Ali Abbas
FINRA Podcast: Financial regulators are embracing AI, is it time you did too?
In FINRA’s latest podcast, the head of Data Analytics and Technology for Member Supervision, Kerry Gendron, discusses how FINRA is augmenting its exam and risk monitoring program with data analytics and technology. CUBE asks whether it’s time financial institutions level-up along with the regulators.
In FINRA’s latest podcast, the head of Data Analytics and Technology for Member Supervision, Kerry Gendron, discusses how FINRA is augmenting its exam and risk monitoring program with data analytics and technology. The podcast gives a rare insight into how this financial regulator, famed for its willingness to embrace technology, is modernising its supervision systems. CUBE dissects the key messaging and asks whether it’s time financial institutions level-up along with the regulators.
Human and machine, working together
The notion of a compliance system in which human and machine work in tandem to create a watertight program is, for many, not a new concept – especially for those familiar with regulatory technology (RegTech). However, it is rare to hear financial regulators endorsing such a system – often choosing to remain tech agnostic instead.
In their most recent podcast, Kerry Gendron sets out a vision in which “highly skilled people working in diverse teams” are “backed by the power of data and innovative technology”. People carry immense strength in their ability to think creatively, make judgements and ask meaningful questions, whereas machines excel in processing large volumes of data at scale, and making predictions accordingly.
A marriage between both human and computer is how FINRA anticipates its future supervisory systems will work. As, Gendron comments “If you take the combination of the human, who’s really good at asking questions, and the machine that can answer questions, that’s where you’ve got a really good partnership of augmented intelligence and that’s the vision that we have for how we can put tools in the hands of Member Supervision staff.”
Technology won’t only help the regulator in terms of ensuring consistency and integrity within its data sets, it will in turn help firms insofar as it reduces their compliance burden. If the regulator is more efficient and effective, this will undoubtedly trickle down to its regulated members.
Using advanced analytics to bolster investigative tools
Throughout the podcast, Gendron notes that FINRA is looking at augmentation in terms of “buckets”. The first bucket is the human working alongside computer to digest data more effectively and efficiently. The second bucket is then taking that data and using it in the best way; “we’re looking to see how we can leverage advanced analytics and make better predictions.” The other two buckets? Leveraging advanced analytics to bolster investigative tools, and operational efficiency.
In particular, this involves assessing the “tedious tasks” carried out by the Member Supervision employees and asking, “how can we free up their time to do more value-added tasks?” As an example, Gendron outlines FINRA’s registered rep exam program in which FINRA are augmenting their employees with analytics. “We have a model which looks at a variety of data, such as disclosures or complaints or employment history data. And based on several of these data points, plus some others, we have a model which creates an output of a preliminary list of registered reps for our staff to review further.”
This is a sentiment that CUBE has long since embraced; too often we see highly talented employees carrying out administrative tasks, simply because businesses have not invested in an infrastructure that embraces artificial intelligence or smart technology. It is refreshing and encouraging to see that it is not only financial institutions that are now re-evaluating their processes, it’s the regulators too.
One might pose that, if financial regulators are bolstering their supervisory systems with regulatory technology, it won’t be long before they require those they regulate to do the same.
