Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature

Patrick Weber (), K. Valerie Carl () and Oliver Hinz ()
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Patrick Weber: Goethe University Frankfurt
K. Valerie Carl: Goethe University Frankfurt
Oliver Hinz: Goethe University Frankfurt

Management Review Quarterly, 2024, vol. 74, issue 2, No 10, 867-907

Abstract: Abstract Digitalization and technologization affect numerous domains, promising advantages but also entailing risks. Hence, when decision-makers in highly-regulated domains like Finance implement these technological advances—especially Artificial Intelligence—regulators prescribe high levels of transparency, assuring the traceability of decisions for third parties. Explainable Artificial Intelligence (XAI) is of tremendous importance in this context. We provide an overview of current research on XAI in Finance with a systematic literature review screening 2,022 articles from leading Finance, Information Systems, and Computer Science outlets. We identify a set of 60 relevant articles, classify them according to the used XAI methods and goals that they aim to achieve, and provide an overview of XAI methods used in different Finance areas. Areas like risk management, portfolio optimization, and applications around the stock market are well-researched, while anti-money laundering is understudied. Researchers implement both transparent models and post-hoc explainability, while they recently favored the latter.

Keywords: Explainable artificial intelligence; Finance; Systematic literature review; Machine learning; Review (search for similar items in EconPapers)
JEL-codes: G00 L50 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11301-023-00320-0

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