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Consumer Credit Assessments in the Age of Big Data

In: Big Data in Finance

Author

Listed:
  • Lynnette Purda

    (Queen’s University)

  • Cecilia Ying

    (Queen’s University)

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.

Suggested Citation

  • Lynnette Purda & Cecilia Ying, 2022. "Consumer Credit Assessments in the Age of Big Data," Springer Books, in: Thomas Walker & Frederick Davis & Tyler Schwartz (ed.), Big Data in Finance, pages 95-113, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-12240-8_6
    DOI: 10.1007/978-3-031-12240-8_6
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