Consumer Credit Assessments in the Age of Big Data

Lynnette Purda () and Cecilia Ying ()
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Lynnette Purda: Queen’s University
Cecilia Ying: Queen’s University

A chapter in Big Data in Finance, 2022, pp 95-113 from Springer

Abstract: Abstract The credit assessment process has traditionally been based on a relatively stable set of financial indicators such as overall indebtedness, cash flow stability, and borrower history. While this approach ensures that borrowers with established credit records have ongoing access to funds, it can lead to the exclusion of those with thin credit files or atypical histories. The disruption in financial services is starting to change this scenario by drawing on alternative sources of data and innovative computational techniques to develop new methods of consumer credit assessments. This chapter explores the motivations behind these changes, discusses the significant evolution in data and analytics that enable the development of new credit quality indicators, and highlights the privacy and ethical challenges of using new and emerging credit assessments in the age of big data.

Keywords: Credit score; FinTech; Peer-to-peer lending; Machine learning; Financial inclusion (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-12240-8_6

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DOI: 10.1007/978-3-031-12240-8_6

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