Explainable Artificial Intelligence (XAI): A Systematic Literature Review on Taxonomies and Applications in Finance

• Published in: IEEE access Vol. 12; pp. 618 - 629
• Main Authors: Martins, Tiago, de Almeida, Ana Maria, Cardoso, Elsa, Nunes, Luis
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Bibliographies; Explainable artificial intelligence; explainable machine learning; Finance; financial applications; Financial management; Literature reviews; Machine learning; Predictive models; Surveys; systematic literature review; Systematics; Taxonomy; XAI
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3347028

Explainable Artificial Intelligence (XAI) is a growing area of research that aims to improve the interpretability of the not-so-informative black-box models. However, it is currently difficult to categorize an existing method in terms of its intrinsic characteristics and explainability. We provide a new unified yet simple taxonomy for the categorization of XAI methods and present the explainability methods currently being applied in finance applications. For both purposes, we present two separate systematic literature reviews: an anthological search for surveys on XAI methods in order to present a unified taxonomy, followed by an exposition of the XAI methods currently in use that have been found. We also concisely define the existing explainability methods using the proposed categories based on the ones most commonly addressed in the reviewed literature and pinpoint specific XAI methods being used in practical applications in Finance.