New Technologies in the Financial Industry: Case of Poland

• Autorzy: Małgorzata Iwanicz-Drozdowska , Ewa Cichowicz , Marianna Cicirko , Marcin Kawiński , Agnieszka K. Nowak
• Języki publikacji: EN

Abstrakty

EN

This study evaluates the scope and consequences of the application of new technologies (NTs) within the Polish banking and insurance sectors and thus contributes to the knowledge of CEE financial market development. The goal is to understand the implementation of particular NTs in two different sectors and identify the motivations, strategies, phases of realisation and cost efficiency depending on the institution's size. The detail of the study requires the use of qualitative research methods. In-depth interviews are employed to figure out the criteria based on which decisions to implement NTs are made. The findings indicate that the primary objective of NT implementation is to respond to customers' needs, followed by cost-cutting and achieving more efficient internal processes. The application of artificial intelligence (AI) and machine learning (ML) in risk management areas is still a work in progress. In the next five years cloud computing is expected to become the most important NT and thus will have to meet numerous regulatory requirements. (original abstract)

Słowa kluczowe

EN

Innovations; High-tech; Artificial intelligence; Cloud computing; Banking sector; Insurance sector

PL

Innowacje; Nowe technologie; Sztuczna inteligencja; Chmura obliczeniowa; Sektor bankowy; Sektor ubezpieczeniowy

• Czasopismo: Economics and Business Review
• Rocznik: 2023
• Tom: 9 (23)
• Numer: nr 3
• Strony: 98--123

Twórcy

autor

Małgorzata Iwanicz-Drozdowska

• Warsaw School of Economics

autor

Ewa Cichowicz

• Warsaw School of Economics

autor

Marianna Cicirko

• Warsaw School of Economics

autor

Marcin Kawiński

• Warsaw School of Economics

autor

Agnieszka K. Nowak

• Warsaw School of Economics

Bibliografia

• Aashima, & Mohanty, B. (2022). How blockchain can transform the financial services ındustry. In K. Sood, R.K. Dhanaraj, B. Balusamy & S. Kadry (Eds.), Blockchain technology in corporate governance (pp. 253-281). https://doi.org/10.1002/9781119865247.ch12
• Altman, E., Iwanicz-Drozdowska, M., Laitinen, E., & Suvas, A. (2020). A race for long horizon bankruptcy prediction. Applied Economics, 52(37), 4092-4111. https://doi.org/10.1080/00036846.2020.1730762
• Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006
• Boot, A., Hoffmann, P., Laeven, L., & Ratnovski, L. (2021). FinTech: What's old, what's new? Journal of Financial Stability, 53, 100836. https://doi.org/10.1016/jjfs.2020.100836
• Cata, T., & Lee, S.M. (2006). Adoption of web-based applications in the financial sector: The case of online insurance. Journal of Internet Commerce, 5(2), 41-61. https://doi.org/10.1300/J179v05n02_03
• Cashless. (2021). The map of Polish Fintech. https://www.cashless.pl/report/mapa-polskiego-fintechu-2021-ang.pdf
• Durkin, M., Jennings, D., Mulholland, G., & Worthington, S. (2008). Key influencers and inhibitors on adoption of the Internet for banking. Journal of Retailing and Consumer Services, 15(5), 348-357. https://doi.org/10.1016/j.jretconser.2007.08.002
• Eling, M., Nuessle, D., & Staubli, J. (2022). The impact of artificial intelligence along the insurance value chain and on the insurability of risks. The Geneva Papers on Risk and Insurance - Issues and Practice, 47, 205-241. https://doi.org/10.1057/s41288-020-00201-7
• Faridpour, M., & Moradi, A. (2020). A novel method for detection of fraudulent bank transactions using multi-layer neural networks with adaptive learning rate. International Journal of Nonlinear Analysis and Applications, 11(2), 437-445. https://doi.org/10.22075/ijnaa.2020.4576
• Harasim, J. (2021). FinTechs, BigTechs and banks - when cooperation and when competition? Journal of Risk and Financial Management, 14(12), 614. https://doi.org/10.3390/jrfm14120614
• Hu, X. (2005). A data mining approach for retailing bank customer attrition analysis. Applied Intelligence, 22(1), 47-60. https://doi.org/10.1023/B:APIN.0000047383.53680.b6
• Jünger, M., & Mietzner, M. (2020). Banking goes digital: The adoption of FinTech services by German households. Finance Research Letters, 34, 101260. https://doi.org/10.1016/j.frl.2019.08.008
• Kitsios, F., Giatsidis, I., & Kamariotou, M. (2021). Digital transformation and strategy in the banking sector: Evaluating the acceptance rate of e-services. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 204. https://doi.org/10.3390/joitmc7030204
• Kliber, A., Będowska-Sójka, B., Rutkowska, A., & Świerczyńska, K. (2021). Triggers and obstacles to the development of the FinTech sector in Poland. Risks, 9(2), 30. https://doi.org/10.3390/risks9020030
• Lee-Ying, T., Hen-Toong, T., & Gek-Siang, T. (2022). Digital financial inclusion: A gateway to sustainable development, Heliyon, 8(6). https://doi.org/10.1016/j.heliyon.2022.e09766
• Marano, P., & Li, S. (2023) Regulating robo-advisors in ınsurance distribution: Lessons from the Insurance Distribution Directive and the AI Act. Risks, 11(1), 12. https://doi.org/10.3390/risks11010012
• Menendez, S., & Hassani, B.(2021). Expected shortfall reliability-added value of traditional statistics and advanced artificial ıntelligence for market risk measurement purposes. Mathematics, 9(17), 2142. https://doi.org/10.3390/math9172142
• Metawa, N., Hassan, M.K., & Metawa, S. (2023). Artificial ıntelligence and big data for financial risk management. Intelligent applications. Routledge.
• Miguel-Dávila, J.Á., Cabeza-García, L., Valdunciel, L., & Flórez, M. (2010). Operations in banking: The service quality and effects on satisfaction and loyalty. Service Industries Journal, 30(13), 2163-2182. https://doi.org/10.1080/02642060903289936
• Muganyi, T., Yan, L., Yin, Y., Sun, H., Gong, X., & Taghizadeh-Hesary F. (2022). FinTech, RegTech, and financial development: Evidence from China. Financial Innovation, 8(29) https://doi.org/10.1186/s40854-021-00313-6
• Piotrowski, D. (2023). Privacy frontiers in customers' relations with banks. Economics and Business Review, 9(1), 119-141. https://doi.org/10.18559/ebr.2023.1.5
• Prisznyák, A. (2022). Bankrobotics: Artificial ıntelligence and machine learning powered banking risk management prevention of money laundering and terrorist financing. Public Finance Quarterly, 67(2), 288-303. https://journals.lib.uni-corvinus.hu/index.php/penzugyiszemle/article/view/1194/629
• PwC. (2022). Uncovering the ground truth: AI in Indian financial services. PricewaterhouseCoopers Private Limited.
• Selimović, J., Pilav-Velić, A., & Krndžija, L. (2021). Digital workplace transformation in the financial service sector: Investigating the relationship between employees' expectations and intentions, Technology in Society, 66, 101640. https://doi.org/10.1016/j.techsoc.2021.101640
• Shala, A., & Perri, R. (2022). Regulatory barriers for fintech companies in Central and Eastern Europe. Eastern Journal of European Studies, 13(2), 292-316. https://doi.org/10.47743/ejes-2022-0214
• Siddik, M.N.A., & Kabiraj, S. (2020). Digital finance for financial inclusion and inclusive growth. In B. George & J. Paul (Eds.), Digital transformation in business and society (pp. 155-168). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-08277-2_10
• Solarz, M., & Adamek, J. (2022). Determinants of digital financial exclusion as a barrier to the adoption of mobile banking services in Poland. Ekonomia i Prawo, 21(2), 503-525. https://doi.org/10.12775/eip.2022.028
• Steiner, P.H., & Maas, P. (2018). When customers are willing to disclose information in the insurance industry: A multi-group analysis comparing ten countries. International Journal of Bank Marketing, 36(6), 1015-1033. https://doi. org/10.1108/IJBM-12-2016-0183
• Trivedi, S.K. (2020). A study on credit scoring modeling with different feature selection and machine learning approaches. Technology in Society, 63, 101413. https://doi.org/10.1016/j.techsoc.2020.101413
• Wang, C., Qiao, C., Ahmed, R.I., & Kirikkaleli, D. (2021). Institutional quality, bank finance and technological ınnovation: A way forward for Fourth Industrial Revolution in BRICS Economies. Technological Forecasting and Social Change, 163, 120427. https://doi.org/10.1016/j.techfore.2020.120427
• Wang, M., Cho, S., & Denton, T. (2017). The impact of personalisation and compatibility with past experience on e-banking usage. International Journal of Bank Marketing, 35(1), 45-55. https://doi.org/10.1108/IJBM-04-2015-0046

Identyfikatory

DOI

https://doi.org/10.18559/ebr.2023.3.926