Integrating Fuzzy Logic into Deep Symbolic Regression
This work addresses explainability in fraud detection for financial institutions, but it is incremental as it builds on existing methods with a focus on specific implications.
This paper tackled the problem of credit card fraud detection by integrating fuzzy logic into Deep Symbolic Regression to improve performance and explainability, finding that the Łukasiewicz implication achieved the highest F1-score and accuracy.
Credit card fraud detection is a critical concern for financial institutions, intensified by the rise of contactless payment technologies. While deep learning models offer high accuracy, their lack of explainability poses significant challenges in financial settings. This paper explores the integration of fuzzy logic into Deep Symbolic Regression (DSR) to enhance both performance and explainability in fraud detection. We investigate the effectiveness of different fuzzy logic implications, specifically Łukasiewicz, Gödel, and Product, in handling the complexity and uncertainty of fraud detection datasets. Our analysis suggest that the Łukasiewicz implication achieves the highest F1-score and overall accuracy, while the Product implication offers a favorable balance between performance and explainability. Despite having a performance lower than state-of-the-art (SOTA) models due to information loss in data transformation, our approach provides novelty and insights into into integrating fuzzy logic into DSR for fraud detection, providing a comprehensive comparison between different implications and methods.