NXTSoft is the Mulesoft for FinTech. Read on to understand the positioning better.
Sramana Mitra: Let’s start by having you introduce yourself as well as NXTSoft.
Rich Longo: I’m the Chief Strategy Officer and Division President of our data and connectivity business. NXTSoft is a data, connectivity, and cyber security business that is focused on protecting, connecting, aggregating, monetizing, and automating the data. Our solution is focused on those four areas.
Sramana Mitra: Can you talk about what kind of customers you are catering to? Could you also do two or three use cases to illustrate what you are talking about?
Rich Longo: We focus primarily on the financial technology area. The majority of our clients are banks, credit unions, and insurance companies. I will give you an example of a use case around some of the customers that utilize our product.
Our customers want to have a better digital experience on the lending side. If you think about it today, I would go ahead and inquire about a loan or a mortgage that I want to get pre-approved. That institution might already have the information about me because I might have additional accounts with them.
Rather than inputting the same information like my address, income, and other relevant information, we connect to the other disparate systems within the institution to drag that information in so that it pre-populates the information needed. The customer can go through the application process much quicker.
In essence, they are just updating the information. Our connectivity channel will take the information from the disparate system and put it on the application for them. As they are updating it, we will transport the relevant data back to those relevant solutions to update that data. Then they go through a process of underwriting.
If you think about underwriting today, there’re a lot of humans that would touch that underwriting process because of the disparity of these different systems that you use. You might use one system for the actual underwriting, but it needs to connect into credit reporting, the three bureaus, the fraud system, and a risk solution system. It then needs to go ahead and pull other relevant information such as whatever current data exists from a lending perspective in the institution.
That institution holds three of the twelve loans that the customer has. Obviously, they want the most up-to-date information. Credit reports are usually refreshed once every 30 days, so our connectivity would then grab to that system and import that information.
The underwriting system would then take all that data and decide whether they want to proceed and approve them or not. At that point, assuming they are approved and we’re ready to lock in a rate, our system would provide the connectivity to another system to look at the rates. For that minute, our system would price them, transfer in that rate, and lock them in.
Now, you are at a point where you want to get funding and you need the appraisal reports. Those are the other third-party systems and we again provide the connectivity to that. These connectivity points used to be done by human beings. They would get this information together, attach it, click on a checklist, and go through that process.
With our system, we are saving time, money, and taking the human capital element out of it and potentially some human errors. Now, let’s take you through the remaining part of the journey. You are now ready to close on that loan.
There’s another third-party system that has all the loan documentation. We transport the data from the underwriting system to the documentation system, then it’s ready for closing. We then provide the connectivity to transport that file securely to a third party to close that loan and remit that immediately to us or the institution so that they can trigger the funding mechanism.
We will trigger the data flow to go to the General Ledger (GL), then transfer the money to a holding account that would then go into a wire system, which is another disparate system to trigger the funding of a loan. Now that the loan has been funded, the files then need to carry over into the servicing side.
If you think about all the data elements that I talked about from the underwriting to the documentation for the loan to the GL into the actual servicing log, all that information gets transferred through our connectivity into a servicing record that’s automatically built without a human being.
That would give an instance of what we do today in potentially taking out 40 to 60 hours worth of work over that time. We take out a lot of friction, helping a customer or an institution close a loan faster. That’s just a part of what we do.
Think about all the points of transfers that involve risk, especially if you are transmitting it from one data center to another. It’s not contained within the financial institution data center because of their cloud systems. We secure the end-to-end data transport and we encrypt and monitor that too.