Leap Analytics Inc., a fintech real estate investment company based in Los Angeles, has unveiled a credit-scoring model designed to incorporate machine learning with a more rigorous data analysis than traditional models.
According to the company, the new credit scoring model will better predict credit risk while creating more equitable and accurate scores, particularly where homeownership is concerned. Leap’s credit-scoring solution analyzes hundreds of consumer data points, including real-time, consumer-approved data, along with past payment history from multiple sources, to create a more comprehensive assessment of a homeowner’s ability to repay debt.
“Traditional credit scoring models were developed decades ago to assess historical patterns in a homeowner’s ability to pay,” said Ashley Bete, Leap’s founder and CEO. “That’s a backward-looking analysis that can put some borrowers at a disadvantage. Our new model promotes financial inclusion by better predicting consumer behavior and identifying those who responsibly manage credit but are overlooked due to the limitations of traditional scoring models, such as FICO.”
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