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.

This work was supported in part by Fundação para a Ciência e a Tecnologia, I. P. (FCT) under Grant UIDB/03126/2020, Grant UIDB/04466/2020, Grant UIDP/04466/2020; and in part by POAT-01-6177-FEDER-000059.

AI,Artificial intelligence,Financial applications,Explainable machine learning,Systematic literature review,XAI

• Computer and Information Sciences - Natural Sciences
• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology

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