Differentiable Inductive Logic Programming for Fraud Detection
This work addresses the need for explainable AI in fraud detection, but it is incremental as it adapts an existing method with data curation without significant performance gains.
The paper tackles the problem of applying Differentiable Inductive Logic Programming (DILP) to fraud detection as an explainable AI method, showing that with data curation, it becomes more applicable and yields results comparable to traditional methods like Decision Trees and Deep Symbolic Classification.
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.