Augmenting the Exam and Risk Monitoring Program with Data Analytics and Technology
Technology is opening up new possibilities when it comes to solving business challenges. And that's important when it comes to optimizing an organization's most valuable limited resource: its people.
On this episode, we hear from Member Supervision's new head of Data Analytics and Technology Kerry Gendron to learn how FINRA is augmenting its examination and risk monitoring program and teaching its employees the art of the possible.
Resources mentioned in this episode:
Listen and subscribe to our podcast on Apple Podcasts, Google Play, Spotify or where ever you listen to your podcasts. Below is a transcript of the episode. Transcripts are generated using a combination of speech recognition software and human editors and may contain errors. Please check the corresponding audio before quoting in print.
00:00 - 00:26
Kaitlyn Kiernan: Technology is opening up new possibilities when it comes to solving business challenges. And that's important when it comes to optimizing an organization's valuable limited resources: its people. On this episode, we hear from Member Supervision's new head of Data Analytics and Technology to learn how FINRA is augmenting its examination and risk monitoring program and teaching its employees the art of the possible.
00:26 – 00:36
00:36 - 00:50
Kaitlyn Kiernan: Welcome to FINRA Unscripted, from Hoboken, New Jersey, I'm your host Kaitlyn Kiernan. Today I'm pleased to welcome to the show Kerry Gendron, Member Supervision's senior vice president of Data Analytics and Technology. Kerry, welcome to the show.
00:50 - 00:52
Kerry Gendron: Thank you so much for having me.
00:52 - 01:24
Kaitlyn Kiernan: So Kerry, on our last episode we spoke with Ivy Ho and Greg Wolff from FINRA Technology and they mentioned that you've been involved with something like a fifth of all FINRA R&D program projects since that program started and you've only been at FINRA for a year. I want to get more into how you are helping to develop the future of the Exam and Risk Monitoring Program’s use of advanced analytics in a bit before we dive into that can you start by telling me a little bit about your background?
01:24 - 02:45
Kerry Gendron: My undergrad was in math and computer science and my first job out of college was as a developer in the financial services industry. What I learned from those first few years as a developer was that I most enjoyed how technology and data could be used creatively to solve business problems. After a few years as a developer, I decided to learn more about just general business, and I went back to grad school at Wharton for my MBA. Post-MBA I was a management consultant for a few years, and I was at a firm who really worked at the intersection of business and technology, strategy and execution and I really love operating within this space.
Fast forward to immediately prior to FINRA, I was a managing director in the legal and compliance division at Morgan Stanley. And there, again, I helped the Legal and Compliance Division think about how they could embrace data and technology more strategically to fulfill their mission. Toward the end of my time there I was also the head of compliance oversight, which encompassed their monitoring and testing programs. And so that really set me up nicely for this current role which, I think, uses a lot of my prior experience. I very much enjoy collaborating with others to solve business problems with data and technology as key tools.
02:45 - 02:48
Kaitlyn Kiernan: What is it that attracted you to FINRA?
02:48 - 03:47
Kerry Gendron: A lot of things. Having worked in the industry I had a tremendous amount of respect for FINRA and its unique role in the industry, and I appreciated—and I still very much appreciate—FINRA's mission. I saw from the outside that FINRA was making strategic investments in its migration of data to the cloud and its ability to mine that data. I knew that these types of investments were foundational, and they could shorten time to market and future innovation which was really appealing to me.
And last but certainly not least, I met Bari Havlik and I really resonated with her vision for both Members Supervision and the role that data and technology could play in Member Supervision's transformation. And I wanted to be part of that transformation. I wanted to work with Bari and her management team and be part of this transformation. And you don't get too many opportunities in your career for such a role, so I jumped at it.
03:47 - 03:57
Kaitlyn Kiernan: How have your impressions from while you're in the industry of FINRA's technology how has that matched up with your impressions now that you've been at FINRA for about a year?
03:58 - 04:31
Kerry Gendron: Since joining FINRA. I am absolutely impressed by the people, by the commitment to the mission, the culture, and that includes the culture of innovation, really impressed with the impact that prior investments in the cloud and other programs like the Createathon and the R&D program have had on raising the digital IQ of FINRA employees. And there's an absolute embracing of the culture of innovation here which is inspiring and makes it a fun place to work.
04:31 - 04:43
Kaitlyn Kiernan: So, your position, it's a new one. So how would you describe the role of technology and data in financial regulation and compliance? and how has that kind of shifted over the course of your career?
04:43 - 05:48
Kerry Gendron: When I started out, I actually was in the technology group and technology was pretty separate from the business. You did your coding; you didn't necessarily interact with the business much. And I'm not sure on the business side. There was a deeper appreciation for what technology and data could bring to the equation. Now, I believe everybody in the business and at companies in general recognize how important data, analytics, technology are to fulfilling the business mission. I think the data and technology IQ of the business folks has increased over the course of my career and will only continue to increase. I think that the expectations for how technology solutions are designed are increasing. And I think that the bringing together of technology and data and business in a much more cohesive partnership is more important than ever before.
05:48 - 05:54
Kaitlyn Kiernan: So that's why your new position is embedded within the business unit.
05:54 - 07:08
Kerry Gendron: Exactly. In my prior role I was embedded in the business and had a technology and data strategy hat and I think Bari has also recognized the importance of that. And it gives our team, the Data Analytics and Technology team within Member Supervision, it gives us a really pivotal role. We absolutely partner with technology on almost everything. We cannot be successful without our technology department partners. But we are also embedded in the team meetings of our colleagues in Members Supervision that we support.
We are at the table when folks are expressing frustration about why things aren't going well. And we can help brainstorm with them on the art of the possible. We can prototype data science solutions or data visualizations or dashboards, whatever the particular problem at hand requires. So you can think about us as a power user group of people working hand-in-hand with technology and the rest of Member Supervision to try to solve problems faster—faster time to market faster time to value—with data and technology as key tools.
07:08 - 07:25
Kaitlyn Kiernan: Earlier you mentioned that one of the things that attracted about working at FINRA was working with Bari and her vision for the Exam and Risk Monitoring Program Transformation. So how do data analytics fit within that broader transformation?
07:26 - 09:30
Kerry Gendron: Well, I think at the heart of Bari's vision for a transformed examined risk monitoring program, at the heart of it is are people—highly skilled people working in diverse teams, working to fulfill the mission of investor protection and market integrity, but backed by the power of data and innovative technology. And I believe Greg and Ivy on the most recent podcast about our R&D program talked about the role of artificial intelligence in augmenting people, thinking about how the humans and the technology can work together in a "whole is greater than the sum of its parts" kind of way.
People are really good at asking deep meaningful business questions, making human judgments, thinking creatively. Machines are really good at taking a lot of data, processing at scale and making predictions based on how they've been trained. So 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.
So different ways that we're thinking about how to use analytics at FINRA, you can kind of roughly classify them in four buckets. How are we getting smarter about the way that we're ingesting data? The blotter analytics program is a really good example of this. We've made strides in the way that we collect data from firms, and we process that data. We are now able to collect data from firms in whatever format they have. And we've developed tools to allow us to accept files in multiple formats and then use algorithms to normalize and transform this information. It's a good example of where we can use some of these techniques to gain efficiencies in our processing.
09:30 – 09:45
Kaitlyn Kiernan: And that sounds like it's good for both FINRA and ensuring data consistency and data integrity, but also good for firms in terms of minimizing the burden on them when it comes to compliance.
09:45 – 09:56
Kerry Gendron: Exactly. We're actively looking for other ways where we can improve efficiency and effectiveness for a variety of our stakeholders, our employees, member firms, etc.
09:57 – 09:59
Kaitlyn Kiernan: So, what's the second bucket?
10:00 – 11:43
Kerry Gendron: So, the first bucket is how can we ingest data more effectively and efficiently. Then we can think about, "Okay, now that we have the data, how can we use that? How can we use that data to make strong predictions?” So, computers are good at taking a lot of data, crunching it and making predictions based on how they've been trained. we're looking to see how we can leverage advanced analytics and make better predictions.
An example of where we're using that today is our registered rep exam program. We are augmenting our employees with an analytic. 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.
And then our staff takes this list and ask things like, "Okay, do we really think that this is an area of risk? Are there other ongoing FINRA exams that are already looking in this area?" We want to make sure we're not duplicating efforts. We're discussing with our risk monitoring analyst to understand maybe the overall culture of compliance at a firm. And it's really the humans in our registered rep exam program that take the output of our model, add additional meaningful analysis that's based on a case by case basis and ultimately decide to focus our limited resources in areas where there is maybe the highest risk of ongoing investor harm.
11:44 – 11:50
Kaitlyn Kiernan: So, it's giving the examiner a much more focused less to look at rather than looking at the entire universe of available options?
11:51 - 12:05
Kerry Gendron: Precisely. And we're able to allow our examiners or this particular specialist team, we're allowing them to focus on the value-add a decision about where to focus the resources.
12:06 - 12:10
Kaitlyn Kiernan: What are the other two buckets that you're broadly looking at?
12:10 – 12:36
Kerry Gendron: We're looking at investigation tools, so how can we leverage advanced analytics to help us connect the dots in the course of an investigation? Again, really trying to boost effectiveness and efficiency. And then lastly operational efficiency. So where do we have our Member Supervision folks doing tedious tasks? How can we free up their time to do more value-added tasks?
12:36 - 12:44
Kaitlyn Kiernan: With those four areas that you're broadly looking to improve, generally what are you hoping to accomplish over the next three to four years?
12:45 - 13:46
Kerry Gendron: How do we find the best partnership between the people in Membership Supervision and the tools and techniques that support them in doing their job? And so, it's thinking about shortening time to value and time to market on different individual solutions, rolling out broad based capabilities to let our staff solve some of their own problems or get at some of their own insights more quickly. And I think at the heart of this is really continuing the work of the Creatathon, the R&D program and what our current team and technology is doing to really broaden folks understanding of the art of the possible. The more everyone in Member Supervision understands, "Oh, actually I can now use artificial intelligence to look at unstructured data where in the past I was limited to looking at structured data."
13:46 - 13:50
Kaitlyn Kiernan: What's the difference between structured and unstructured data?
13:50 - 14:57
Kerry Gendron: Structured data is data that adheres to a predefined format. It is straightforward to analyze. Unstructured data is everything else. You can think about 80 percent of the world's data is unstructured.
So, let's take a normal email, a normal e-mail message contains both structured and unstructured data. Structured data would be the "to" and the "from" fields, because the computer is expecting a very predefined format in those fields—an email address. If there are multiple email addresses they're separated by semicolons. The timestamp on the email is a date/time. That is a structured piece of information. You wouldn't expect an image to be in that field, you wouldn't expect a name to be in that field. The timestamp on an email is always going to be a date and time. Those are examples of structured data.
The subject of the email and the body of the email is entirely up to the person writing the email to decide what to put in those sections. That is unstructured. The computer reading that email has no idea what to expect in those fields.
14:57 - 15:16
Kaitlyn Kiernan: So what you're trying to teach examiners is to stop thinking about what maybe 5-10 years ago the limitations were, where you could only deal with the structured data like the stuff you can put in an excel sheet, and think more broadly about what the data capabilities are with this unstructured data.
15:17 – 16:28
Kerry Gendron: Exactly. I think Greg said it really nicely when he talked about the R&D program. Where technology and data will make the biggest impact in Member Supervision, at FINRA, is when the business users recognize that there's a better way of doing what they're already doing. But in order for them to raise their hand and have that idea they have to know that there is a better way, or they have to know what the art of the possible is.
I think about what we're doing in the R&D program, and there's other initiatives underway, even just brown bag lunches to talk about successful solutions. "Hey, look at what we were able to do before!" These all help expand people's thinking, so the next time they're looking at a particular problem they have a new mental model of how this problem might be solved, which is where a lot of really amazing creative ideas come from. And then it's about selecting from those ideas which ideas have the biggest return on investment and which of those ideas are actually technologically feasible today versus this a great idea, we're gonna put it in the parking lot and we're gonna hope that technology evolves in a few years to be able to solve this in a more cost effective way.
16:29 - 16:59
Kaitlyn Kiernan: So it sounds like in the past someone might have just complained about the tedious or some part of their job that was difficult or time consuming, they might have complained around the water cooler back when we were in the office that it was safe to stand within six feet of someone else, but you want those people not just to speak complaining about it but to think like, "Oh I've heard that Kerry's team could maybe do something about this," and they might be able to find a solution or technology and not just complain about it, but to come to you and think of how can we fix and address this.
17:00 - 17:44
Kerry Gendron: Absolutely. And it's already happening. I have been here for one year and we are already seeing people proactively reaching out to the Data Analytics and Technology team saying, "Hey, I have a problem. Is this something that you can help us solve?," which is fantastic. It's not only thinking about how we can solve specific problems, but how can we embed capabilities to solve multiple problems with similarities, because it gets expensive really quickly to solve every single problem discreetly, but maybe you can roll out a particular tool that can solve a whole bunch of problems. And so, we're always thinking about return on investment and how we factor that into which problems we're able to solve.
17:45 - 17:53
Kaitlyn Kiernan: So, speaking of your team can you tell me a little bit about them. How big is the Member Supervision Data Analytics and Technology team? And who are they?
17:54 - 18:51
Kerry Gendron: Our team is roughly 25 people with a variety of backgrounds and skill sets, folks with deep industry or regulatory experience as well as risk management and consulting. We have PhDs and Master’s in Mathematics, Financial Engineering, Data Science, Statistics, Economics, several MBAs. Our team has skill set spanning A.I. and machine learning and other advanced analytics and statistical methods. We can design dashboards and data visualizations. We can analyze data and business processes. We have several of our team who have prior experience in our Examination and Risk Monitoring Programs as well as our AML and Financial Crime programs, which means they really understand the data that is at the heart of our analysis. And we have folks who are skilled in thinking about how to design solutions with the business user at the heart of those solutions.
18:51 – 18:58
Kaitlyn Kiernan: Just to wrap up, as you just mentioned you've been here a year now. What are your overall impressions?
18:58 - 19:18
Kerry Gendron: The people are amazing and it's a wonderful place to work. It is a place that embodies its values and its mission. People work together collaboratively across all areas of FINRA to try to protect investors and the integrity of the markets. And it's inspiring.
19:18 - 19:29
Kaitlyn Kiernan: Well Kerry, thanks so much for joining us to tell us about your new role and how Member Supervision is thinking about and exploring the use of advanced analytics and data and technology.
19:30 - 19:31
Kerry Gendron: Thank you so much.
19:31 - 19:45
Kaitlyn Kiernan: Listeners, if you don't already, make sure to subscribe to FINRAUnscripted wherever you listen to podcasts. And if you have any ideas for future episodes you can send us an email at [email protected] Till next time
19:45 – 19:50
19:50 - 20:18
Disclaimer: Please note FINRA podcasts are the sole property of FINRA and the information provided is for informational and educational purposes only. The content of the podcast does not constitute any rule amendment or interpretation to such rules. Compliance with any recommended conduct presented does not mean that a firm or person has complied with the full extent of their obligations under FINRA rules, the rules of any other SRO or securities laws. This podcast is provided as is. FINRA and its affiliates are not responsible for any human or mechanical errors or omissions. Parties may not reproduce these podcasts in any form without the express written consent of FINRA.
20:18 – 20:24
Music Fades Out