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Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance

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  • Königstorfer, Florian
  • Thalmann, Stefan

Abstract

Artificial intelligence (AI) is receiving increasing attention in business and society. In banking, the first applications of AI were successful; however, AI is mainly applied in investment banking and backend services without customer contact. AI in commercial banking with its focus on customer interaction has received little attention so far. Introducing AI in commercial banking could change business processes and interactions with customers, which could create research opportunities for behavioral finance. Based on this research gap, we conducted a structured literature review to identify applications of AI in commercial banks and the challenges of implementing AI. Our findings suggest that by using AI, commercial banks can reduce losses in lending, increase security in processing payments, automate compliance-related work, and improve customer targeting. Researchers worry about realizing technological advantages; the embedding of AI in business processes; ensuring user acceptance through transparency; privacy; and suitable documentation. Finally, we propose a research agenda for behavioral finance.

Suggested Citation

  • Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
  • Handle: RePEc:eee:beexfi:v:27:y:2020:i:c:s2214635019302503
    DOI: 10.1016/j.jbef.2020.100352
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    as
    1. Brüggen, Elisabeth C. & Hogreve, Jens & Holmlund, Maria & Kabadayi, Sertan & Löfgren, Martin, 2017. "Financial well-being: A conceptualization and research agenda," Journal of Business Research, Elsevier, vol. 79(C), pages 228-237.
    2. 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.
    3. Ning Du & David V. Budescu, 2018. "How (Over) Confident Are Financial Analysts?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(3), pages 308-318, July.
    4. Priyank Gandhi & Tim Loughran & Bill McDonald, 2019. "Using Annual Report Sentiment as a Proxy for Financial Distress in U.S. Banks," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(4), pages 424-436, October.
    5. 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.
    6. Bhatia, Ankita & Chandani, Arti & Chhateja, Jagriti, 2020. "Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    7. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
    8. Ellen Tobback & David Martens, 2019. "Retail credit scoring using fine‐grained payment data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1227-1246, October.
    9. Thanh Nguyen & Liem T. Nguyen & Anh Duc Ngo & Hari Adhikari, 2018. "CEO Optimism and the Credibility of Open-Market Stock Repurchase Announcements," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(1), pages 49-61, January.
    10. Yan Zhang & Peter Trubey, 2019. "Machine Learning and Sampling Scheme: An Empirical Study of Money Laundering Detection," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 1043-1063, October.
    11. Bücker, Michael & van Kampen, Maarten & Krämer, Walter, 2013. "Reject inference in consumer credit scoring with nonignorable missing data," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1040-1045.
    12. Vladimir Anic & Martin Wallmeier, 2020. "Perceived Attractiveness of Structured Financial Products: The Role of Presentation Format and Reference Instruments," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(1), pages 78-102, January.
    13. Zheng, Changjun & Ashraf, Badar Nadeem, 2014. "National culture and dividend policy: International evidence from banking," Journal of Behavioral and Experimental Finance, Elsevier, vol. 3(C), pages 22-40.
    14. Janina Harasim, 2016. "Europe: The Shift from Cash to Non-Cash Transactions," Palgrave Macmillan Studies in Banking and Financial Institutions, in: Jakub Górka (ed.), Transforming Payment Systems in Europe, chapter 2, pages 28-69, Palgrave Macmillan.
    15. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    16. 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.
    17. Brenner, Lukas & Meyll, Tobias, 2020. "Robo-advisors: A substitute for human financial advice?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    18. Malte Krueger & Kay Leibold, 2008. "Internet Payments in Germany," International Handbooks on Information Systems, in: Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), Handbook on Information Technology in Finance, chapter 11, pages 239-256, Springer.
    19. Gaurav Kumar & Cal B. Muckley & Linh Pham & Darragh Ryan, 2019. "Can alert models for fraud protect the elderly clients of a financial institution?," The European Journal of Finance, Taylor & Francis Journals, vol. 25(17), pages 1683-1707, November.
    Full references (including those not matched with items on IDEAS)

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    2. Rodrigues, Ana Rita D. & Ferreira, Fernando A.F. & Teixeira, Fernando J.C.S.N. & Zopounidis, Constantin, 2022. "Artificial intelligence, digital transformation and cybersecurity in the banking sector: A multi-stakeholder cognition-driven framework," Research in International Business and Finance, Elsevier, vol. 60(C).
    3. Wongsansukcharoen, Jedsada, 2022. "Effect of community relationship management, relationship marketing orientation, customer engagement, and brand trust on brand loyalty: The case of a commercial bank in Thailand," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
    4. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    5. Cristian-Mihai Vidu & Florina Pinzaru & Andreea Mitan, 2022. "What managers of SMEs in the CEE region should know about challenges of artificial intelligence’s adoption? – an introductive discussion," Nowoczesne Systemy Zarządzania. Modern Management Systems, Military University of Technology, Faculty of Security, Logistics and Management, Institute of Organization and Management, issue 1, pages 63-76.
    6. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    7. Kumar, Satish & Rao, Sandeep & Goyal, Kirti & Goyal, Nisha, 2022. "Journal of Behavioral and Experimental Finance: A bibliometric overview," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    8. Leone, Daniele & Schiavone, Francesco & Appio, Francesco Paolo & Chiao, Benjamin, 2021. "How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem," Journal of Business Research, Elsevier, vol. 129(C), pages 849-859.
    9. Daragmeh, Ahmad & Lentner, Csaba & Sági, Judit, 2021. "FinTech payments in the era of COVID-19: Factors influencing behavioral intentions of “Generation X” in Hungary to use mobile payment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).

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    More about this item

    Keywords

    Behavioral finance; Financial service; Commercial banks;
    All these keywords.

    JEL classification:

    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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