Scarlett with Money 2020, and this is how fintech companies actually make that cash where we look past buzzwords and focus on the parts of finance that quietly generate real revenue.
Today we're talking about artificial labs, and this is a perfect example of a fintech succeeding by modernizing an industry from the inside, not trying to blow it up.
Commercial and specialty insurance is enormous.
It's global.
It's profitable and operationally it still relies on a shocking amount of manual process.
Artificial labs builds digital brokering and underwriting platforms for complex commercial insurance, replacing spreadsheets, emails, and. workflows with software designed for real underwriting decisions.
So how does artificial labs actually make money?
First, enterprise contracts.
Large brokers, carriers, and MGAs pay recurring multi-year contracts to use artificial labs as their underwriting and placement platform.
This software sits directly in the risk selection workflow, which means it's treated as a core infrastructure, not as a nice to have tool.
Second, long term institutional deployments.
Once implemented, artificial labs is rolled out across teams, product lines, and geographies that not only expands its revenue over time, but it also creates durability.
Third, deep workflow.
Artificial labs doesn't sit alongside underwriting decisions.
It is where the underwriting happens.
That level of entrenchment creates real switching costs in a heavily regulated environment.
Here's the thing about commercial insurance it's conservative by design.
Systems change slowly, but when they do change, it's usually because the old way has become too inefficient to sustain the margins.
So that, my friends, is exactly why Artificial labs is gaining momentum right now.
Underwriting margins are under pressure.
Manual processes create inconsistency and regulators expect transparency and audibility that let's be honest, spreadsheets just simply can't deliver.
That backdrop showed up clearly in the company's funding in February 26.
Artificial labs raised $45 million Series.
And they did this all about scaling globally and expanding into the US market.
This wasn't investors chasing hype.
It was a bet on slow inevitable modernization.
So what is the mode exactly?
Regulatory complexity combined with workflow depth.
Underwriting platforms must satisfy internal risk teams, executives, auditors, and regulators all at once.
Once the system does that reliably, replacing it becomes risky and expensive.
Artificial labs isn't trying to disrupt insurance, it's trying to run it better.
And that, my friends, is how artificial labs actually makes that money by becoming the system insurers rely on to price risk consistently, compliantly, and profitably year after year.
Not flashy, not consumer facing, but exactly the kind of business that compounds quietly.
Have the fintech that you want us to feature on the show, drop it in the comments and it might just make the cut.
In the meantime, back over to you.
