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Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management: A Systematic Literature Review

Author

Listed:
  • Jewel Kumar Roy

    (Széchenyi István University, Hungary & Jatiya Kabi Kazi Nazrul Islam University, Bangladesh)

  • László Vasa

    (Széchenyi István University, Győr, Hungary)

Abstract

The ever-changing landscape of financial technology has undergone significant changes owing to advancements in machine learning, artificial intelligence, blockchains, and digitalization. These changes have had a profound impact on the provision of financial services, specifically, credit scoring and lending. This study examines the intersection of financial technology, artificial intelligence, machine learning, blockchain, and digitalization in the context of credit services with a focus on credit scoring and lending. This study addressed three main research questions: The research followed a comprehensive methodology, considering factors such as population, intervention, comparison, outcomes, and setting to ensure that collected data aligns with research objectives. The research questions were structured using the PICOS framework, and the PRISMA model was used for the systematic review and study selection. The publications analysed covered a wide range of datasets and methodologies.

Suggested Citation

  • Jewel Kumar Roy & László Vasa, 2024. "Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management: A Systematic Literature Review," International Journal of Business Analytics (IJBAN), IGI Global Scientific Publishing, vol. 11(1), pages 1-23, January.
    • Handle: RePEc:igg:jban00:v:11:y:2024:i:1:p:1-23
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    References listed on IDEAS

    as
    1. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
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