Meet The Team: Benard Akalbeo

Meet The Team: Benard Akalbeo

Benard Akalbeo is the new Vice President, Quantitative Analyst at MountainView Risk and Analytics, leading the Quant Team as they continue to provide detailed analytics for our customers. Before joining the team at MVRA, Benard put his analytical skills to work in the insurance sector focused on risk, while also completing his PhD in Economics. In the interview below, Benard gives us a deeper dive into his background, his passions, and his extensive knowledge in risk and the industry.

Q: Can you please introduce yourself?

A: Hello, my name is Benard Akalbeo. I have a PhD in Economics from Georgia State University. While in Grad School, I had the opportunity to work as a Graduate Research Assistant with industry-level data, building logistic regression models to predict the probability of default of projects that we had from the industry. At the end of the day, I knew where I wanted to go. I wanted to do something quantitative. Two of the professors that I worked with did some interesting industry-level work and because of that I also developed an interest in machine learning algorithms, casing models, predicting and clustering models. So I have some experience doing all of these things. My forte is in the quantitative field, with anything that has to do with modeling such as building models themselves, challenging the model, evaluating the models and then providing appropriate validation for the models. That’s a little about me.

Q: Prior to your role at MVRA, have you worked in the banking or finance industry?

A: My role as Vice President of Quantitative Analysis at MVRA is my first official job in the banking space. My prior role was in the insurance field and while it is closely linked to the banking sector, it is different in its own right. Before joining the PhD in Economics program, my background was in actuarial science, which has to do with predicting the probability of a contingency occurring then making provisions for it. Just after I graduated with my first degree, I worked at an insurance company where we built life insurance models and then monitored them to make sure they were working effectively.

Q: What are some similar trends you see between insurance and banking?

A: Risk. Risk is everywhere. Normally, in an insurance company, you are looking at the probability that somebody defaults on their premium payments. It’s like you have guaranteed clients a certain amount of money that, in the event of a contingency, will be paid out to them. But before they can enjoy that lump sum promised to them, they should also hold on to the power of the bargain. They are supposed to make sure that they consistently pay their premiums. So, when you look at the probability that someone will default on their premium, which means their account goes delinquent, it’s not too different from, say, a mortgage in the mortgage industry, which is closely linked with banking. Where maybe a bank provides mortgages for people, then some of those people end up defaulting on the payment arrangement and as a result, the loan becomes delinquent. Sometimes if you see that the loan is in trouble, you need to restructure. So, the concept of TDR comes in. It all has to do with making provisions in the event of a risk, or a default. In insurance, we did something called economic capital modeling, which is basically setting aside money for unexpected losses or unexpected defaults. You can translate the same idea to the banking industry, whereby with these new models, you need to make provisions for potential default, expected credit losses, the potential losses that you’re likely to take from your credit portfolios. So, there’s a good link there from the insurance industry to the banking industry, but data protection of the knowledge and module of conducting all of these analyses is a little bit different. These reserves, for lack of a better word, are prudent for every company to set aside to avoid stressing the business when expected money isn’t realized.

Q: Can you tell me about the CECL model?

A: To be honest, when I accepted this role was when I started learning about the CECL model. It made perfect sense because prior to CECL, businesses would normally use the incurred loss methodology to account for unexpected losses. The problem with this is it is backward looking. It doesn’t really take into account historical values or events that aren’t carried in the present. How about if something is going to happen in the future? So this incurred loss methodology was restricted in the sense that it didn’t take future events into account. With the advent of the 2008 Financial Crisis, they saw that this methodology of forecasting for losses was restricted. The CECL, which is current expected credit losses methodology, was actually enacted in order to replace this incurred loss methodology. You don’t have to wait for losses to actually be incurred. It was always prudent to take into account the future. What if there was some form of exogenous shock? We saw that in the 2008 Financial Crisis a lot of businesses couldn’t cope because they didn’t take this into account. Then in 2020, COVID happened. There is always bound to be something happening in the future with significant potential effects on loss management; so it makes sense to add a future forecast component to the present and then the current events that we use in making reserves for our credit facilities, which is credit losses.

Q: What do you expect around your day-to-day role at MVRA?

A: This is only my 5th week, but I have a general idea of the work I’ll be doing. In addition to CECL models, I’ll be doing RATA compliance, which has to do with the Fair Lending Act. It makes sure that when people apply for loans conditional on similar characteristics, no one is discriminated against based on the color of their skin, where they live, their debt-to-income ratio, things like that. That is actually tied to something we do in economics that we call treatment effects, whereby you want to have a subject which is the target and then a control, and you want to see if there are any systematic differences between them. Also something that I am currently looking at is the automated valuation model, which generates reports for loan portfolios in the real estate space. So, from the day-to-day, most of what I’ll be doing as a newbie, will be basically getting myself familiar with how some of these models work and then I will be validating the models. I need to still get used to how things are done in the banking space. For example, if you are writing a validation report, you need an executive summary, model monitoring, governance of the model, things like that. I’ll say, from day-to-day, I am going to continue learning how the processes work. I have learned a lot already from reading old reports. Next, I can move into the analysis bit, sooner rather than later.

Q: Can you give us a little more on your background and where you are right now?

A: I’m currently in Ghana, Africa. This is where I was born and grew up. I was here before coming to the United States to continue my education. There was a family emergency that I needed to come back for. I am working from here for the time being. The time difference is about five hours. I have adjusted my time so that it is the same as when I was working in Atlanta, so that I am still able to work with my teammates and learn from them.

Q: Can you share with us a little about your upbringing?

A: I grew up in the northern part of Ghana. The North is somehow different from the South. I’m in the South right now, in Accra. The South is relatively richer and not just in Ghana. When you take other African countries like Nigeria, Burkina Faso, even the Ivory Coast, the north is always lagging behind the south. As I learn more, I try to look at emerging markets and the lack of information symmetry. The banking space between the north and south is entirely different. So I plan to use my knowledge to study these things and see what is actually driving the differences and how to bridge the gap. 

Growing up in the North was quite tough for me, then after some time, my mom used to live in the South. So, I used to live in the North by myself, but later on I joined my mother and went to high school in the South. My grades wouldn’t have been as great as they were when I came to the South. I had my high school and college education here in the south. After college, I worked for a year and then applied to do my Master’s in the United States. I think God had other plans for me. The director of the PhD program called me and told me he didn’t think I should waste my time on a Master’s degree and encouraged me to do a direct PhD. So, I took the opportunity. It was tough from the beginning, switching from an actuarial science background to economics, but because of the math background that these two had in common I found my feet after the first semester and the rest is history. I’m almost done, I just need to defend and finish the formalities at school.

Q: What do you enjoy outside of work?

A: Outside of the United States, the biggest sport is soccer. I grew up playing soccer. When I am in Atlanta I go to Georgia Tech to play because Georgia State doesn’t have a stadium, so I go to the neighboring school. I play on Friday nights. I also read a lot of books. My interest span is between finance, economics, politics and sometimes fiction. I also enjoy movies. 

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