(6 Minute Read)

A conversation with Andrew Hellrigel, Technical Analyst

We sat down with Andrew Hellrigel, who recently joined the Ledgestone team as a technical analyst. He told us about his work with the transformation of insurance coverages, machine learning, and how computers and humans can coexist and complement each other.  

Tell us a little bit about yourself, and how you found your way into your field and to Ledgestone.

A:

I am a recent graduate of Georgia Tech in Atlanta and got my degree in electrical engineering. My focus was on signal processing and machine learning. When I was searching for jobs, the main things that were important to me were smaller companies with a good culture and variety in the work that I would be doing. I wanted lots of opportunities to learn new things and grow within the company. Thirdly, I wanted to be a leader in some capacity, whether it is just over my own projects, or leading other people.

I heard about Ledgestone from Kevin Dill, who was my neighbor growing up. Initially, I was off put by the type of work I would have an opportunity to work on because it was primarily software focused. I have a lot of background in mechanical and electrical engineering and a lot less of background in software engineering, specifically DevOps, which is a lot of what the Ledgestone opportunity was. But it became clear that it would be an awesome opportunity to learn a lot and fill in a lot of the gaps in my knowledge. Beyond that everyone that I talked to was super, super great. It seemed like there was a good company culture here and it fit my personality very, very well. And that is what drew me to accept the job and I am glad that I did because it has been a lot of fun, and in a brief time I have learned a lot I get to do a lot of like, the stuff that I am interested on the technical side, some software development, and some of the machine learning techniques and artificial intelligence knowledge that I bring to the table.  

Technical analyst seems like a broad role title. How would you describe your role and some of the work that you are focused on?  

A:

The goal of an analyst is to analyze information. And within the context of insurance, the information that is available about insurance topics is very fragmented. There is no quality source to just learn about insurance as a concept. So, I have been gathering a bunch of information, materials, and resources, whether that be reading blogs, reading white papers, going through other insurance companies' forms, going through legal documents, talking to insurance agents, and then compiling that information into one place where it is more accessible and easier to understand.  

Then the main goal I have is best described by an analogy. If you look at how Netflix takes information about users and tries to recommend good movies for them to watch, In the same way, what we want to do is take information about businesses and recommend the coverage that they need to fully mitigate the risks that they have. And we want to do it in a way that is easy for them to understand, and much more efficient for us.  

So, within that, how would you describe machine learning and its impact on the process of recommending the right coverage?

A:

At its core, the goal of machine learning is to take data that we have collected about a company and use different mathematical and software processes that are remarkably like how the human brain makes decisions. We are taking these biological processes of neurons firing that make up the human decision-making process and then using algorithms that function very similarly to that to create a computer program that can analyze a lot of data and make decisions like a human can.

That raises an interesting point, if you are just trying to make a program that makes decisions like humans do, like, why not just go with my insurance agent that I have been with for 20 years? What are the advantages of using a computer and machine learning as opposed to simply having a human with experience making that decision?

A:

The thing that you cannot replace is human connection. And that is something that our company values a lot. A lot of our employees here do an excellent job of forming human connections with other business owners.  

The problem is that they do not always understand or do not have the knowledge to assess that business’s risks if they are not super familiar with the industry. The main thing that machine learning does to augment human business and decision-making processes is that it will always improve over time.  

So, if the algorithm decides once, and we discover for whatever reason that it is wrong, we can update the algorithm so that it makes the correct decisions in the future. And now everyone that is using the machine learning tool to make decisions will make the correct decision in the future. So, it is somewhat of a transfer of knowledge and allows a company to take people that do not have knowledge about a particular space and give them the tools to make decisions about that space without needing in-depth knowledge.  

When it comes to the insurance business and this process, what have been some of the challenges and rewarding components of that journey for you?

A:

The most challenging has been just navigating through the wide variety of information about insurance that does exist and trying to organize those ideas and thoughts into something that is more cohesive and understandable. It presents a lot of challenges to solve.

The most rewarding thing is just getting the opportunity to work with people that are also excited about what they are doing. Because it is super fun to see that, and having co-workers that trust you, and being able to trust them, creates this environment where a lot of productivity and innovation can happen. And that is not something you see very often.

What is something you really enjoy about your role or are looking forward to?

A:

I really like the idea of developing a product from scratch and getting to see the entire lifecycle of that product grow. So, what I am looking forward to most is just seeing what this risk assessment tool develops into over the course of me working on it. Additionally, just seeing people get added to this project, and how their different abilities and backgrounds play into how the product continues to develop.

To bring it back to machine learning and your background, what do you see as the future of our work on culture and how ML will impact that?

A:

We have been seeing across all industries that data is an immensely powerful tool. This goes back to your earlier question of the advantages of machine learning. I did not really touch on this one a lot, but another advantage is that a computer can process lots and lots of data much, much faster than humans can. So even though right now humans have a better decision-making process, we are bottlenecked by the amount of information we can consume to make those decisions. And that's where machine learning can come in to augment that decision making process. So, I think that using the tools of machine learning we can digest a ton of data to get great insight into the company's culture, discover trends across industries, understand how our training and techniques are improving businesses, and can continually improve the way that we drive cultural change.

In your past roles, have you seen major cultural differences? If so, how have they affected you as an employee?

A:

I have had three jobs, working at small, medium, and large companies. What I learned about those distinct cultures is that for me in the larger companies there is a lot of bureaucracy and hoops you must jump through to get things done. It took me two weeks just to start doing anything because of how I had to set up my computer and deal with other elements of the company. Contrast that with when I worked at smaller companies where I started working the day I got there. I was straight into it. I had more control over the projects in smaller settings and it was clearer how the work you were doing was directly impacting the company.  

At the larger company, in my role I still had a good boss, and he was still genuinely fun to work with. But I can see how, had I not had a good boss, it would have been miserable. I recognize that for me, the environment of small businesses is better because they are a lot more agile, and I could work on more unique projects.  

Well, we hope that your role here at Ledgestone allows for a ton of that! Thanks for taking the time to sit down and chat!

A:

Of course! I am excited to be a Ledgestone. It has been a ton of fun so far. I am really excited to see how this company grows.  

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