UMBC Mic'd Up
UMBC Mic'd Up
AI Meets Finance | Real World Learning with UMBC Data Science
In this episode of UMBC Mic’d Up, host Dennise Cardona, chats with Dr. Abdullah Karasan—adjunct faculty member in UMBC’s Data Science program and Founder of Leveragai—about the intersection of AI, finance, and education.
Dr. Karasan shares his journey into financial data science and offers insight into his innovative new tool, Stockaivisor, a generative AI-powered platform built to simplify investing and learning for both beginners and professionals. He also highlights how UMBC students helped build the product and how hands-on learning is preparing graduates for in-demand careers in AI and fintech.
Useful Links:
🔗 Learn more about UMBC's graduate programs in Data Science: https://professionalprograms.umbc.edu/data-science/
🔗 Explore Stockaivisor: https://stockaivisor.com/
🔗Explore Leveragai: https://www.leveragai.com
Dennise Cardona 0:00
Hey, welcome to UMBC Mic'd Up podcast. My name is Dennise Cardona from the Office of Professional Programs, and today we have a very special guest with us, Dr Abdullah Karasan, and we're going to be talking about AI finance and real world learning through stock. Ai visor. Welcome, Dr Karasan, and it's so wonderful to have you here on the podcast to talk about your latest and greatest product that you've launched and to talk a little bit about your experience as an adjunct faculty member here at UMBC.
Dr. Abdullah Karasan 0:34
Of course, thanks for having me. It's great honor for me to be here. I'm really excited about this podcast.
Dennise Cardona 0:41
Excellent. Let's start off then with your story, right? So, how did your journey lead you to work at the intersection of AI and finance? Okay? Like
Dr. Abdullah Karasan 0:55
My major is in economics, and while I was purchasing my PhD in the field of financial mathematics. I decided to work on the financial data science in my thesis, in my PhD thesis. So starting from here, starting from my PhD thesis, I decided to switch my career to data science because I saw lots of opportunities there. Very important to have a specific domain knowledge in the field of AI. So knowing finance gives its power, gives you this stage. So I believe I can contribute to this field, knowing finance and AI same time. So since then, I consider myself as a financial data scientist.
Dennise Cardona 1:38
I love it. That's a cool role and a very important position these days. So you've been an adjunct faculty member at UMBC in the Data Science program for some time now. What has that experience been like for you?
Dr. Abdullah Karasan 1:52
It's great. It's a great opportunity for me to be a part of academia, because it's kind of, you know, my ultimate goal always since my undergrad years, because I decided to be a part of the faculty. Since then and then I completed my PhD, I just want to be a part of UMBC, because UMBC provides really competitive and comprehensive and they up to date, programs to the decisions. So being a part of it and help people grow, help myself grow, is something that I don't want to miss. And being a part of an academia also write more papers, books and something like that. So this also, I feel alive when I'm part of the academia, because when you fully focus on your day to day jobs, or like, sometimes you lose track. So chief me focus being an academia. Yes, absolutely. Focus is very important. But it's so wonderful to be able to say that you can actually lose yourself in your work because you are passionate about it, you have such a love for it. And that's just a really big gift, I think, in life, because not everybody has that. And so yeah, it's a really big gift to be able to say that you can you feel that way about work. Yeah, it's important what you're opening, right? So otherwise you spend, like, hours, 10 hours in a day. So if you don't like what you're doing, it's a big disaster.
Speaker 1 3:22
that's
Dennise Cardona 3:22
that's right, how have you been able to collaborate with UMBC students and alumni in your work, especially in hands on projects?
Dr. Abdullah Karasan 3:33
Trust me, it's very easy, like we have full talented NAU students. I guess I work with four or five different students from UMBC, and two, three of them I closely work in the long term, in the long term. So they're really talented guys, but the most important part is that our product is generative AI based products, and I helped them to sharpen their skills in the generative AI now, I strongly believe that they have the skills and we sometimes sit down and chat, and I think they also believe that they have a very solid background for now with the during the process of generating the these products have done a lot.
Dennise Cardona 4:23
What a great opportunity for graduate students. That's one of the things that I love most about being a member of UMBC community, as a staff member, but also as a an alum. Now, because I graduated from one of the master's programs here at UMBC, and one of the things that I found and also from being here as the host of the podcast and talking with so many faculty members, is that it's so hands on. You all are out there in the real world doing real things right now, real time, and you're bringing that to the classroom. And gosh, what an amazing benefit that is for students. Yeah, those students are very lucky.
Dr. Abdullah Karasan 4:58
Yeah. Hopefully, yeah, hopefully, absolutely.
Dennise Cardona 5:03
So tell us about you have this new product, right? You have a company leverage AI, and under that company, you have this new product that you put out, stock, AI, visor. Am I saying that correctly? Okay, what is it and what does it aim to solve in the financial world?
Dr. Abdullah Karasan 5:21
yeah, thanks for bringing this up, bringing this up. So the thing is, what we are trying to do with stack advisor is to make things easier for the financial investors. Using generative AI is very limited in the field of sorry, using AI in the field, in the financial field is very limited. So what we try to do is to tackle this problem. So our product is very generative, AI intensive product. So what we try to do is to make things simpler for the newbies or maybe the professional users, because we have some advanced tools for them too, and that's why all you need to do in our product, in stack advisor, all you need to do is to click on Run, and then we compile all the information, daily, intraday, historical data, and then get together all this information and return you an advice as result, saying that due to this and that you can do this or that. So it's very easy for you, for the investors or the irrespective of irrespective if you are a newbie or a professional. And additionally, we have an academ and which is another generative AI tool. All you need to do is to write a prompt and recreate up to 20 hour long content for you so that you can understand each and every financial topic in at your pace. You can learn it at your pace. So this is another, a new feature inside the stack advisor.
Dennise Cardona 7:01
So I had the privilege of you showing it to me. We spoke before this podcast recording, and you showed me the interface and one thing. So I'm excited about this for many reasons, but one from a personal standpoint, I'm learning how to work in the stock market. I'm learning how to do paper trading right now, just playing around with cryptocurrency and some stocks, and I'm using paper trading, and I'm using technical analysis, and your product does all of that. It helps to it showed me that I'm able to do technical analysis within it using AI. But also what I love is a lot of us who are new to stock trading and learning how to do all of this investing. It's very overwhelming. It can be very intimidating. And one of the things that your product does is it, like you said, it acts like a teacher. You ask it to give you a course on how to read technical analysis, how to read candlesticks, anything that you want to know, it's going to teach you that. And so you're really forming a solid foundation, and educational knowledge based foundation that works in tandem with the AI. So it's a very cool product.
Dr. Abdullah Karasan 8:15
Yeah, sounds like you're one of our potential customers.
Dennise Cardona 8:19
Indeed. Yes, I will be definitely checking it out, because I have a I guess you call it a mastermind Alliance, where there's a group of us who are learning how to do this together. So I told them, hey, soon we're going to be going to be looking at this product to see because I just think it's so cool. Yeah, what makes this product stock AI advisor, different from traditional financial analysis tools?
Dr. Abdullah Karasan 8:47
yeah, like simply puts generator AI usage, because we take advantage of generator AI in many aspects of the of the product, in many features In this product, like in stack advisor Academy, in chat bot in robo advisor, daily reporting, predictive analysis. All these features are using generative AI, and it makes it faster, more accurate and easy to use for the users like but if you're an advanced user, don't need that. Advanced user also needs this type of product, but let's assume that they don't we have some strong and advanced tools as well, like return analysis, factor analysis, this type of analysis are more like addressing the advanced users, and we have chart you can run your own technical analysis using intraday data, generative AI makes a difference.
Dennise Cardona 9:40
Yeah, sounds like it now, you mentioned that UMBC students helped build this product with you. One of them is a co founder of your company, leverage AI, which is amazing. Can you talk about what roles they played and what it was like to involve them in this live project?
Dr. Abdullah Karasan 9:58
Sure, Mostly, they have the role as a data scientist and software engineer? So I was writing down the code, but I'm not coming from software engineering backgrounds. I know my background is financial mathematics and financial modeling, and I write down the technical code, say the course or the technical parts, and I deliver my code to the team, and they just deploy it. So all the you know, the product is in Microsoft Azure Cloud, so I'm not very familiar with the deployment and other stuff. I just create the stuff in my local and deliver the code to them, and then they deploy and connect it into the user interface like the website. Mostly they have done these tasks, like sometimes they help me to create the product, but mostly they just work on the software engineering part. Now, what do you think that those students are going to be walking away with and how might this kind of experience help to shape their careers in the future. Yeah, you know what? I talk with them a lot about this. So what you have learned, so how you think you can use it? Like I would like to continue with that, first of all, like I don't want them to be to go to the job market and find another job, because I really like this experience. Hopefully they like to but I think the students who have the software engineering role improve their skill in developing a product which is based on generative AI and cloud, cloud tools. So because these are the tools that you should know before going into the job market, I'm one of the person should that this give them a competitive age in this in the markets, because software engineers just focus on either front end or back end. But when you combine AI and software engineering skills, this makes you a really competitive candidate in the market. So that's why I'm quite sure that if they go to the job market, they can, they can lend a dream job. But it doesn't necessarily mean that I want them to go to the job market.
Dennise Cardona 12:12
I get it. You sound like you have created quite a dynamic team, and when you find folks that you can work with, that you can trust, that you can you know they're bringing their value and together, that value that all of you bring creates this magic that is really hard to repeat with a different dynamic. So totally understand, but if you find that right group, yeah, do you want to hang on to that.
Dr. Abdullah Karasan 12:41
Yeah, when you start telling someone the issue, whatever, it's really important for you to be understood. Like, I start telling the problem to my team, and they just understand it, because we have been working for like, a year now, so I need to spend hours to tell what's going on, what we should do. Something like that. They just understand what's the point and how to solve it. So this is very valuable to me.
Dennise Cardona 13:06
I'm curious, from a point, from a point of view of a graduate student, how did you pull these particular graduate students into this project? Did you see something in them that you're like, they're gonna know what to do with this and then you just pull them into it. How did that work?
Dr. Abdullah Karasan 13:23
Yes and no. Actually, yes for my co founder, because he is one of my grad assistant teaching assistant, I just suggest him to join our team and to be a co founder, and then we work together. And the other guys is coming from the background, because I don't know many people from software engineering backgrounds. One of my former students suggest me a couple, suggest me a couple of names, and then I sit down and work with them and still continue to work with them. Yes to my circle as a co founder, but also referrals also important points, because if the students are studying grad degree in the UMBC, it's highly likely that they are talented, so that's why it's easy to work with them.
Dennise Cardona 14:14
Shifting gears a little bit from your perspective, where is AI in finance headed Are there any trends that excite you or maybe even concern you?
Dr. Abdullah Karasan 14:26
Actually, yes, we have some fields that you know exactly me and also concerns me. So first of all, the graph knowledge, I'm not even talking about how generative AI is getting bigger and bigger in the field, but the the graph knowledge and graph network and the causality analysis, maybe the next two big things in the finance because in financial analysis, it's very important, very important, to detect the relationship between the variables. So if you're using a complex data, big data, or a text based data, it's really hard to do that, but recent advancements, especially the rack, maybe the raft, like retrieve augmented fine tuning analysis, helps us to better understand this relationship combining with the Knowledge Graph and causality analysis is also help us to better understand the relationship. So these are the things that I'm focusing on currently, and I see that the financial data science is growing towards this field. The other thing, what concerns me is the lose regulation in AI. This is a common problem in the field of AI. The regulations are not that strict, and that's why there are some obvious violations, especially copyright issues as you know it, the companies are trying to bypass this copyright issues, simply putting an argument called like fair use. But I don't know if it works, but we'll see. Time will show. But developing a product based on generative AI is something that we need to be very careful in terms of legal restrictions, and the legal perspective is not something that we can overlook.
Dennise Cardona 16:32
Yeah, that sounds like a big challenge, but it's definitely a necessity to be able to look into that, the ethical concerns, the legal concerns, there are so many challenges when it comes to generative AI, especially in a high stakes field like finance, I would imagine, yes, yeah. Now for students or career changers interested in both AI and finance, what skills or mindsets should they start to develop now?
Dr. Abdullah Karasan 17:04
Yeah. Like, interestingly, these two fields are growing too fast. Like, I noticed that most of the successful startups are FinTech startups. If you check the list of 510, most successful startups in the United States, you will see that there are many tech startups there. So if you combine financial knowledge with AI, this makes you a really competitive candidate, for sure, because these two fields are will be the field of field in the future, right? You should know finance and AI too. Like, even though you don't have to be like, no all. You don't need to have a solid theoretical background. But if finance and AI and this gives you a really important age in the job market. But if you're not a candidate in the job market, still like intellectually this grow. And the thing is, don't be discourage yourself. If things feel overwhelmed at first, everyone starts somewhere, right, focusing on focus on building a strong foundation. Stay curious, never stop learning with consistent, consistency and passion, you will find your place, that's for sure. This is how it works, generally. So this will be my piece of advice.
Dennise Cardona 18:33
What makes a graduate program like UMBC data science program or even UMBC software engineering program fit for this type of future focused work.
Dr. Abdullah Karasan 18:43
As I said at the very first part of our conversation, like these programs are very comprehensive, includes many hands on practices and also up to date. So if you combine these three, you have everything you need, right? Because we just, I just teaching the field of data science, so I know that we teach everything from top to bottom. So when you properly learn in these two years, when you properly learn what we are teaching like you will be ready for the for the entry level positions, or the just the others as well. So that's why keeping it, keeping the coverage comprehensive and keeping it up to date is the key in creating a successful program, especially the masters program.
Dennise Cardona 19:39
Yeah, and it goes back to what I had said earlier, that I love how UMBC has adjunct faculty who are people who are out there doing the work, the real work, right now, in real time, because everything's changing so fast, and it's not just based in theory, it's based in applied real life scenarios. So yeah, it's a really great opportunity for students and UMBC programs to really be able to learn from people who are out there doing this stuff.
Dr. Abdullah Karasan 20:13
Yeah, it's changing so fast that I couldn't sometimes keep up, like when I tried to read the paper and say, Okay, it's what's going on here. It's a brand new field. So this doesn't happen in finance, but in financial data science, things are changing because of the speed, the change of speed in data science.
Dennise Cardona 20:33
Indeed, this has been such a great conversation. I've thoroughly enjoyed it, and I really I cannot wait to check out the stock AI advisor, I will be linking to that in the description, so folks who are listening, who are interested in checking it out, you'll be able to get to it from the description. Thank you so much for sharing your insights with us today. It's been great. Yeah, I appreciate it. Thank you so much for inviting me. It's really pleasure for me to be here with Dennise, yeah, and thank you everyone for listening tuning into this episode of UMBC Mic'd Up podcast. If you'd like to learn more about our offerings, check the links in the description. Thank you so much for listening.
Dr. Abdullah Karasan 21:11
Thank you.