I’ve seen a lot in Silicon Valley. I’ve built and sold two companies, and I’ve invested in over 200 startups, including some of the biggest names in AI like Anthropic and OpenAI. I’ve seen brilliant ideas, and I’ve seen some that were… less than brilliant. But the one thing that never ceases to amaze me is the ingenuity of criminals.
Back in the early days of RemoteTeam, we were so focused on building the product that we almost missed a sophisticated fraud attempt. It wasn’t your typical stolen credit card. This was something new. Something… synthetic. It was a ghost in the machine, an identity that didn’t exist in the real world but was real enough to open accounts and try to siphon off funds. We caught it, but it was a wake-up call. The game had changed.
The Rise of the Synthetic Identity
So what is this boogeyman of the financial world? Synthetic identity fraud is when a criminal combines real and fake information to create a brand-new, fake identity. They might use a real Social Security number that belongs to a child or a deceased person, combine it with a fake name and address, and then use that to apply for credit. It’s not like traditional identity theft where they steal your entire identity. It’s more insidious. They’re creating a new person from scratch.
And it's a huge problem. We're talking about billions of dollars in losses for banks every year. Some estimates put it as high as $20 billion. It’s the fastest-growing financial crime in the world, and it’s only getting worse with the rise of AI.
The AI Arms Race
Ironically, the same AI technologies that are being used to create more sophisticated synthetic identities are also our best hope for fighting back. It’s an arms race, and right now, the banks are playing catch-up. But they’re learning fast.
I’ve seen some incredible AI-powered fraud detection systems in action. One of the most promising approaches is using machine learning to analyze vast amounts of data and identify patterns that a human would never be able to see. These models can look at thousands of data points for each new account application and flag the ones that look suspicious.
For example, an AI might notice that an applicant’s address has only ever been associated with a PO box, or that their phone number is a VoIP number that was created just a few days ago. These are small red flags on their own, but when you combine them with other data points, they can paint a pretty clear picture of a synthetic identity.
The Bizarre and Brilliant Ways AI is Fighting Back
But it’s not just about flagging suspicious applications. AI is also being used in some truly bizarre and brilliant ways to distinguish between real and synthetic identities.
One of my portfolio companies is working on a system that analyzes the way a person types. It can tell the difference between a real person and a bot based on the rhythm and cadence of their keystrokes. It’s like a digital fingerprint, and it’s incredibly difficult to fake.
Another company is using AI to analyze a person’s social media presence. They can look at the age of the account, the number of connections, and the level of engagement to determine if it’s a real person or a synthetic identity. It’s a bit creepy, I’ll admit, but it’s also incredibly effective.
The Future is a Double-Edged Sword
Look, I’m a huge believer in the power of AI. I’ve invested my own money in it, and I’ve seen firsthand how it can change the world for the better. But I’m also a realist. I know that for every new technology, there’s a criminal out there trying to figure out how to exploit it.
The fight against synthetic identity fraud is far from over. The criminals will continue to get more sophisticated, and we’ll have to keep innovating to stay ahead of them. But I’m optimistic. With the right tools and the right people, we can win this fight. We have to.
Frequently Asked Questions
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.