Developing an Intelligent Model for Evaluating Banks’ Financial Performance Using Big Data, Risk Indicators, and Multi-Criteria Decision-Making Methods
Keywords:
Bank financial performance, big data, risk indicators, multi-criteria decision-making, thematic analysis, intelligent bankingAbstract
This study aimed to develop an intelligent model for evaluating banks’ financial performance by integrating banking big data, risk indicators, and multi-criteria decision-making methods within the context of the Iranian banking system. This qualitative study was conducted using thematic analysis. The participants consisted of banking experts, financial managers, risk managers, data analytics specialists, credit assessment experts, and university scholars in finance and banking located in Tehran. Purposeful and snowball sampling techniques were applied, and theoretical saturation was achieved after 22 semi-structured interviews. Interviews lasted between 45 and 75 minutes and were conducted using an interview guide focused on financial performance evaluation, risk-based assessment, banking data infrastructure, and intelligent decision-support mechanisms. Data analysis was performed through open, axial, and selective coding using NVivo software. Credibility and trustworthiness were enhanced through member checking, peer review, audit trail documentation, and constant comparison of codes and categories. The analysis resulted in five main categories: data-driven infrastructure and big data governance, integration of financial and risk indicators, dynamic weighting and multi-criteria decision-making, predictive intelligence and early warning mechanisms, and organizational-regulatory implementation capability. The findings indicated that banks’ financial performance evaluation can no longer rely solely on traditional profitability, liquidity, and capital adequacy ratios. Participants emphasized the need to integrate transactional, behavioral, credit, operational, regulatory, and macroeconomic data with risk indicators such as credit risk, liquidity risk, operational risk, asset quality, income resilience, and regulatory compliance. The proposed model redefines bank financial performance evaluation as an intelligent, multidimensional, risk-sensitive, and data-driven process. It may provide a practical foundation for managerial dashboards, bank ranking systems, risk early-warning tools, and strategic decision-making in the banking industry.
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References
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
Allsop, D. B., Chelladurai, J. M., Kimball, E. R., Marks, L. D., & Hendricks, J. J. (2022). Qualitative methods with NVivo software: A practical guide for analyzing qualitative data. Psych, 4(2), 142–159. https://doi.org/10.3390/psych4020013
Athanasoglou, P. P., Brissimis, S. N., & Delis, M. D. (2008). Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of International Financial Markets, Institutions and Money, 18(2), 121–136. https://doi.org/10.1016/j.intfin.2006.07.001
Basel Committee on Banking Supervision. (2013). Principles for effective risk data aggregation and risk reporting. Bank for International Settlements.
Berger, A. N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking & Finance, 21(6), 849–870. https://doi.org/10.1016/S0378-4266(97)00003-4
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
Federal Financial Institutions Examination Council. (1997). Uniform Financial Institutions Rating System. Federal Register, 62(3), 752–757.
Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European Journal of Operational Research, 204(2), 189–198. https://doi.org/10.1016/j.ejor.2009.08.003
Guest, G., Namey, E., & Chen, M. (2020). A simple method to assess and report thematic saturation in qualitative research. PLOS ONE, 15(5), e0232076. https://doi.org/10.1371/journal.pone.0232076
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer.
Kou, G., Peng, Y., & Lu, C. (2014). MCDM approach to evaluating bank loan default models. Technological and Economic Development of Economy, 20(2), 292–311. https://doi.org/10.3846/20294913.2014.913275
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
