Using artificial intelligence, BetaScore predicts chargeoff risk with over 98% accuracy and offers universal credit score coverage by leveraging both traditional and non-traditional data sources.
Three quarters of small businesses in the US lack credit scores, making them invisible to lenders. Legacy bureaus use data sources like UCC filings and trade disputes, but only a small subset of small businesses actually generate this data. It's also an incomplete picture of financial health. We can generate a BetaScore for any small business using data they actually have.
We are moving away from a siloed way of looking at credit scoring data and expanding the aperture to include new and unique data sources that are proven to correlate to a business’ financial health profile.
Our proprietary credit scoring model uses machine learning that is trained on millions of actual loan records and is able to predict charge off risk with 98% accuracy and provide explainability down to the 10th decimal point.
Small businesses have messy and inconsistent data (financial statements, business plans, tax transcripts) that require hours of manual work to gather and parse. We use Gemini to make sense of these documents and to organize the relevant data into a usable format. This data is ultimately incorporated into the training model to predict the risk.