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.

翻译：当前机器学习的发展趋势倾向于可解释性，即使这需要以性能为代价。因此，在欺诈检测领域，可解释的人工智能方法尤为重要。本研究探讨了可微归纳逻辑程序设计作为一种可解释的人工智能方法在欺诈检测中的适用性。尽管DILP的可扩展性是一个众所周知的问题，但我们表明，通过一些数据整理工作（如清洗数据、将表格和数值数据调整至背景事实陈述的预期格式），其适用性将显著提高。在处理过程中，与决策树等传统方法或深度符号分类等较新方法相比，DILP虽未展现出显著优势，但仍能提供可比的结果。我们展示了其局限性及改进方向，并指出了相较于传统方法（如递归规则学习）更具应用潜力的场景。