reporting and compliance processes. Financial organisations must now comply with Market Abuse Regulation (MAR), which took effect in 2016, and get ready for Markets in Financial Instruments Directive II (MiFID II), which comes into force in January 2018. These machine learning superpowers will help compliance professionals to manage MAR and MiFID much more efficiently
Reduced risk of fraud and rogue trading
With a comprehensive focus on surveilling all communication channels - including considerable improvements to speech analysis, which has been particularly vulnerable to fraudulent activity - instances of fraud are minimised and identified.
Greater control over the trading floor
Machine learning in compliance affords teams a holistic view of the trading floor. This leads to greater and more centralised control. This is further strengthened by the multipurpose use of data. Thanks to machine learning, big data analysis can also be used for business intelligence, behaviour pattern scrutiny and strategic decision support, among other functions.