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Hard Problems. Easy Money.

We’ve recently opened up engineering positions at Exeq, and it’s absolutely fascinating reading some of the initial questions and comments from applicants. Here’s the gist of many of them:

Wait. You’re building a consumer finance application. What does that have to do with large graph processing? The app is beautiful! What are you using machine learning for, though? Why are there two roles for Platform and Product? What’s the distinction?

I’d love to talk a bit about what we’re doing, why we’re doing it, and what it all means for our users. As a disclaimer, this will be a high-level overview of what we’re building for intellectual property reasons (our lawyers are awesome people, honest!), but it should give you a sense for the scope and scale of what we’re up to at Exeq.

What the heck is this transaction?

Have you ever looked at your bank account and wondered that? Perhaps even aloud? Not “what is this transaction” in the sense you don’t remember purchasing that thing or spending that money, but as in you can’t even figure out what the transaction is.

Anyone who has taken any amount of time to glance over their banking application has come across line items like this:

This is simply a Netflix subscription payment.

There’s a lot going on in this data that we as consumers have to read and piece together just to do some basic analysis on it ourselves. Here’s a question for you: why does this data go through at least three systems before arriving in the human brain…still indecipherable? Point-of-sale, credit card processors, our banks…POS systems, indeed!

For instance: you and I can look at SBUX, Starbucks Coffee, Starbucks, and SBUCKS and know that they all refer to the same merchant. But many systems would consider them four distinct entities.

At Exeq, we’re building systems to normalize merchant data so that we can make transactions actually readable. This means a large-scale system that can store and identify every merchant in the world…with very little information to go on from the outset.

2,000,000,000 transactions per year. And that’s just NYC Millennials.

The 1+ million millennials in NYC make 5 transactions per day, on average. Over the course of the year, this is nearly two billion transactions. For just 22–29 year olds in New York City…not including transactions from users we’ll serve in the rest of the country!

Seriously, this is a lot of data. Want to talk about “big data” or “scale”? Try building systems that can process and store information representing an entire economy. “Big data” isn’t a buzzword for us to attract talent. It’s a reality. It’s affected everything from systems design to technical stack choices. We’re constantly thinking in terms of a distributed architecture and concurrency models. We worry about high availability. We concern ourselves with security for large data sets.

Product design isn’t just visual.

We’re designing the tools that generations will use to interact with their money on an everyday basis. We know that colors and font choices simply isn’t enough to make someone want to use an app. There has to be a vibrancy, an energy, some amount of joy in the experience. Let’s face it: banks are boring. Finance applications are dry. At Exeq, we want to breathe life into how you see your finances.

At the end of the day, your finances are a means to an end, and it’s about damn time someone designed an app that reflects that. Despite what the spreadsheet-like apps out there will tell you, it’s not about the money. It’s about what we can accomplish with our money.

We’re constantly having very involved conversations about the philosophy of economics, sociological behavior of financial transactions, and what makes us tick as consumers. It’s fascinating…and important. If we understand why we spend and how we save, we can build better tools that enable us to do both…better.

Managing your finances should be easy. Making daily financial decisions should be easy. Money should be easy.

But easy money means hard problems.