Big-Data Modeling of Nonlinear Banking Fragility: Quantum Features, Machine Learning Validation, and Explainable Risk Signals

Authors

1 College of Accounting, Guangzhou Huashang College, Guangzhou 511300, China
2 Department of Auditing, School of Accounting, Guangzhou Huashang College, Guangzhou 511300, China
3 School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou 511300, China
Corresponding author: 464085923@qq.com

DOI:

https://doi.org/10.63646/ADMP9926

Keywords:

Banking fragility; quantum-inspired features; random forest; SHAP; big data; explainable machine learning; emerging markets

Abstract

Banking fragility in emerging markets emerges from interactions among credit risk, market volatility, concentration, and macroeconomic shocks that conventional linear panel models struggle to capture. This study develops a big-data analytical pipeline that fuses functional features derived from Quantum Field Theory (QFT)—including a double-well stochastic potential indicator and a Faddeev-Popov constrained quantization correction—with supervised and unsupervised machine learning to detect nonlinear, regime-switching dynamics in the Mexican banking system. Drawing on an annual panel of eleven multiple-banking institutions covering 2014 to 2023, we engineer 14 micro-prudential, macro-financial, and quantum-inspired features. We then validate the quantum indicators against observed insolvency proxies via logistic regression, Random Forest classification with SHAP-based interpretation, principal-component clustering, and bootstrap resampling. The quantum fragility feature is positively and significantly associated with the lower-quartile Z-score state (coefficient = 2.66, p = 0.003) and yields measurable lifts in extreme-event sensitivity, particularly around the 2016 emerging-market shock and the 2020 pandemic. Random Forest importance and SHAP attribution rank non-performing loans, return on assets, the Lerner index, and the capitalization ratio as the dominant risk drivers, with the Faddeev-Popov correction contributing complementary signal in transition periods. The framework offers a reproducible, explainable big-data architecture for prudential supervision, early-warning systems, and research on financial fragility in emerging markets.

How to Cite

Chen, P., Ma, Y., & Zhang, L. (2026). Big-Data Modeling of Nonlinear Banking Fragility: Quantum Features, Machine Learning Validation, and Explainable Risk Signals. Data Science & Big Data Technology, 1(1), 197-223. https://doi.org/10.63646/ADMP9926

References

Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608. https://doi.org/10.1257/aer.20130456

Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1705–1741. https://doi.org/10.1257/aer.20120555

Allen, F., & Carletti, E. (2013). Systemic risk from real estate and macro-prudential regulation. International Journal of Banking, Accounting and Finance, 5(1/2), 28–48. https://doi.org/10.1504/IJBAAF.2013.058091

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman's Z-score model. Journal of International Financial Management & Accounting, 28(2), 131–171. https://doi.org/10.1111/jifm.12053

Anginer, D., Demirgüç-Kunt, A., & Zhu, M. (2014). How does competition affect bank systemic risk? Journal of Financial Intermediation, 23(1), 1–26. https://doi.org/10.1016/j.jfi.2013.11.001

Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685–725. https://doi.org/10.1146/annurev-economics-080217-053433

Baaquie, B. E. (2018). Quantum field theory for economics and finance. Cambridge University Press. https://doi.org/10.1017/9781108399685

Banxico. (2022). Financial system report 2022. Bank of Mexico. https://www.banxico.org.mx/publications-and-press/financial-system-report/financial-system-report.html

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

Battiston, S., Caldarelli, G., May, R. M., Roukny, T., & Stiglitz, J. E. (2016). The price of complexity in financial networks. Proceedings of the National Academy of Sciences, 113(36), 10031–10036. https://doi.org/10.1073/pnas.1521573113

Battiston, S., Mandel, A., Monasterolo, I., Schütze, F., & Visentin, G. (2017). A climate stress-test of the financial system. Nature Climate Change, 7(4), 283–288. https://doi.org/10.1038/nclimate3255

Bazdresch, S., & Werner, A. M. (2017). Contagion of international financial crises: The case of Mexico. World Scientific Studies in International Economics, 58, 65–88. https://doi.org/10.1142/9789813222908_0004

Beck, T., De Jonghe, O., & Schepens, G. (2013). Bank competition and stability: Cross-country heterogeneity. Journal of Financial Intermediation, 22(2), 218–244. https://doi.org/10.1016/j.jfi.2012.07.001

Berger, A. N., Klapper, L. F., & Turk-Ariss, R. (2009). Bank competition and financial stability. Journal of Financial Services Research, 35(2), 99–118. https://doi.org/10.1007/s10693-008-0050-7

Bernal, M., & López, A. (2022). Bankruptcy prediction in Mexican firms: A machine learning approach. Contaduría y Administración, 67(3), 1–28. https://doi.org/10.22201/fca.24488410e.2022.3370

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195–202. https://doi.org/10.1038/nature23474

Bolton, P., Despres, M., Pereira da Silva, L. A., Samama, F., & Svartzman, R. (2020). The green swan: Central banking and financial stability in the age of climate change. Bank for International Settlements. https://doi.org/10.2139/ssrn.3674434

Borio, C., & Drehmann, M. (2009). Assessing the risk of banking crises: Revisited. BIS Quarterly Review, March, 29–46. https://www.bis.org/publ/qtrpdf/r_qt0903e.htm

Boyd, J. H., & De Nicoló, G. (2005). The theory of bank risk taking and competition revisited. The Journal of Finance, 60(3), 1329–1343. https://doi.org/10.1111/j.1540-6261.2005.00763.x

Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: An application to default risk analysis. Bank of England Staff Working Paper, 816. https://doi.org/10.2139/ssrn.3435104

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.

Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203–216. https://doi.org/10.1007/s10614-020-10042-0

Cao, L. (2022). AI in finance: Challenges, techniques, and opportunities. ACM Computing Surveys, 55(3), 1–38. https://doi.org/10.1145/3502289

Carbó-Valverde, S., Cuadros-Solas, P. J., & Rodríguez-Fernández, F. (2020). A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests. PLOS ONE, 15(10), e0240362. https://doi.org/10.1371/journal.pone.0240362

Cardarelli, R., Elekdag, S., & Lall, S. (2011). Financial stress and economic contractions. Journal of Financial Stability, 7(2), 78–97. https://doi.org/10.1016/j.jfs.2010.01.005

Cermeño, R., León, F., & Mantey, G. (2021). Bank competition and stability in Mexico: Evidence from new spread-based measures. Cuadernos de Economía, 44(124), 51–63. https://doi.org/10.1016/j.cesjef.2020.05.001

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785

Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the AI frontier: Applications and value of deep learning. McKinsey Global Institute Discussion Paper.

Cont, R., & Schaanning, E. (2017). Fire sales, indirect contagion and systemic stress testing. Norges Bank Working Paper, 2017/2. https://doi.org/10.2139/ssrn.2541114

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

Demirgüç-Kunt, A., Detragiache, E., & Tressel, T. (2008). Banking on the principles: Compliance with Basel core principles and bank soundness. Journal of Financial Intermediation, 17(4), 511–542. https://doi.org/10.1016/j.jfi.2007.10.003

Drehmann, M., Borio, C., Gambacorta, L., Jiménez, G., & Trucharte, C. (2010). Countercyclical capital buffers: Exploring options. BIS Working Papers, 317. https://doi.org/10.2139/ssrn.1648946

Egger, D. J., Gambella, C., Marecek, J., McFaddin, S., Mevissen, M., Raymond, R., Simonetto, A., Woerner, S., & Yndurain, E. (2020). Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 1, 3101724. https://doi.org/10.1109/TQE.2020.3030314

Espinoza-Vega, M. A., & Solé, J. (2010). Cross-border financial surveillance: A network perspective. IMF Working Paper, WP/10/105. https://doi.org/10.5089/9781455200337.001

Faddeev, L. D., & Slavnov, A. A. (1980). Gauge fields: An introduction to quantum theory. Benjamin/Cummings.

Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669. https://doi.org/10.1016/j.ejor.2017.11.054

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451

Gai, P., & Kapadia, S. (2010). Contagion in financial networks. Proceedings of the Royal Society A, 466(2120), 2401–2423. https://doi.org/10.1098/rspa.2009.0410

García-Herrero, A. (2022). Latin American financial systems after the pandemic. Latin American Journal of Central Banking, 3(2), 100061. https://doi.org/10.1016/j.latcb.2022.100061

Goldstein, I., Jiang, W., & Karolyi, G. A. (2019). To FinTech and beyond. The Review of Financial Studies, 32(5), 1647–1661. https://doi.org/10.1093/rfs/hhz025

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009

Hałaj, G., Martinez-Jaramillo, S., & Battiston, S. (2024). Financial stability through the lens of complex systems. Journal of Financial Stability, 71, 101228. https://doi.org/10.1016/j.jfs.2024.101228

Hao, W., Lefèvre, C., Tamturk, M., & Utev, S. (2019). Quantum option pricing and data analysis. Quantitative Finance and Economics, 3(3), 490–507. https://doi.org/10.3934/QFE.2019.3.490

Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209–212. https://doi.org/10.1038/s41586-019-0980-2

Heaton, J. B., Polson, N. G., & Witte, J. H. (2017). Deep learning for finance: Deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3–12. https://doi.org/10.1002/asmb.2209

Hernández-Trillo, F., & Villagómez, F. A. (2012). The behavior of debt in Latin America: Did fundamentals change? Economic Modelling, 29(5), 1773–1781. https://doi.org/10.1016/j.econmod.2012.05.027

Hoffart, F. M., D'Orazio, P., Holz, F., & Kemfert, C. (2024). Exploring the interdependence of climate, finance, energy, and geopolitics. Applied Energy, 361, 122885. https://doi.org/10.1016/j.apenergy.2024.122885

Hung, J.-L., He, W., & Shen, J. (2020). Big data analytics for supply chain relationship in banking. Industrial Marketing Management, 86, 144–153. https://doi.org/10.1016/j.indmarman.2019.11.001

Kaminsky, G. L., & Reinhart, C. M. (1999). The twin crises: The causes of banking and balance-of-payments problems. American Economic Review, 89(3), 473–500. https://doi.org/10.1257/aer.89.3.473

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. https://doi.org/10.48550/arXiv.1711.08251

Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787. https://doi.org/10.1016/j.jbankfin.2010.06.001

Kingsly, P. K. M. (2025). Quantum finance and structural transformation of less developed countries. SSRN. https://doi.org/10.2139/ssrn.5219462

Kou, G., & Lu, Y. (2025). FinTech: A literature review of emerging financial technologies and applications. Financial Innovation, 11(1), 1–34. https://doi.org/10.1186/s40854-024-00668-6

Laeven, L., & Valencia, F. (2020). Systemic banking crises database II. IMF Economic Review, 68(2), 307–361. https://doi.org/10.1057/s41308-020-00107-3

Lepetit, L., & Strobel, F. (2015). Bank insolvency risk and Z-score measures: A refinement. Finance Research Letters, 13, 214–224. https://doi.org/10.1016/j.frl.2015.01.001

Lepetit, L., Nys, E., Rous, P., & Tarazi, A. (2008). Bank income structure and risk: An empirical analysis of European banks. Journal of Banking & Finance, 32(8), 1452–1467. https://doi.org/10.1016/j.jbankfin.2007.12.002

Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030

Liu, Q., Yang, K., Sun, M., & Tang, R. (2020). Big data for financial risk management: A bibliometric and visualization analysis. Sustainability, 12(5), 1842. https://doi.org/10.3390/su12051842

Lu, W., Lu, Y., Li, J., Sigov, A., Ratkin, L., & Ivanov, L. A. (2024). Quantum machine learning: Classifications, challenges, and solutions. Journal of Industrial Information Integration, 42, 100736. https://doi.org/10.1016/j.jii.2024.100736

Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29. https://doi.org/10.1080/23270012.2019.1570365

Lu, Y. (2021). Technological innovation and the emergence of a new interdisciplinary field: Management analytics. Nanotechnologies in Construction, 13(3), 181–192. https://doi.org/10.15828/2075-8545-2021-13-3-181-192

Lu, Y. (2025). The current status and developing trends of Industry 4.0: A review. Information Systems Frontiers, 27(1), 215–234. https://doi.org/10.1007/s10796-021-10221-w

Lu, Y., & Yang, J. (2024). Quantum financing system: A survey on quantum algorithms, potential scenarios and open research issues. Journal of Industrial Information Integration, 41, 100663. https://doi.org/10.1016/j.jii.2024.100663

Lu, Y., Ivanov, L. A., Wang, F., Pisarenko, Z. V., & Ye, C. (2024). Management analytics: A bibliometric analysis. Nanotechnologies in Construction, 16(3), 257–266. https://doi.org/10.15828/2075-8545-2024-16-3-257-266

Lu, Y., Pisarenko, Z. V., Yang, L., & Ye, C. (2024). Advancing decision-making: The role of management analytics in modern business practices. Nanotechnologies in Construction, 16(5), 431–440. https://doi.org/10.15828/2075-8545-2024-16-5-431-440

Lu, Y., Sigov, A. S., Ratkin, L., Ivanov, L. A., & Zuo, M. (2023). Quantum computing and industrial information integration: A review. Journal of Industrial Information Integration, 35, 100511. https://doi.org/10.1016/j.jii.2023.100511

Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://doi.org/10.48550/arXiv.1705.07874

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9

Maldonado, W. L., Tourinho, O. A. F., & Valli, M. (2012). Exchange rate bubbles: Fundamental value estimation and rational expectations test. Journal of International Money and Finance, 31(5), 1033–1059. https://doi.org/10.1016/j.jimonfin.2011.12.013

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

Mishkin, F. S., & White, E. N. (2014). Unprecedented actions: The Federal Reserve's response to the global financial crisis. The Federal Reserve's Role in the Global Economy, 220–243. https://doi.org/10.1017/CBO9781316162170.011

Moreno-Brid, J. C. (2023). Mexican macroeconomic policy: Challenges and proposals. CEPAL Review, 140, 7–24. https://doi.org/10.18356/16840348-2023-140-1

Mugel, S., Kuchkovsky, C., Sánchez, E., Fernández-Lorenzo, S., Luis-Hita, J., Lizaso, E., & Orús, R. (2022). Dynamic portfolio optimization with real datasets using quantum processors and quantum-inspired tensor networks. Physical Review Research, 4(1), 013006. https://doi.org/10.1103/PhysRevResearch.4.013006

Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87

Ngonyama, N., Mgxekwa, B., & Sibanda, K. (2025). The impact of financial technology and cyber risk on non-bank financial intermediation. Springer. https://doi.org/10.1007/978-3-031-86224-3_11

Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028. https://doi.org/10.1016/j.revip.2019.100028

Peskin, M. E., & Schroeder, D. V. (1995). An introduction to quantum field theory. CRC Press. https://doi.org/10.1201/9780429503559

Petropoulos, A., Siakoulis, V., Stavroulakis, E., & Vlachogiannakis, N. E. (2020). Predicting bank insolvencies using machine learning techniques. International Journal of Forecasting, 36(3), 1092–1113. https://doi.org/10.1016/j.ijforecast.2019.11.005

Philippon, T. (2019). On FinTech and financial inclusion. NBER Working Paper, 26330. https://doi.org/10.3386/w26330

Pistoia, M., Ahmad, S. F., Ajagekar, A., Buts, A., Chakrabarti, S., Herman, D., Hu, S., Jena, A., Minssen, P., Niroula, P., Rattew, A., Sun, Y., & Yalovetzky, R. (2021). Quantum machine learning for finance. arXiv. https://doi.org/10.48550/arXiv.2109.04298

Rebentrost, P., Gupt, B., & Bromley, T. R. (2018). Quantum computational finance: Monte Carlo pricing of financial derivatives. Physical Review A, 98(2), 022321. https://doi.org/10.1103/PhysRevA.98.022321

Reinhart, C. M., & Rogoff, K. S. (2009). The aftermath of financial crises. American Economic Review, 99(2), 466–472. https://doi.org/10.1257/aer.99.2.466

Reinhart, C. M., & Rogoff, K. S. (2014). Recovery from financial crises: Evidence from 100 episodes. American Economic Review, 104(5), 50–55. https://doi.org/10.1257/aer.104.5.50

Schaeck, K., & Cihák, M. (2014). Competition, efficiency, and stability in banking. Financial Management, 43(1), 215–241. https://doi.org/10.1111/fima.12010

Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172–185. https://doi.org/10.1080/00107514.2014.964942

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. https://doi.org/10.1016/j.asoc.2020.106181

Sidaoui, J., Ramos-Francia, M., & Cuadra, G. (2010). The global financial crisis and policy response in Mexico. BIS Papers, 54, 279–298. https://www.bis.org/publ/bppdf/bispap54r.pdf

Sigrist, F., & Hirnschall, C. (2019). Grabit: Gradient tree-boosted Tobit models for default prediction. Journal of Banking & Finance, 102, 177–192. https://doi.org/10.1016/j.jbankfin.2019.03.004

Stamatopoulos, N., Egger, D. J., Sun, Y., Zoufal, C., Iten, R., Shen, N., & Woerner, S. (2020). Option pricing using quantum computers. Quantum, 4, 291. https://doi.org/10.22331/q-2020-07-06-291

Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41–56. https://doi.org/10.1016/j.knosys.2013.12.006

Tovar-Silos, R., & Cano-Plata, E. (2020). Financial stability indicators for emerging markets: Methodological proposal and empirical evidence for Mexico. Revista Mexicana de Economía y Finanzas, 15(4), 547–566. https://doi.org/10.21919/remef.v15i4.500

Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How 'big data' can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031

Wu, H. P., Liu, Z., Dong, H. Y., Lu, Y., & Xu, L. D. (2025). Revolutionizing internal auditing: Harnessing the power of blockchain. Enterprise Information Systems, 19(1–2), 2448003. https://doi.org/10.1080/17517575.2024.2448003

Xu, R., Zhu, J., Yang, L., Lu, Y., & Xu, L. D. (2024). Decentralized finance (DeFi): A paradigm shift in the FinTech. Enterprise Information Systems, 18(9), 2397630. https://doi.org/10.1080/17517575.2024.2397630

Ye, Z., & Lu, Y. (2022). Quantum science: A review and current research trends. Journal of Management Analytics, 9(3), 383–402. https://doi.org/10.1080/23270012.2022.2089064

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

Zhou, J. (2025). Quantum finance: Exploring the implications of quantum computing on financial models. Computational Economics. https://doi.org/10.1007/s10614-025-10894-4