Small Business Lending Identity Crisis: KYC, KYB, and What’s Missing

Underwriting small business loans is not as simple as it sounds. You get what appears to be a legitimate application. But then you notice the same phone number from another business that defaulted last year. Or the domain name has no web presence. Or it’s tied to a personal credit profile that just doesn’t add up. You dig in, just to be sure…and suddenly you’re spending an hour on one file, trying to piece together whether this is a real business or if someone is trying to game the system.

That’s the reality of small business lending. Small businesses are complex, and so are the people behind them. Successes overlap with failures as business identities get murky and as the small business lender you’re not sure if you fully know what’s going on. But the truth is in there. You just have to be confident that you know it before you make a funding decision. Not every murky small business story is an attempt to defraud you of capital. But how do you know?

We’ve been there too.

At Rapid Finance, we’ve been working with small business owners for over 20 years. We’ve celebrated their wins, helped them through downturns, and through it all have seen more than our share of fraud.

There are tools built for fraud detection in lending. Know Your Customer (KYC) for personal lending and Know Your Business (KYB) for commercial lending. But each takes a siloed approach to analyzing data. Small businesses tend to have attributes of both. They operate in a gray zone between consumer-focused and commercial focused fraud detection systems. This hybrid space is exactly where sophisticated fraud rings tend to hide.

So we built something that would access and analyze data in a more holistic way to get a clearer picture of the small business entity applying for a loan.

A whole new way to see what other lenders miss

Over the course of pioneering small business lending for the past 20 years, Rapid Finance began unpacking the inequities of traditional fraud detection techniques in this market. And it’s been worthwhile doing so. These enterprises don’t just drive innovation and create jobs—they anchor communities and fuel resilience. Small businesses now account for 43.5% of U.S. GDP (Small Business Administration, 2023).

So we reimagined how a fraud detection system might do a better job of bubbling up the bad actors who attempt to gain the system. The result is Lynx, Not just another fraud filter, but a smarter, more connected way to understand who you’re lending to.

Lynx isn’t a dashboard. It’s not a checkbox. It’s a behind-the-scenes intelligence system that quietly goes to work the moment an application enters your ecosystem. It pulls in your own historical data, enriches it with external signals, and then overlays insights from a shared network of lenders to help you spot both opportunity and risk more clearly.

Think of it like a seasoned analyst who’s read every file, seen every trick, and still believes in giving good borrowers a chance – fast.

Here’s how it works: Unified layers of data

Layer 1: Unlocking the power of your own historical data

As a lender you likely already have a treasure trove of useful data. All the past applications, approved and denied. All the identifying names, numbers, addresses, domain IPs, contact numbers, and activity associated with each.

Lynx’s API seamlessly integrates with your database to connect all the dots across its siloes, safely and intelligently. It will quickly see any suspicious patterns of single entities operating across different identities. In seconds it finds associations it would take you days or weeks to find through manual review. But it’s not done. Lynx then moves to the next layer.

Layer 2: Grounding your data with external sources of truth

Lynx also correlates your application data against subscriptions to multiple 3^rd party sources.  It now tries to confirm the integrity of the data. It simultaneously cross references both KYC and KYB providers to fill in the gaps each of them alone won’t find. It finds any connections to flagged watchlists and sanctions databases. If any associated disposable emails, spoofed geolocations, or known proxies are out there, Lynx’s AI will find it and ensure the integrity of all data you are working with. But Lynx still isn’t done.

Layer 3: Circle the wagons in a Trust Network of other lenders

We’re all in this fight together. The ultimate power of Lynx is how it brings our whole industry together in common cause against the growing sophistication of fraud rings. It’s designed to be used by multiple lenders across the small business lending ecosystem. By incorporating anonymized data from other participants in real time, it sniffs out fraudulent patterns not seen in the silos of data trapped in the lending ecosystem.

We built Lynx so lenders like us can stop second-guessing. So underwriters can focus on the deals that deserve attention. So automation can be smarter, not just faster.

With Lynx, lenders like you can:

• Cut the noise and drastically reduce manual reviews
• Say “yes” faster to good businesses that need capital now
• Improve conversion by removing unnecessary friction
• Prevent losses by identifying risk early and with confidence
• Grow your lending footprint—without growing your fraud exposure 

Ultimately, the Lynx story isn’t about fraud. It’s about servicing the huge economic force that is small business in America. It’s about protecting the millions of small business entrepreneurs, and the lending institutions that serve them, from criminals who hide in the blindspots of our data infrastructure. Lynx enables you as a lending institution to spend your energy on the legitimate business of small business.

We welcome the opportunity to show you how it works and hear what challenges you’re facing. If you’d like to learn more about how Lynx can be totally customized around your business needs and lending rules, please reach out to enterprisesales@rapidfinance.com so we can schedule a personalized presentation. 

Let’s talk. 

Lynx goes beyond siloed data analysis to detect fraud patterns across three data layers