Hi Hunters, Jamie here, co-founder of Heron Data - we help SMB lenders to score their applicants using bank transaction data. We’ve been working on making it easy for SMB lenders to parse and evaluate bank statements of loan applicants but the big question all of our customers asked was ‘so what’? Revenue might be $X, but what impact does that have on default? So we built the Heron Score. It is an objective way to evaluate the cashflow strength of companies in the SMB space. The model is based on short-term unsecured loans in the US and connects bank data and loan outcomes to stack rank businesses according to their likelihood of repaying a loan with a score between 1 and 1000. How does this help SMB lenders? 🔮 Improve default rates: Data is up-to-date (pulled from most recent bank statements / Plaid) and the score is highly predictive of default risk, particularly for young businesses that might not have a long repayment history 🔄 Full coverage: we can pull any applicant you have (no gaps as you might get from credit agency) ⏳ Reduce time-per-file: Heron score allows underwriters to get an instant snapshot of a company’s default risk to aid in quick decisioning If you’d like to learn more, or take us for a test drive, we are happy to do a no-strings-attached POC, where we can take your historical loan repayment data + some applications, and show you what the delta would be if you used our score in production. Feel free to reach out to me directly [jamie@herondata.io] or book a demo via our website.

Great to see Heron going from strength to strength 💪

I'm intrigued by the Heron Score's apparent accuracy and performance, and I look forward to learning more about its effectiveness in predicting SMB credit risk.