🛡️ Visa Verify

https://screen.studio/share/Prh576Er (demo video)

Inspiration

Every day, over \$1 billion in credit card fraud occurs globally, impacting both consumers and merchants.
We wanted to build a system that brings trust and transparency back into online payments. Inspired by Visa’s commitment to secure transactions, our team set out to create a smarter way to detect fraud before it happens.

What We Learned

We dove deep into the world of behavioral biometrics, learning how subtle patterns like typing cadence, mousepad pressure, and scroll rhythms can uniquely identify a user.
These non-intrusive signals help distinguish between a legitimate customer and a fraudster using stolen credentials.

We also explored device, network, and behavioral analytics, understanding how combining these data points can drastically increase detection accuracy.
Our risk scoring algorithm analyzes these signals in real-time to accurately flag potentially fraudulent transactions without interrupting the user experience.

How We Built It

We built Visa Verify using a combination of web technologies and API integrations:

🧩 Chrome Extension – Simulates how vendors would embed the verification widget into their checkout process.
🌐 Frontend – Built using HTML, CSS, and JavaScript, designed to be lightweight and responsive.
⚙️ Backend API – Developed an endpoint using Node.js and Express for merchants to call that returns a transaction’s risk score and top risk factors. Serves as a simple plug & play product for merchants to power their platforms with fraud detection using just a few lines of code.
🎨 Design – Crafted in Figma, inspired by Visa’s brand aesthetic; clean, professional, and trustworthy.

Our focus was on keeping the interface informative yet calming, with clear visual feedback for both users and merchants.

Challenges We Faced

One of our biggest challenges was design. For many of us, this was our first time creating a full product aesthetic from scratch. Balancing simplicity with information density took several iterations in Figma.

We also spent significant time ideating and refining our fraud detection model, determining which metrics (like behavioral patterns, device consistency, and transaction velocity) best predict fraudulent behavior without introducing false positives.

Finally, integrating the Chrome extension, API, and merchant dashboard into one cohesive system required a lot of coordination, version control, and testing.

The Result

Visa Verify provides real-time fraud detection through a seamless, user-friendly interface.
Each transaction is given a risk score and the top contributing factors, such as:

VPN usage
Unusual behavioral biometrics (typing cadence, pressure sensitivity & frequency of clicks, scroll movements, etc.)
Large or abnormal transaction amounts

Transactions above a certain threshold automatically trigger multi-factor authentication (MFA), adding a secure step before completion.

What’s Next

We plan to expand Visa Verify by:

Integrating directly with payment gateways like Stripe, Shopify, and Square.
Leveraging machine learning models to continuously improve accuracy over time.
Building a developer SDK for easier integration into merchant platforms.

Our vision is a world where every online transaction feels secure; for both merchants and customers.