How AI Is Changing How Companies Get Funded
Credit underwriting is one of the most nuanced disciplines in finance. It requires judgment, experience, pattern recognition, and an understanding of risk that no algorithm fully replaces. That hasn’t changed with the proliferation of AI tooling, nor should it. What has changed, however, is the infrastructure which enables greater scale, transparency, and efficiency in credit underwriting without compromising critical human judgement.
The challenge in private credit has never been a shortage of capital or a shortage of good companies. It’s been a matching problem. There are hundreds of lending products across dozens of structures — venture debt, revenue-based financing, asset-based lending, equipment financing, structured credit, lines of credit, and more. Each has its own investment criteria, risk appetite, and ideal borrower profile. And on the other side, there are thousands of companies that could benefit from non-dilutive capital, but have no efficient way to determine which product fits, which lender is right, or what terms are realistic.
That’s where AI is making a real difference. Not by replacing the judgment that makes underwriting work, but by making the process of getting to the right conversation faster, more transparent, and intuitive than it has ever been.
Better matching at scale.
Historically, matching a company to the right capital product has been a manual, relationship-driven process. A founder talks to an advisor, who talks to a few lenders, who each evaluate the deal through their own lens. It works — but it’s slow, opaque, and limited by whoever happens to be in the room.
AI changes the scale of that matching. When you can ingest a company’s financial data — revenue streams, cash flow patterns, customer concentration, contract backlog, balance sheet composition — and compare it against hundreds of lending criteria simultaneously, you can identify the right fit in minutes instead of weeks.
Depth of data that surfaces what matters.
One of the most valuable things AI does in the lending process is surface the information that helps humans make better and more efficient investment decisions.
When a company connects its financial data to an AI-powered platform like Hum, the system can identify patterns, trends, and potential flags that would take an analyst days to find manually. Revenue concentration risk. Seasonal cash flow patterns. Margin trends that signal strength or stress. Contract renewal timing. Working capital cycles.
None of this replaces the underwriter’s judgment about what those patterns mean. But it gives them a richer, more complete picture faster.
For borrowers, this results in greater transparency into how their business is being evaluated. That transparency matters. One of the biggest frustrations founders have with the traditional lending process is the black box: you submit your financials, wait weeks, and get a yes or no with limited explanation. AI tooling can make that process legible — showing borrowers what lenders are looking at, what’s working in their favor, and what they need to address.
A faster, more intuitive process.
Speed in private credit determines outcomes. AI accelerates the fundraising process not by rushing diligence, but by eliminating the dead time. The weeks spent gathering documents that could be pulled automatically. The back-and-forth over data formats. The redundant questions across multiple lender conversations. When the data infrastructure handles the routine, borrowers and lenders can focus on the questions that actually determine whether a deal is the right fit.
At Hum, this is the infrastructure we’ve been building from day one. Our platform connects directly to a company’s financial data, builds a comprehensive profile, and uses that profile to match them with the right capital product, whether it’s through our own balance sheet or a partner in our network. The underwriting still requires judgment. The diligence still requires rigor. But the path to getting there is fundamentally more efficient.
→ Ready to see what you match with? Speak to Hum.
