As a graduate student studying finance at the Carlson School of Management, University of Minnesota – Twin Cities, I have heard a lot about the power and potential of data. But I didn’t know what to expect when I started my internship in the equipment finance industry. What is equipment finance? How would my data background translate into this industry? How does AI fit into equipment finance? I had all these questions before I joined the Tamarack team as an Associate Data Analyst this summer.

Over the past three months, I immersed myself in equipment finance working alongside industry professionals, handling operational data daily, and working directly with clients helping them with their data analytic challenges. By assisting multiple equipment finance companies rather than making deals, I gained a special view into how the industry works. During my summer at Tamarack, I found answers to my questions.

Fast pace requires flexibility

The equipment finance industry is very fast paced. There are always deals to be made. equipment finance companies lose almost one third of their book of business every year due to run off, so not only do they need to replace their business, but they also need to win more. Every day I would see the new deals that had been made and tracked by our customers. It is important for a company to have a steady flow of deals to allow them to keep growing. Equipment finance is fast paced in terms of our involvement with them as well. We have regular meetings with clients, internal meetings on how we can improve our products, communication between teams, and a constant effort to stay on the same page with customers. It gets busy when working with so many different people at such a fast pace, so it is important to be as flexible as possible.

Equipment finance has layers

Equipment finance has layers. It’s not all just one company leasing equipment to other companies. Some lessors do something called floor planning or wholesale leasing – when the lessor provides capital to an equipment dealer to stock their showroom floor). Then they collaborate with that dealer to convert the big lease or loan to multiple leases for each single piece of equipment. It’s kind of like a lease distributor model.

Another interesting thing I learned is EF companies do something called syndication. Syndication is where the lessor bundles up a pool of deals and sells them to another lender. In doing so the lessor kind of recycles their capital and then starts all over making new leases with the proceeds from the syndication.
Each equipment finance company has its own way of operating. Some focus on one layer while others include several. It is common for a company to focus on doing specific deals in specific industries, but each company has a different way of operating so no two companies are identical.

Data driven decisions

Data plays a critical part in the operation of equipment finance companies. Every decision that they make is backed by data, so they need a lot of it to make the right decisions. Some data that is used is customer info, payment agreements, asset descriptions, payment descriptions, delinquency information, etc. Working with Aaron Jackson and Jeremy Fisher helped me appreciate just how much data we handle for each customer. We gather data from several database systems that each customer uses and house it all in one area. After that, we can extract the data from our database to build the reports and provide any insight into the customer requests. It has been a great experience being able to use so much data and see how everything fits together to paint a picture of the company.

A lot of potential for AI

The thing that interested me most in joining Tamarack was the AI work that they do. After talking with Spencer Thomas, CEO of a Tamarack client KLC Financial, he said that AI is a new thing in equipment finance, and it could be very powerful. I have been able to work with Khrystyna Voloshyn and the AI team to develop delinquency predictors. There is a lot of back and forth that goes on between us and the customer as we develop the AI models.
Each customer has their own expertise and views on the importance of different data types when it comes to judging and signing deals. They can confirm our decisions and help us change direction if necessary. People have been in the industry for a long time so it can be difficult to explain some of the decisions the model makes since they might have done it a different way normally.

Again, it is a very new technology in the equipment finance industry, so we need to build trust with the customer and show that what we are doing works. It is a tool at the end of the day; It’s not going to take away anybody’s job. Being able to finally apply my machine learning skills in a real-world application has been a very fulfilling process.

Critical to the economy

Equipment finance is what keeps everything moving. It’s what keeps buildings and roads being built, allows people to purchase vehicles, helps farmers produce food, and keeps your favorite pizza shop open. Every business requires some equipment to operate, and this industry helps those businesses source what they need. The industry is an old one, but I am happy to have contributed by pushing a new era of equipment finance with data and AI.
Authors Note: I want to thank Scott Nelson, Tim Appleget, Aaron Jackson, Jeremy Fisher, Khrystyna Voloshyn and the rest of the Tamarack team for letting me work with them this summer. It has been a fun time and a very good learning experience. I have a new appreciation for equipment finance and the future of AI.

Authors Note: I want to thank Scott Nelson, Tim Appleget, Aaron Jackson, Jeremy Fisher, Khrystyna Voloshyn and the rest of the Tamarack team for letting me work with them this summer. It has been a fun time and a very good learning experience. I have a new appreciation for equipment finance and the future of AI.