(Interview by Mireia Martínez i Sellarès)
Elena Paniagua is a Mexican mathematician who works in finance consulting. She got her Bachelor’s degree from the UNAM, the National Autonomous University in Mexico and did her Master’s in Finance Mathematics at Ulm University in Germany. She is currently living in the Netherlands and working at the consulting company Capgemini. In her daily work, she uses mathematics to test different kinds of models used by banks.
Mireia: So, Elena, could you explain what your job is about and how do you use mathematics in your daily work?
Elena: Right now I have a project in the Credit Analytics area in a bank, and what we do is that we monitor and test all the credit risk models that are used for regulatory capital. Basically what that means is that we make sure that the models the bank is using work the way they should. To do this we use a lot of math because we have to perform a lot of tests –that is, statistical tests– which have to be done to ensure the models are working correctly.
M: So you don’t develop the models: you get the models and check that they work the way they should, right?
E: Yes, exactly, that’s what we do.
M: And what do these model exactly?
E: Well, there’s three things that are usually modelled. The first one is PD (Probability of Default), that is, the chances of someone who has a loan of not paying the loan back. The next one is called EAD (Exposure at Default), that’s the amount of money this person won’t be able to pay. And the third one is called LGD, that’s the loss that would actually be there for the bank. With these three things you are able to calculate something that’s called the Expected Loss, that’s the amount of money that the bank expects to lose from a client. What the bank has to do with that information is save enough money to make sure it won’t go bankrupt in case something happens and a client is not able to pay back to the contributors of the bank. So this is why it is called Regulatory Capital, because it is like a buffer of money that has to be saved. This is part of something called “Basel” that is like a global standard for banks to prevent a crisis. So this is what we do: instead of making money for banks, we make sure that they don’t use too much of it so that they won’t go bankrupt.
M: And the mathematics that you use in your daily work, did you learn it while you were studying or did you learn it specifically for your job?
E: I think it was a little bit of both. You see, for this kind of job you not only have to test the model but you have to understand what the model is doing, because otherwise it is really hard to test anything since you don’t know what to check. This means that you have to understand how to build the model and what it is doing: for this you need a lot of math that I learned by working. But also in my master’s I already learned a lot of things about these topics: Risk Theory, Credit Risk Theory, and a lot of Statistics.
M: So what has your career been like? How did you end up doing this job? Did you know early on that you wanted to do this?
E: No. (Laughs.) While I was doing my Bachelor’s I was always very interested in Probability so I took every possible class in the field: Stochastic Processes, Measure Theory, everything theoretical about Probability. And while I was doing so I was looking into what it is used for. I never was really interested in doing pure maths, but for finance you do use all of these theories. To me, it’s really cool because it is all about trying to figure out what is going to happen, so it’s like playing to guess the future and I find that super interesting. Also because it has a lot of theoretical insight behind it, so it is not just a guess, it’s really complex mathematics and I was always attracted to that.
But when I finished my Bachelor’s I didn’t know what I was going to do next. I knew I wanted to do a Master’s but I had no idea in what. What was I going to do with a Master’s degree? I was a bit lost. So while I was finishing my Bachelor’s thesis, a friend of mine called me and said: “Hey, I am working at a bank right now and we’re looking for people who develop models for credit risk. Are you interested?” And I said, like, “I have no idea… I’ll just go for the interview and see how it goes.” It was my first interview ever, I’d never applied for a job before, and I never thought I was going to work in a bank, so my first impression was, like, “Oh my, everyone is in a suit! I don’t want this, I am not used to this.” But I went to do the interview and when they told me what they were working on, I thought “Oh, this is actually quite nice!” I ended up getting the job and I really liked it. A lot of friends from university were working there, so it was almost like an extension of university.
It is there that I learned what it meant to do applied mathematics. It was a combination of a lot of maths and a lot of programming, which I had never done before –that’s something I learned while doing the job: how to write something in a computer so that it would come up with a result. So that was quite nice, but I still had this feeling of, like, “I want to go abroad, I want to learn more about what I am doing,” because while I understood a lot of what I was doing, there were also a lot of things I did not properly understand. With the Master’s I learned everything that was behind what I did, and then I finished it and now I’m still working on the same thing.
M: How come you chose Germany for your Master’s?
E: I always wanted to study abroad. When I was 15 years old I came to Europe for the first time and I was in Berlin. I remember walking around and I saw the Humboldt University, and I remember thinking “This looks really nice.” I had read about it somewhere else also, there is a Probability school there that is super good so I thought “Hey, I want to study here.” And then I started thinking about Germany. In fact I was never interested in the States or Canada, I was always more inclined towards Europe.The other thing is that, in Germany, education is free and that is a big incentive if you are not from Europe.
So I applied to the Humboldt University but I didn’t get accepted, which was better in the end because that was a pure math master’s program, but turns out there was another one that was more finance-inclined. A friend of mine told me about it at the same time as my mum did. So I looked into it and liked that one better because it had a lot of classes that were related to programming and understanding practical stuff better. I really liked it, applied, and I got in.
M: And if we go all the way back, how did you decide to study maths in the first place? How did little Elena know?
E: (Laughs, then thinks.) Uhmmm, because I was good at it! Math was always something I found easy to understand. I know it’s super cliché but I really think math is the basis of everything. Like, if you understand math, you understand how the universe works, basically. Do you know what I mean? It’s just going to the beginning, it’s understanding the real basis of everything. So math to me is basic knowledge, it is as basic as it can get. I just really like that it’s simple. Not “simple” as in “easy,” but simple in the sense that it is constructed in a logical way, it has a very specific structure. It’s not like other disciplines, where theories are open to interpretation. But, at the same time, math is a really creative field, because there are some many problems we don’t know how to solve! We just keep coming up with new solutions to solve them. And things do get more complicated as we go on, but there is still so much to be done, so we have to keep thinking of ways to improve our understanding.
M: Do you explore this creative aspect of mathematics in your job?
E: Yes, precisely because there are a lot of things that we don’t know how to do. There are so many new challenges coming up and we have to keep up. We are always thinking of new ways to do things. A concrete example: with AI and machine learning, there are a lot of new tools being developed but we also need to know how to test them. So we are looking into ways of thinking, like, how do we do this? Should we go in this direction or in this other one? For example, there is a portfolio that is going into crisis and we are trying to develop a model for it. You go check it and you see that some aspects of it are different than usual, so how do you test that? You need to find news ways to do so, and this happens all the time.
M: On a separate note, do you think that events or activities like the EGMO, which are just for girls, are necessary? Do you think that you would’ve liked to participate in something like this as a kid?
E: Yes, I do. You see, I never felt alienated for being a woman wanting to study math. Maybe it’s because my parents are also in that area of research so I never heard negative comments like “Wow, you’re a girl! You shouldn’t study math.” But now, as I grow older, I really feel there is a difference in gender equality. And it’s really, really clearly there: it’s not just a subtle thing, it’s very obvious. I am one of the only women in my department that is doing the sort of job I do, and the number of applicants that are women is really low compared to men. So I think there is something that should be done there. I think that women should be encouraged to do this kind of jobs, to not feel like it’s a “man’s world.” The more we encourage girls to keep on doing mathematics, to go for science, the better. I think it’s really important to not be afraid of choosing something different just because you are going to be the only one doing it. The more we get women to participate in science, the less alienated they are going to feel. It’s really important to make sure we work towards that goal.