Understanding confidence in Banksthe role of personal characteristics and Artificial Intelligence

  1. Gómez-Martínez, Raúl 1
  2. Pérez-González, Benito 2
  3. Medrano-García, María Luisa 1
  4. Torres-Pruñonosa, Jose 2
  1. 1 Universidad Rey Juan Carlos (España)
  2. 2 Universidad Internacional de La Rioja (España)
Revista:
Anales del Instituto de Actuarios Españoles

ISSN: 0534-3232

Año de publicación: 2024

Número: 30

Tipo: Artículo

DOI: 10.26360/2024_03 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Anales del Instituto de Actuarios Españoles

Resumen

La confianza en los bancos y las instituciones financieras es una piedra angular de la estabilidad financiera y la prosperidad económica. Este estudio investiga la relación entre las características personales y la confianza en los bancos, reconociendo el papel fundamental de la confianza en la formación de las percepciones de los individuos sobre las instituciones financieras. Mediante un enfoque de métodos mixtos que combina técnicas de encuesta y modelización de inteligencia artificial, analizamos datos recogidos de una muestra representativa de la comunidad universitaria. Nuestros resultados ponen de relieve la influencia significativa de factores demográficos como la edad, el sexo y el nivel educativo en la confianza en los bancos. Además, validamos nuestra hipótesis utilizando métricas como el área ROC y el área PRC, que indican el poder predictivo de las características personales a la hora de determinar la confianza en los bancos. El análisis de sensibilidad aclara aún más la importancia relativa de los distintos predictores en la configuración de los niveles de confianza. Las implicaciones de nuestra investigación se extienden a los responsables políticos, las instituciones financieras y los investigadores, ofreciendo ideas para intervenciones a medida, estrategias de captación de clientes y futuras investigaciones. Al profundizar en el conocimiento de los factores que impulsan la confianza en los bancos, este estudio contribuye a mejorar la estabilidad financiera y la confianza de los consumidores en el sector bancario.

Referencias bibliográficas

  • Andrade, J. L., & Valencia, J. L. (2021, December). Modeling lapse rates using machine learning: a comparison between survival forests and cox proportional hazards techniques. Anales del Instituto de Actuarios Españoles, 27, 161-183. https://doi.org/10.26360/2021_7
  • Andres, C., Betzer, A., Doumet, M. (2021): Measuring changes in credit risk: The case of CDS event studies, Global Finance Journal, 49, 100647. https://doi.org/10.1016/j.gfj.2021.100647
  • Andros, S., Akimova, L., & Butkevich, O. (2020). Innovations in management of banks deposit portfolio: structure of customer deposit. Marketing & Management of Innovations, (2). https://doi.org/10.21272/mmi.2020.2-15
  • Angelov, P. P., Soares, E. A., Jiang, R., Arnold, N. I., & Atkinson, P. M. (2021). Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), e1424. https://doi.org/10.1002/widm.1424
  • Attwal, K. P. S., & Dhiman, A. S. (2020). Exploring data mining tool-Weka and using Weka to build and evaluate predictive models. Advances and Applications in Mathematical Sciences, 19(6), 451-469
  • Bhardwaj, A., Mishra, S., & Jain, T. K. (2021). An analysis to understanding the job satisfaction of employees in banking industry. Materials Today: Proceedings, 37, 170-174. https://doi.org/10.1016/j.matpr.2020.04.783
  • Bigné, E. (2020). Teaching in business administration: a customized process driven by technological innovations. Journal of Management and Business Education, 3(1), 4–15. https://doi.org/10.35564/jmbe.2020.0002
  • Bouallègue, Z.B., & Richardson, D.S. (2022). On the ROC Area of Ensemble Forecasts for Rare Events, 27(5), 787–796. https://doi.org/10.1175/WAF-D-21-0195.1
  • Chauhan, J. S., Kaushal, D., & Pais, R. (2023). Factors Determining the Bank Selection Criteria of Consumers: A Banking Customers Survey. European Economic Letters (EEL), 13(1), 286-291. https://doi.org/10.52783/eel.v13i1.169
  • De Leon, M. V., & Tubay, J. B. (2020). Ethical leadership of supervisors and internal social capital in a financial institution. International Journal of Economics and Business Administration. https://doi.org/10.35808/ijeba/462
  • Ekström, J., Åkerrén Ögren, J., & Sjöblom, T. (2023). Exact Probability Distribution for the ROC Area under Curve. Cancers 15, 1788. https://doi.org/10.3390/cancers15061788
  • El Fouki, M., Aknin, N., & El Kadiri, K. E. (2019). Towards an improved classification model based on deep Learning and nearest rules strategy. Campus Virtuales, 8(1), 111-119. http://www.uajournals.com/campusvirtuales/journal/14/9.pdf
  • Goulet Coulombe, Ph. (2024). The macroeconomy as a random forest. Journal of Applied Econometrics (early view). https://doi.org/10.1002/jae.3030
  • Hammond, P., & Opoku, M.O. (2023). The mediating effect of going concern and corporatereporting in the relationship between corporate governance and investor confidence in financial institutions. Heliyon 9(10), e20447. https://doi.org/10.1016/j.heliyon.2023.e20447
  • Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226-251. https://doi.org/10.1016/j.eswa.2019.01.012
  • Huang, F., Xiong, H., Chen, S., Lv, Z., Huang, J., Chang, Z., & Catani, F. (2023). Slope stability prediction based on a long short-term memory neural network: comparisons with convolutional neural networks, support vector machines and random forest models. International Journal of Coal Science & Technology 10 (18). https://doi.org/10.1007/s40789-023-00579-4
  • Isidro, H., Nanda, D., & Wysocki, P. D. (2020). On the relation between financial reporting quality and country attributes: Research challenges and opportunities. The Accounting Review, 95(3), 279-314. https://dx.doi.org/10.2139/ssrn.3427028
  • Josephson, J., & Shapiro, J. (2020). Credit ratings and structured finance. Journal of Financial Intermediation, 41, 100816. https://doi.org/10.1016/j.jfi.2019.03.003
  • Karkošková, S. (2023). Data governance model to enhance data quality in financial institutions. Information Systems Management, 40(1), 90-110. https://doi.org/10.1080/10580530.2022.2042628
  • Kuchciak, I., & Wiktorowicz, J. (2021). Empowering financial education by banks—social media as a modern channel. Journal of Risk and Financial Management, 14(3), 118. https://doi.org/10.3390/jrfm14030118
  • Kusi, B. A., Forson, J. A., Adu-Darko, E., & Agbloyor, E. (2023). Global financial crisis, international capital requirement and bank financial stability: an international evidence. Journal of Financial Regulation and Compliance, 31(2), 237-258. https://doi.org/10.1108/JFRC-04-2022-0057
  • Laeven, L., & Valencia, F. (2020). Systemic banking crises database II. IMF Economic Review, 68, 307-361. https://doi.org/10.1057/s41308-020-00107-3
  • Lee, S. J., Posenau, K. E., & Stebunovs, V. (2020). The anatomy of financial vulnerabilities and banking crises. Journal of Banking & Finance, 112, 105334. https://doi.org/10.1016/j.jbankfin.2018.04.013
  • Lei, X., Mohamad, U.H., Sarlan, A., Shutaywi, M., Daradkeh, Y. I., & Mohammed, H.O. (2022). Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms. Information Processing & Management, 59(5). https://doi.org/10.1016/j.ipm.2022.103036
  • Mangala, D., & Soni, L. (2023). A systematic literature review on frauds in banking sector. Journal of Financial Crime, 30(1), 285-301. https://doi.org/10.1108/JFC-12-2021-0263
  • Marqués, F. (2020). Machine Learning. Técnicas de Análisis Supervisado: Modelos Predictivos Multiecuacional: Independently Published
  • Martín, E., & Caballero, R. (2020). Las bases de Big Data. España: Los Libros de La Catarata
  • Mikhaylov, A., Dinçer, H., & Yüksel, S. (2023). Analysis of financial development and open innovation oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs. Financial Innovation, 9(1), 1-34. https://doi.org/10.1186/s40854-022-00399-6
  • Nasreen, S., Mahalik, M. K., Shahbaz, M., & Abbas, Q. (2020). How do financial globalization, countries? Research in International Business and Finance, 54, 101247. https://doi.org/10.1016/j.ribaf.2020.101247
  • Ozili, P. K. (2020). Theories of financial inclusion. In Uncertainty and challenges in contemporary economic behaviour, 89-115. Emerald Publishing Limited. https://doi.org/10.1108/978-1-80043-095-220201008
  • Rice, M. E., & Harris, G. T. (2005). Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r. Law and Human Behavior, 29(5), 615–620. https://doi.org/10.1007/s10979-005-6832-7
  • Russell, R. (2018). Machine learning: guía paso a paso para implementar algoritmos de machine learning con Python: CreateSpace Independent Publishing Platform
  • Van der Cruijsen, C., de Haan, J., & Roerink, R. (2023). Trust in financial institutions: A survey. Journal of economic surveys, 37(4), 1214-1254. https://doi.org/10.1111/joes.12468
  • Vanclay, F. (2020). Reflections on Social Impact Assessment in the 21st century. Impact Assessment and Project Appraisal, 38(2), 126-131. https://doi.org/10.1080/14615517.2019.1685807
  • Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Netherland: Elsevier Science
  • Xu, X. (2020). Trust and financial inclusion: A cross-country study. Finance Research Letters, 35, 101310. https://doi.org/10.1016/j.frl.2019.101310
  • Yadav, S., & Shukla, S. (2016, February). Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In 2016 IEEE 6th International conference on advanced computing (IACC) (pp. 78-83). IEEE. https://doi.org/10.1109/IACC.2016.25
  • Yego, N.K.K. , Nkurunziza, J., & Kasozi, J. (2023). Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors. Plos One. https://doi.org/10.1371/journal.pone.0294166
  • Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224. https://doi.org/10.1016/j.jii.2021.100224
Fuente de los datos: Dialnet