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Foundations of Online Payment Fraud Detection and Deep Learning Models

In: Neural Network-Based Deep Learning for Online Payment Fraud Detection

Author

Listed:
  • Yu Xie

    (Shanghai Maritime University, College of Information Engineering)

  • Yue Tian

    (Shanghai Normal University, Department of Computer Science and Technology)

  • Jiamin Yao

    (Shanghai Maritime University, College of Information Engineering)

  • Guanjun Liu

    (Tongji University, Department of Computer Science)

Abstract

In Chap. 1, we provide a high-level analysis of the evolution of online fraudulent transactions, the limitations of traditional OPFD systems, and the potential of deep learning in financial security. Building on this foundation, this chapter offers a systematic review of the foundational concepts and operational characteristics of online payment transactions, identifies the core challenges faced by OPFD, and introduces the deep learning model framework that will be used throughout the book. In addition, this chapter discusses commonly used evaluation metrics and assessment strategies in OPFD, establishing a unified methodological basis for model design, experimental comparison, and performance analysis in the subsequent chapters.

Suggested Citation

  • Yu Xie & Yue Tian & Jiamin Yao & Guanjun Liu, 2026. "Foundations of Online Payment Fraud Detection and Deep Learning Models," Springer Books, in: Neural Network-Based Deep Learning for Online Payment Fraud Detection, chapter 2, pages 13-32, Springer.
    • Handle: RePEc:spr:sprchp:978-981-95-8513-7_2
    DOI: 10.1007/978-981-95-8513-7_2
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