IDEAS home Printed from https://ideas.repec.org/a/igg/jban00/v11y2024i1p1-23.html
   My bibliography  Save this article

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, vol. 11(1), pages 1-23, January.
    • Handle: RePEc:igg:jban00:v:11:y:2024:i:1:p:1-23
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJBAN.347504
    Download Restriction: no
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
    2. HUO, Peng & WANG, Luxin, 2022. "Digital economy and business investment efficiency: Inhibiting or facilitating?," Research in International Business and Finance, Elsevier, vol. 63(C).
    3. Darko B. Vuković & Senanu Dekpo-Adza & Stefana Matović, 2025. "AI integration in financial services: a systematic review of trends and regulatory challenges," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-29, December.
    4. Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).
    5. Li Xian Liu & Zhiyue Sun & Kunpeng Xu & Chao Chen, 2024. "AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges," IJFS, MDPI, vol. 12(3), pages 1-35, June.
    6. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Ayed Alwadain & Rao Faizan Ali & Amgad Muneer, 2023. "Estimating Financial Fraud through Transaction-Level Features and Machine Learning," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
    8. Wang, Dan & Chen, Zhi & Florescu, Ionuţ & Wen, Bingyang, 2023. "A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating," Research in International Business and Finance, Elsevier, vol. 64(C).
    9. Wang, Liang & Li, Munan, 2024. "An exploration method for technology forecasting that combines link prediction with graph embedding: A case study on blockchain," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    10. Robert Kudelić & Tamara Šmaguc & Sherry Robinson, 2025. "Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-43, December.
    11. Goodell, John W. & Oriani, Marco Ercole & Paltrinieri, Andrea & Patel, Ritesh, 2023. "The importance of ABS 2 journals in finance scholarship: Evidence from a bibliometric case study," Finance Research Letters, Elsevier, vol. 55(PA).
    12. Minghao Zhou & Wenhao Lai, 2023. "Coal gangue recognition based on spectral imaging combined with XGBoost," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-15, January.
    13. Alena Vagaská & Miroslav Gombár & Antonín Korauš, 2022. "Mathematical Modeling and Nonlinear Optimization in Determining the Minimum Risk of Legalization of Income from Criminal Activities in the Context of EU Member Countries," Mathematics, MDPI, vol. 10(24), pages 1-25, December.
    14. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    15. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    16. Pavlos I. Zitis & Stelios M. Potirakis & Alex Alexandridis, 2024. "Forecasting Forex Market Volatility Using Deep Learning Models and Complexity Measures," JRFM, MDPI, vol. 17(12), pages 1-22, December.
    17. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
    18. Yasmeen Ansari & Mansour Saleh Albarrak & Noorjahan Sherfudeen & Arfia Aman, 2022. "A Study of Financial Literacy of Investors—A Bibliometric Analysis," IJFS, MDPI, vol. 10(2), pages 1-16, May.
    19. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    20. Anica-Popa Liana-Elena & Vrîncianu Marinela & Petrică Papuc Iuliana-Mădălina, 2023. "AI – powered Business Services in the Hyperautomation Era," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 17(1), pages 1036-1050, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jban00:v:11:y:2024:i:1:p:1-23. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.