What if a bank was run by machines?

At Aven, our mission is to provide the lowest-cost, most transparent, and most convenient access to capital… by reinventing the bank itself.

The vision

We asked: What if a bank were run entirely by machines?

Why does it matter? Because since the credit card was invented in 1950, the average American has continued to pay well over 20% APR. In a world where automation and intelligence are advancing exponentially, finance has stood still. We think it’s overdue for reinvention.

We’re building a platform for machine banking to radically reduce the cost of capital for everyone. That’s our vision, our “utopia” that we build towards everyday.

Let’s explore that vision and the highly technical challenges we’re tackling to get there—and why engineers at Aven find this journey so exciting.

Why should machines run a bank?

All the inefficiencies of our current banking system lead to costs — costs that are passed on to consumers like us in the form of interest rates and fees. We believe a fully autonomous bank—where moving electrons replace moving physical atoms—will be the most efficient.

This efficiency will result in a radically lower cost of capital for consumers.

A machine-run bank can operate 24/7, with full transparency, auditability, observability, traceability, and built-in compliance. These attributes will make it the lowest-cost option for consumers: both immediately, and in the long term.

The engineering challenges

The engineering challenges of building this are enormous. They range from machine learning, artificial intelligence, and even robotic arms, as we push the absolute limits of what’s possible.

Here’s a sneak preview of just a few of the problems we work on at Aven:

1. Machine Learning at Scale

Capital is a commodity—and in a commodity market, even a 1% efficiency gain is massive. Imagine an auto loan that’s 1% cheaper: 90% of the world’s users would switch to that lender. That’s why automation and machine learning are core to becoming a true machine bank.

At Aven, machine learning isn’t just a “nice to have.” It’s foundational to our efficiency strategy. We spend millions annually on marketing and growth. Our approach involves running a sophisticated multi-model ranking of the U.S. population every seven days—predicting each person’s likelihood of responding to our messaging.

This lets us create highly personalized creatives that spotlight our value prop. The result? Millions saved in acquisition costs and billions grown in balances.

2. Robotics

In the U.S., mortgage and auto loans often still require wet signatures with pen and ink. So we invented robotic arms to let users remotely sign documents, streaming their signature in real time. Inventing our way around the inefficiencies of a legacy industry required blending robotics, legal research, modern UX design, encryption, and distributed system design.

The result? A technology breakthrough that counties across the country now accept.

3. Capital Logistics

We manage billions in credit lines, allocated across multiple “debt warehouses,” each worth hundreds of millions. How do you pack these lines efficiently?

It’s a sophisticated constraint-solver problem—each warehouse has different prices and limitations on who, what, and how much it can serve. Optimization here saves our customers millions, and creates millions in enterprise value. From random seeding to simulation to automatic projections of future customer acquisition, this is a fascinating space to make more efficient and iterate on.

4. Sentinel Runner Agents

Automated regulatory compliance is critical to our systems as we scale. Similar to NASA’s shuttle systems, we run multiple completely independent, production-grade systems that ensure compliance with every federal and state regulation.

Our independent agent code scans every user interaction every four hours. It checks message delivery, account agreement content, and everything in between to verify production compliance.

5. Humans as Sensors

We call our operations “A System of Machine-Orchestrated Work Queues.” At Aven, we’ve introduced a model where machines coordinate nearly every operation, and humans act as sensors. All income tasks are routed by machines into task queues (QUs). Humans step in as needed—for example, to detect fraud (yes, even a man in a hat and fake mustache!) and feed data into our machine systems, which centralizes coordination and final decision making.

Our "humans as sensors" model balances automation with adaptability. Machines handle day-to-day operations with minimal error and process massive amounts of data at scale, while humans step in when things get fuzzy.

Conclusion

For engineers who want to push the boundaries of what’s possible in Machine Learning/AI, software engineering, and financial technology and want to work with incredibly sharp, mission-driven people: Aven is a once-in-a-lifetime opportunity with unparalleled impact and scale.

Here you’ll work with extraordinary technical talent, including:

• The first Applied Computer Vision leads at Facebook
• Creators of Microsoft’s fastest C++ compilers
• Founding engineers of Microsoft Azure
• Founding designers of Cash App at Square

Passionate about solving impossible problems, and want to reshape the future of capital and finance?

We’re already doing it. Come join us.