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A review of credit scoring research in the age of Big Data

Author

Listed:
  • Ceylan Onay
  • Elif Öztürk

Abstract

Purpose - This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big Data bring to credit scoring. Design/methodology/approach - Content analysis methodology is used to analyze 258 peer-reviewed academic papers from 147 journals from two comprehensive academic research databases to identify their research themes and detect trends and changes in the credit scoring literature according to content characteristics. Findings - The authors find that credit scoring is going through a quantitative transformation, where data-centric underwriting approaches, usage of non-traditional data sources in credit scoring and their regulatory aspects are the up-coming avenues for further research. Practical implications - The paper’s findings highlight the perils and benefits of using Big Data in credit scoring algorithms for corporates, governments and non-profit actors who develop and use new technologies in credit scoring. Originality/value - This paper presents greater insight on how Big Data challenges traditional credit scoring models and addresses the need to develop new credit models that identify new and secure data sources and convert them to useful insights that are in compliance with regulations.

Suggested Citation

  • Ceylan Onay & Elif Öztürk, 2018. "A review of credit scoring research in the age of Big Data," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 26(3), pages 382-405, July.
    • Handle: RePEc:eme:jfrcpp:jfrc-06-2017-0054
    DOI: 10.1108/JFRC-06-2017-0054
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    Citations

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    Cited by:

    1. Sánchez, Marisa A., 2022. "A multi-level perspective on financial technology transitions," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    2. Valter T. Yoshida Jr & Alan de Genaro & Rafael Schiozer & Toni R. E. dos Santos, 2023. "A Novel Credit Model Risk Measure: does more data lead to lower model risk in credit scoring models?," Working Papers Series 582, Central Bank of Brazil, Research Department.

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