How To Calculate Company Revenue With Plaid
We started Heron Data because we learned while building our own lending FinTech that while less than 10% of companies have high quality, up-to-date accounting data, every single business has up-to-date bank data. Bank transaction data is also the single source of truth about what happens in a company. It's great for joining other datasets to (like Commerce data) and even for verifying accounting data because it can't be easily doctored.
What's more, it's easy to pull bank data via API with Plaid. But how do you go from the raw transactions to an accurate revenue number that you can use to assess an applicant's financial health?
You could start by labelling a dataset of tens of millions of transactions, differentiating between revenue and non-revenue deposits, then training ML models to categorise in real time with a high degree of accuracy and coverage. You would probably also want to ship a dashboard to recategorise transactions you might have got wrong, and then continue to add training data and retrain your ML to avoid model drift and keep up with new vendors. Maybe you're interested in the platform that a company uses to generate revenue and so you create a database of payment processors, commerce platforms, and the like and write logic to add that tag to revenue transactions.
You could do all that, or you could use Heron Data. Book a call to hear more.