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Differentiable Inductive Logic Programming for Fraud Detection

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  • Boris Wolfson
  • Erman Acar

Abstract

Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of Fraud Detection. This work investigates the applicability of Differentiable Inductive Logic Programming (DILP) as an explainable AI approach to Fraud Detection. Although the scalability of DILP is a well-known issue, we show that with some data curation such as cleaning and adjusting the tabular and numerical data to the expected format of background facts statements, it becomes much more applicable. While in processing it does not provide any significant advantage on rather more traditional methods such as Decision Trees, or more recent ones like Deep Symbolic Classification, it still gives comparable results. We showcase its limitations and points to improve, as well as potential use cases where it can be much more useful compared to traditional methods, such as recursive rule learning.

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  • Boris Wolfson & Erman Acar, 2024. "Differentiable Inductive Logic Programming for Fraud Detection," Papers 2410.21928, arXiv.org.
    • Handle: RePEc:arx:papers:2410.21928
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    References listed on IDEAS

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    1. Andrzej Dudek & Marcin Pełka, 2022. "Symbolic data analysis as a tool for credit fraud detection," Bank i Kredyt, Narodowy Bank Polski, vol. 53(6), pages 587-604.
    2. Petr Hajek & Mohammad Zoynul Abedin & Uthayasankar Sivarajah, 2023. "Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework," Information Systems Frontiers, Springer, vol. 25(5), pages 1985-2003, October.
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