Examining The Use Of Alternative Data In Underwriting And Credit Scoring To Expand Access To Credit
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Examining the Use of Alternative Data in Underwriting and Credit Scoring to Expand Access to Credit
Author | : Kristin N. Johnson |
Publisher | : |
Total Pages | : 17 |
Release | : 2020 |
Genre | : |
ISBN | : |
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Over the last several years, a body of sophisticated algorithms commonly described as artificial intelligence (“AI”) and distributed ledger technologies have altered the financial market ecosystem, creating a new class of financial institutions - Fintech firms. Supplementing traditional credit underwriting data inputs and processes, fintech firms employ newer modeling techniques and consider a broader range of source data referred to descriptively (rather than normatively) as alternative data. These new inputs include information regarding consumers' financial transactions, recurring payments history and a behavioral score based on social networking and digital-interface. Fintech firms include both the non-depository digital platforms that operate independently and platforms that partner with legacy banks to originate loans. Fintech firms servicing credit scoring and underwriting markets offer great promise but also present unique concerns.
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