Can Big Data Defeat Traditional Credit Rating?
59 Pages Posted: 11 Jan 2019 Last revised: 7 Nov 2019
Date Written: September 25, 2019
This paper examines the impact of large-scale alternative data, or big data, on predicting consumer loan delinquency for a traditional lender. Based on a unique proprietary dataset containing 700 million individuals and 20,000 variables, we construct a big data credit score by applying machine learning techniques to deal with high dimensionality and massive missing values. We find that incorporating the big data credit score improves the lender’s accuracy in predicting a borrower’s delinquency likelihood by 22.6%. We identify two possible ways through which big data contributes: providing more information for those without public credit records and correcting financial misreporting.
Keywords: Big Data, FinTech, Personal Credit, Alternative Data, Income Exaggeration
JEL Classification: G10, G21, G23
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