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This paper presents an operational security platform for financial institutions, built on advanced machine learning algorithms and offered in a Fintech-as-a-Service (FaaS) model. The system integrates transactional layers with external AML, KYC, and fraud detection services, enabling real-time anomaly detection. A comparative evaluation of selected classification algorithms was conducted, employing various resampling, dimensionality reduction, and classifier ensemble strategies. Gaussian Naive Bayes demonstrated superior accuracy and efficiency in probabilistic risk estimation, making it optimal for real-time deployment. The platform’s modularity, integration layer, and hybrid classification engine offer high adaptability to evolving financial threat landscapes.

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A Prototype of an AI-Driven Operational Security System for FinTechs: A FaaS-Based Approach to Fraud Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Andrzej Dorochowicz,
  • Dariusz Jankowski,
  • Paweł Ksieniewicz,
  • Katarzyna Topolska,
  • Mariusz Topolski,
  • Paweł Zyblewski

摘要

This paper presents an operational security platform for financial institutions, built on advanced machine learning algorithms and offered in a Fintech-as-a-Service (FaaS) model. The system integrates transactional layers with external AML, KYC, and fraud detection services, enabling real-time anomaly detection. A comparative evaluation of selected classification algorithms was conducted, employing various resampling, dimensionality reduction, and classifier ensemble strategies. Gaussian Naive Bayes demonstrated superior accuracy and efficiency in probabilistic risk estimation, making it optimal for real-time deployment. The platform’s modularity, integration layer, and hybrid classification engine offer high adaptability to evolving financial threat landscapes.