IDEAS home Printed from https://ideas.repec.org/a/spr/manrev/v74y2024i2d10.1007_s11301-023-00320-0.html
   My bibliography  Save this article

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

Author

Listed:
  • Patrick Weber

    (Goethe University Frankfurt)

  • K. Valerie Carl

    (Goethe University Frankfurt)

  • Oliver Hinz

    (Goethe University Frankfurt)

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.

Suggested Citation

  • Patrick Weber & K. Valerie Carl & Oliver Hinz, 2024. "Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature," Management Review Quarterly, Springer, vol. 74(2), pages 867-907, June.
    • Handle: RePEc:spr:manrev:v:74:y:2024:i:2:d:10.1007_s11301-023-00320-0
    DOI: 10.1007/s11301-023-00320-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11301-023-00320-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11301-023-00320-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Snyder, Hannah, 2019. "Literature review as a research methodology: An overview and guidelines," Journal of Business Research, Elsevier, vol. 104(C), pages 333-339.
    2. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    3. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    4. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    5. Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
    6. Daniel Berg, 2007. "Bankruptcy prediction by generalized additive models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(2), pages 129-143, March.
    7. Ricarda Bouncken & Johanna Gast & Sascha Kraus & Marcel Bogers, 2015. "Coopetition: a systematic review, synthesis, and future research directions," Post-Print hal-02018068, HAL.
    8. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    9. Ricarda Bouncken & Johanna Gast & Sascha Kraus & Marcel Bogers, 2015. "Coopetition: a systematic review, synthesis, and future research directions," Post-Print hal-02945341, HAL.
    10. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    11. Benjamin M. Abdel-Karim & Nicolas Pfeuffer & Oliver Hinz, 2021. "Machine learning in information systems - a bibliographic review and open research issues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 643-670, September.
    12. Dominik Aaken & Florian Buchner, 2020. "Religion and CSR: a systematic literature review," Journal of Business Economics, Springer, vol. 90(5), pages 917-945, June.
    13. Nian, Ke & Coleman, Thomas F & Li, Yuying, 2021. "Learning sequential option hedging models from market data," Journal of Banking & Finance, Elsevier, vol. 133(C).
    14. Burgt, Joost Van Der, 2020. "Explainable AI in banking," Journal of Digital Banking, Henry Stewart Publications, vol. 4(4), pages 344-350, March.
    15. Wu, Qi & Yan, Xing, 2019. "Capturing deep tail risk via sequential learning of quantile dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    16. George A. Akerlof, 1970. "The Market for "Lemons": Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(3), pages 488-500.
    17. Henner Gimpel & Daniel Rau & Maximilian Röglinger, 2018. "Understanding FinTech start-ups – a taxonomy of consumer-oriented service offerings," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 245-264, August.
    18. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    19. Butaru, Florentin & Chen, Qingqing & Clark, Brian & Das, Sanmay & Lo, Andrew W. & Siddique, Akhtar, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 218-239.
    20. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    21. Rainer Alt & Roman Beck & Martin T. Smits, 2018. "FinTech and the transformation of the financial industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 235-243, August.
    22. Bekiros, Stelios D., 2010. "Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1153-1170, June.
    23. Martina Sageder & Christine Mitter & Birgit Feldbauer‐Durstmüller, 2018. "Image and reputation of family firms: a systematic literature review of the state of research," Review of Managerial Science, Springer, vol. 12(1), pages 335-377, January.
    24. Sigrist, Fabio & Hirnschall, Christoph, 2019. "Grabit: Gradient tree-boosted Tobit models for default prediction," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 177-192.
    25. Collins, Christopher & Dennehy, Denis & Conboy, Kieran & Mikalef, Patrick, 2021. "Artificial intelligence in information systems research: A systematic literature review and research agenda," International Journal of Information Management, Elsevier, vol. 60(C).
    26. Schnaubelt, Matthias & Fischer, Thomas G. & Krauss, Christopher, 2020. "Separating the signal from the noise – Financial machine learning for Twitter," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    2. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    3. Kristina Stoiber & Kurt Matzler & Julia Hautz, 2023. "Ambidextrous structures paving the way for disruptive business models: a conceptual framework," Review of Managerial Science, Springer, vol. 17(4), pages 1439-1485, May.
    4. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    5. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Omar Carrasco-Carvajal & Mauricio Castillo-Vergara & Domingo García-Pérez-de-Lema, 2023. "Measuring open innovation in SMEs: an overview of current research," Review of Managerial Science, Springer, vol. 17(2), pages 397-442, February.
    7. Durongkadej, Isarin & Hu, Wenyao & Wang, Heng Emily, 2024. "How artificial intelligence incidents affect banks and financial services firms? A study of five firms," Finance Research Letters, Elsevier, vol. 70(C).
    8. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    9. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
    10. García-Céspedes, Rubén & Moreno, Manuel, 2022. "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
    11. Martin R.W. Hiebl & Zhen Li, 2020. "Non-family managers in family firms: review, integrative framework and future research agenda," Review of Managerial Science, Springer, vol. 14(4), pages 763-807, August.
    12. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).
    13. Patrick Gregori & Zulaicha Parastuty, 2021. "Investigating the process of entrepreneurial team member exits: a systematic review and future research directions," Review of Managerial Science, Springer, vol. 15(4), pages 847-878, May.
    14. Corbo, Leonardo & Kraus, Sascha & Vlačić, Božidar & Dabić, Marina & Caputo, Andrea & Pellegrini, Massimiliano M., 2023. "Coopetition and innovation: A review and research agenda," Technovation, Elsevier, vol. 122(C).
    15. Alicia Mas-Tur & Sascha Kraus & Mario Brandtner & Ralf Ewert & Wolfgang Kürsten, 2020. "Advances in management research: a bibliometric overview of the Review of Managerial Science," Review of Managerial Science, Springer, vol. 14(5), pages 933-958, October.
    16. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
    17. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    18. Ana Joana Fernandes & Joao J. Ferreira, 2022. "Entrepreneurial ecosystems and networks: a literature review and research agenda," Review of Managerial Science, Springer, vol. 16(1), pages 189-247, January.
    19. Jan Svanberg & Tohid Ardeshiri & Isak Samsten & Peter Öhman & Presha E. Neidermeyer & Tarek Rana & Natalia Semenova & Mats Danielson, 2022. "Corporate governance performance ratings with machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(1), pages 50-68, January.
    20. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.

    More about this item

    Keywords

    Explainable artificial intelligence; Finance; Systematic literature review; Machine learning; Review;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • L50 - Industrial Organization - - Regulation and Industrial Policy - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:manrev:v:74:y:2024:i:2:d:10.1007_s11301-023-00320-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.