Building a fairer credit score
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Why we need it
Credit scores omit critical data points (ie: rental and cell phone payments) that are the most useful to underrepresented communities
Tens of millions of Europeans and Americans are “credit invisible”, meaning that you have no credit history with any of the nationwide credit reporting agencies.
You receive a poor credit score if you have a limited credit history, which disproportionately affects young people and immigrants.
How it works
We derive credit scores by applying machine learning to a blend of on-chain and off-chain data
Machine learning better captures non-linear relationships which are common to credit risk
By combining transactional data with lending outcomes we generate highly accurate predictors of risk
Machine learning can develop new ways to assess the creditworthiness for unbanked adults using alternative data