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The Use of Artificial Intelligence and Expert Systems in Finance: A Bibliometric Analysis

Year 2023, , 2110 - 2127, 18.09.2023
https://doi.org/10.25295/fsecon.1269889

Abstract

Developments in artificial intelligence technology have also had an impact on various sectors. One of the sectors where artificial intelligence technology is most widely used is finance. This fact arouses the interest of researchers, and the literature on applications of artificial intelligence in finance continues to grow. Therefore, the aim of this study is to examine the evolving literature on artificial intelligence and expert systems in finance. The bibliometric analysis approach was used to evaluate 452 articles published in the Scopus database between 1988-2022. Analyzes by country, university, journal, and author were performed using the R-based bibliometrix program. As a result of the study, it was found that although the number of articles has increased over the years, the largest increase occurred in recent years. The most productive and impactful journal is “Expert Systems with Applications”, and the most impactful author is Doumpos (2001). However, the institution and country with the highest number of publications are “Hunan University of Finance and Economics” and China, respectively. Moreover, China is the country with the most interactions. On the other hand, it was found that the most frequent keyword in the studied papers is artificial intelligence and that this concept has a strong connection with the concepts of finance and machine learning. The concept of expert systems ranks sixth in terms of the number of uses. The results of this study provide an overview of the literature on artificial intelligence and expert systems in finance.

References

  • Ahmed, S., Alshater, M. M., El Ammari, A. & Hammami, H. (2022). Artificial Intelligence and Machine Learning in Finance: A Bibliometric Review. Research in International Business and Finance, 61, 101646.
  • Aria, M. & Cuccurullo, C. (2017). Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. Journal of Informetrics, 11(4), 959-975.
  • Bahrammirzaee, A. (2010). A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Computing and Applications, 19(8), 1165-1195.
  • Boukherouaa, E. B., Shabsigh, M. G., AlAjmi, K., Deodoro, J., Farias, A., Iskender, E. S., Mirestean, A. T. & Ravikumar, R. (2021). Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance. International Monetary Fund.
  • Canbas, S., Cabuk, A. & Kilic, S. B. (2005). Prediction of Commercial Bank Failure Via Multivariate Statistical Analysis of Financial Structures: The Turkish Case. European Journal of Operational Research, 166(2), 528-546.
  • Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P. & Oliveira, A. L. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194-211.
  • Chan, L., Hogaboam, L. & Cao, R. (2022). Applied Artificial Intelligence in Business: Concepts and Cases. Springer Nature.
  • Choudhri, A. F., Siddiqui, A., Khan, N. R. & Cohen, H. L. (2015). Understanding Bibliometric Parameters and Analysis. Radiographics, 35(3), 736-746.
  • Das, S. R. & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment Extraction from Small Talk on The Web. Management Science, 53(9), 1375-1388.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W. M. (2021). How to Conduct a Bibliometric Analysis: An Overview and Guidelines. Journal of Business Research, 133, 285-296.
  • Gao, F., Jia, X., Zhao, Z., Chen, C. C., Xu, F., Geng, Z. & Song, X. (2021). Bibliometric Analysis on Tendency and Topics of Artificial Intelligence Over Last Decade. Microsystem Technologies, 27, 1545-1557.
  • Goodell, J. W., Kumar, S., Lim, W. M. & Pattnaik, D. (2021). Artificial Intelligence and Machine Learning in Finance: Identifying Foundations, Themes, and Research Clusters from Bibliometric Analysis. Journal of Behavioral and Experimental Finance, 32, 100577.
  • Hassani, H., Silva, E. S., Unger, S., TajMazinani, M. & Mac Feely, S. (2020). Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is The Future?. Ai, 1(2), 143-155.
  • Herrmann, H. & Masawi, B. (2022). Three and A Half Decades of Artificial Intelligence in Banking, Financial Services, and Insurance: A Systematic Evolutionary Review. Strategic Change, 31(6), 549-569.
  • International Data Corporation. (2022). Worldwide Artificial Intelligence Spending Guide. https://www.idc.com/getdoc.jsp?containerId=prUS49670322.
  • Janková, Z. (2021). A Bibliometric Analysis of Artificial Intelligence Technique in Financial Market. Scientific Papers of The University of Pardubice. Series D, Faculty of Economics & Administration, 29(3), 1268.
  • Kumbure, M. M., Lohrmann, C., Luukka, P. & Porras, J. (2022). Machine Learning Techniques and Data for Stock Market Forecasting: A Literature Review. Expert Systems with Applications, 197, 116659.
  • Malekipirbazari, M. & Aksakalli, V. (2015). Risk Assessment in Social Lending via Random Forests. Expert Systems with Applications, 42(10), 4621-4631.
  • Mandala, G. N., Buddhi, D., Arumugam, M., Harbola, S., Othman, B. & Almashaqbeh, H. A. (2022). A Critical Review of Applications of Artificial Intelligence (AI) And Its Powered Technologies in The Financial Industry. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2362-2365. IEEE.
  • Milana, C. & Ashta, A. (2021). Artificial Intelligence Techniques in Finance and Financial Markets: A Survey of The Literature. Strategic Change, 30(3), 189-209.
  • Nazareth, N. & Reddy, Y. Y. R. (2023). Financial Applications of Machine Learning: A Literature Review. Expert Systems with Applications, 119640.
  • OECD. (2021a). OECD Business and Finance Outlook 2021: AI in Business and Finance. OECD Publishing, Paris.
  • OECD. (2021b). Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. https://www.oecd.org/finance/artificial-intelligence-machine-learningbig-data-in-finance.htm.
  • Patel, J., Shah, S., Thakkar, P. & Kotecha, K. (2015). Predicting Stock Market Index Using Fusion of Machine Learning Techniques. Expert Systems with Applications, 42(4), 2162-2172.
  • Pindoriya, N. M., Singh, S. N. & Singh, S. K. (2008). An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets. IEEE Transactions on Power Systems, 23(3), 1423-1432.
  • Preis, T., Reith, D. & Stanley, H. E. (2010). Complex Dynamics of Our Economic Life on Different Scales: Insights from Search Engine Query Data. Philosophical Transactions of The Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1933), 5707-5719.
  • Ren, Y. S., Ma, C. Q., Kong, X. L., Baltas, K. & Zureigat, Q. (2022). Past, Present, and Future of The Application of Machine Learning in Cryptocurrency Research. Research in International Business and Finance, 63, 101799.
  • Şeker, Y. & Atasel O. Y. (2023). Muhasebe Alanındaki Çalışmaların SciVal Analitiğe Dayalı Bibliyometrik Performans Analizi. Fiscaoeconomia, 7(1), 862-884.
  • Sezer, O. B., Gudelek, M. U. & Ozbayoglu, A. M. (2020). Financial Time Series Forecasting with Deep Learning: A Systematic Literature Review: 2005-2019. Applied Soft Computing, 90, 106181.
  • Shi, Y. & Li, X. (2019). A Bibliometric Study on Intelligent Techniques of Bankruptcy Prediction for Corporate Firms. Heliyon, 5(12), e02997.
  • Singh, V. K., Singh, P., Karmakar, M., Leta, J. & Mayr, P. (2021). The Journal Coverage of Web of Science, Scopus and Dimensions: A Comparative Analysis. Scientometrics, 126, 5113-5142.
  • Weber, P., Carl, K. V. & Hinz, O. (2023). Applications of Explainable Artificial Intelligence in Finance-A Systematic Review of Finance, Information Systems, and Computer Science Literature. Management Review Quarterly, 1-41.
  • Zhang, G., Hu, M. Y., Patuwo, B. E. & Indro, D. C. (1999). Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis. European Journal of Operational Research, 116(1), 16-32.

Finans Alanında Yapay Zekâ ve Uzman Sistemlerin Kullanımı: Bibliyometrik Bir Analiz

Year 2023, , 2110 - 2127, 18.09.2023
https://doi.org/10.25295/fsecon.1269889

Abstract

Yapay zekâ teknolojisindeki gelişmeler çeşitli sektörler üzerinde de etkili olmaktadır. Yapay zekâ teknolojisinin en fazla kullanıldığı sektörlerden biri de finans alanıdır. Bu durum araştırmacıların da ilgisini çekmekte ve finans alanındaki yapay zekâ uygulamalarına yönelik literatür de gün geçtikçe artmaktadır. Bu doğrultuda çalışmanın amacı, finans alanındaki yapay zekâ ve uzman sistemler üzerine gelişen literatürü incelemektir. Bibliyometrik analiz yaklaşımı kullanılarak Scopus veri tabanında yer alan 452 makale 1988-2022 dönemi için değerlendirilmiştir. Bu bağlamda R tabanlı bibliometrix programından yararlanılarak ülkeler, üniversiteler, dergiler ve yazarlar açısından analizler gerçekleştirilmiştir. Çalışmanın sonucunda, araştırılan konudaki makalelerin sayısı yıllar itibariyle artmakla birlikte en fazla artışın son yıllarda gerçekleştiği belirlenmiştir. En üretken ve en etkili dergi “Expert Systems with Applications” ve en etkili yazar ise Doumpos (2001) olmuştur. Bununla birlikte en fazla yayın yapan kurum ve ülke sırasıyla “Hunan University of Finance and Economics” ve Çin’dir. Üstelik Çin en fazla etkileşimde bulunan ülke konumundadır. Diğer taraftan incelenen çalışmalarda en fazla yer alan anahtar kelimenin yapay zekâ olduğu ve bu kavramının finans ve makine öğrenimi kavramlarıyla arasında güçlü bir bağ olduğu tespit edilmiştir. Uzman sistemler kavramı ise kullanım sayısı açısından altıncı sırada yer almaktadır. Bu çalışmanın sonuçları finans alanındaki yapay zekâ ve uzman sistemler literatürünün genel bir görünümünü sunmaktadır.

References

  • Ahmed, S., Alshater, M. M., El Ammari, A. & Hammami, H. (2022). Artificial Intelligence and Machine Learning in Finance: A Bibliometric Review. Research in International Business and Finance, 61, 101646.
  • Aria, M. & Cuccurullo, C. (2017). Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. Journal of Informetrics, 11(4), 959-975.
  • Bahrammirzaee, A. (2010). A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems. Neural Computing and Applications, 19(8), 1165-1195.
  • Boukherouaa, E. B., Shabsigh, M. G., AlAjmi, K., Deodoro, J., Farias, A., Iskender, E. S., Mirestean, A. T. & Ravikumar, R. (2021). Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance. International Monetary Fund.
  • Canbas, S., Cabuk, A. & Kilic, S. B. (2005). Prediction of Commercial Bank Failure Via Multivariate Statistical Analysis of Financial Structures: The Turkish Case. European Journal of Operational Research, 166(2), 528-546.
  • Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P. & Oliveira, A. L. (2016). Computational Intelligence and Financial Markets: A Survey and Future Directions. Expert Systems with Applications, 55, 194-211.
  • Chan, L., Hogaboam, L. & Cao, R. (2022). Applied Artificial Intelligence in Business: Concepts and Cases. Springer Nature.
  • Choudhri, A. F., Siddiqui, A., Khan, N. R. & Cohen, H. L. (2015). Understanding Bibliometric Parameters and Analysis. Radiographics, 35(3), 736-746.
  • Das, S. R. & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment Extraction from Small Talk on The Web. Management Science, 53(9), 1375-1388.
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W. M. (2021). How to Conduct a Bibliometric Analysis: An Overview and Guidelines. Journal of Business Research, 133, 285-296.
  • Gao, F., Jia, X., Zhao, Z., Chen, C. C., Xu, F., Geng, Z. & Song, X. (2021). Bibliometric Analysis on Tendency and Topics of Artificial Intelligence Over Last Decade. Microsystem Technologies, 27, 1545-1557.
  • Goodell, J. W., Kumar, S., Lim, W. M. & Pattnaik, D. (2021). Artificial Intelligence and Machine Learning in Finance: Identifying Foundations, Themes, and Research Clusters from Bibliometric Analysis. Journal of Behavioral and Experimental Finance, 32, 100577.
  • Hassani, H., Silva, E. S., Unger, S., TajMazinani, M. & Mac Feely, S. (2020). Artificial Intelligence (AI) or Intelligence Augmentation (IA): What Is The Future?. Ai, 1(2), 143-155.
  • Herrmann, H. & Masawi, B. (2022). Three and A Half Decades of Artificial Intelligence in Banking, Financial Services, and Insurance: A Systematic Evolutionary Review. Strategic Change, 31(6), 549-569.
  • International Data Corporation. (2022). Worldwide Artificial Intelligence Spending Guide. https://www.idc.com/getdoc.jsp?containerId=prUS49670322.
  • Janková, Z. (2021). A Bibliometric Analysis of Artificial Intelligence Technique in Financial Market. Scientific Papers of The University of Pardubice. Series D, Faculty of Economics & Administration, 29(3), 1268.
  • Kumbure, M. M., Lohrmann, C., Luukka, P. & Porras, J. (2022). Machine Learning Techniques and Data for Stock Market Forecasting: A Literature Review. Expert Systems with Applications, 197, 116659.
  • Malekipirbazari, M. & Aksakalli, V. (2015). Risk Assessment in Social Lending via Random Forests. Expert Systems with Applications, 42(10), 4621-4631.
  • Mandala, G. N., Buddhi, D., Arumugam, M., Harbola, S., Othman, B. & Almashaqbeh, H. A. (2022). A Critical Review of Applications of Artificial Intelligence (AI) And Its Powered Technologies in The Financial Industry. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2362-2365. IEEE.
  • Milana, C. & Ashta, A. (2021). Artificial Intelligence Techniques in Finance and Financial Markets: A Survey of The Literature. Strategic Change, 30(3), 189-209.
  • Nazareth, N. & Reddy, Y. Y. R. (2023). Financial Applications of Machine Learning: A Literature Review. Expert Systems with Applications, 119640.
  • OECD. (2021a). OECD Business and Finance Outlook 2021: AI in Business and Finance. OECD Publishing, Paris.
  • OECD. (2021b). Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. https://www.oecd.org/finance/artificial-intelligence-machine-learningbig-data-in-finance.htm.
  • Patel, J., Shah, S., Thakkar, P. & Kotecha, K. (2015). Predicting Stock Market Index Using Fusion of Machine Learning Techniques. Expert Systems with Applications, 42(4), 2162-2172.
  • Pindoriya, N. M., Singh, S. N. & Singh, S. K. (2008). An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets. IEEE Transactions on Power Systems, 23(3), 1423-1432.
  • Preis, T., Reith, D. & Stanley, H. E. (2010). Complex Dynamics of Our Economic Life on Different Scales: Insights from Search Engine Query Data. Philosophical Transactions of The Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1933), 5707-5719.
  • Ren, Y. S., Ma, C. Q., Kong, X. L., Baltas, K. & Zureigat, Q. (2022). Past, Present, and Future of The Application of Machine Learning in Cryptocurrency Research. Research in International Business and Finance, 63, 101799.
  • Şeker, Y. & Atasel O. Y. (2023). Muhasebe Alanındaki Çalışmaların SciVal Analitiğe Dayalı Bibliyometrik Performans Analizi. Fiscaoeconomia, 7(1), 862-884.
  • Sezer, O. B., Gudelek, M. U. & Ozbayoglu, A. M. (2020). Financial Time Series Forecasting with Deep Learning: A Systematic Literature Review: 2005-2019. Applied Soft Computing, 90, 106181.
  • Shi, Y. & Li, X. (2019). A Bibliometric Study on Intelligent Techniques of Bankruptcy Prediction for Corporate Firms. Heliyon, 5(12), e02997.
  • Singh, V. K., Singh, P., Karmakar, M., Leta, J. & Mayr, P. (2021). The Journal Coverage of Web of Science, Scopus and Dimensions: A Comparative Analysis. Scientometrics, 126, 5113-5142.
  • Weber, P., Carl, K. V. & Hinz, O. (2023). Applications of Explainable Artificial Intelligence in Finance-A Systematic Review of Finance, Information Systems, and Computer Science Literature. Management Review Quarterly, 1-41.
  • Zhang, G., Hu, M. Y., Patuwo, B. E. & Indro, D. C. (1999). Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis. European Journal of Operational Research, 116(1), 16-32.
There are 33 citations in total.

Details

Primary Language English
Subjects Finance
Journal Section Articles
Authors

Yusuf Güneysu 0000-0002-6809-1995

Publication Date September 18, 2023
Published in Issue Year 2023

Cite

APA Güneysu, Y. (2023). The Use of Artificial Intelligence and Expert Systems in Finance: A Bibliometric Analysis. Fiscaoeconomia, 7(3), 2110-2127. https://doi.org/10.25295/fsecon.1269889

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