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Safeguarding FinTech innovations with machine learning: Comparative assessment of various approaches

Author

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  • Mirza, Nawazish
  • Elhoseny, Mohamed
  • Umar, Muhammad
  • Metawa, Noura

Abstract

Protecting data is paramount to the development of FinTech. Fraudulent activities can exploit weaknesses in FinTech systems, wreaking havoc on both customers and service providers. However, machine-learning approaches have the potential to spot irregularities in FinTech systems, looking for red flags in economic data sets and using such red flags to inform predictive models for the detection of future fraud. We assess anomaly detection techniques, thereby adding to this crucial topic. We apply a variety of techniques to multiple synthetic and real-world databases. Findings corroborate that machine-learning approaches help with fraud detection, although with varying degrees of effectiveness. Our findings demonstrate that competitive advantage is the most crucial component amongst some Fintech-based predictors, while sales volume is diagnosed as having the least effective importance. To ensure the consistency and accuracy of our findings, we choose case studies for evaluating ML-based fraudulent activities based on the availability of properly allowed appropriate.

Suggested Citation

  • Mirza, Nawazish & Elhoseny, Mohamed & Umar, Muhammad & Metawa, Noura, 2023. "Safeguarding FinTech innovations with machine learning: Comparative assessment of various approaches," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001356
    DOI: 10.1016/j.ribaf.2023.102009
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    References listed on IDEAS

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    1. Mark A Chen & Qinxi Wu & Baozhong Yang, 2019. "How Valuable Is FinTech Innovation?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2062-2106.
    2. Senyo, PK & Osabutey, Ellis L.C., 2020. "Unearthing antecedents to financial inclusion through FinTech innovations," Technovation, Elsevier, vol. 98(C).
    3. Itay Goldstein & Wei Jiang & G Andrew Karolyi, 2019. "To FinTech and Beyond," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1647-1661.
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    Cited by:

    1. Deng, Ying & Cao, Zhitao & Yang, Na, 2024. "Understanding the nexus between fintech, natural resources, green investment, and environmental sustainability in China: A DYNARDL approach," Resources Policy, Elsevier, vol. 91(C).
    2. Hossain, Mohammad Razib & Rao, Amar & Sharma, Gagan Deep & Dev, Dhairya & Kharbanda, Aeshna, 2024. "Empowering energy transition: Green innovation, digital finance, and the path to sustainable prosperity through green finance initiatives," Energy Economics, Elsevier, vol. 136(C).
    3. Pak-Lok Poon & Santoso Wibowo & Sau-Fun Tang, 2024. "A FinTech Clustering Framework: Technology, Model, and Stakeholder Perspectives," Papers 2412.05285, arXiv.org.

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