[1] Shu, W., Strassmann, P. A. (2005). Does information technology provide banks with profit? Information & Management, 42 (5) 781-787. doi:10.1016/j.im.2003.06.007
[2] Agarwal, R., Dhar, V. (2014). Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research, Information Systems Research, 25 (3) 443-448. https://doi.org/10.1287/isre.2014.0546
[3] Turban, E., Sharda, R., Delen, D. (2014). Decision support and business intelligence systems. Essex: Pearson.
[4] Moro, S., Cortez, P., Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42 (3) 1314-1324. doi:10.1016/ j.eswa.2014.09.024
[5] Bhasin, M. L. (2006). Data Mining: A Competitive Tool in the Banking and Retail Industries. Banking and finance.
[6] Pulakkazhy, S., Balan, R. (2013). Data Mining In Banking And Its Applications-A Review. Journal of Computer Science, 9 (10) 1252-1259. doi:10.3844/jcssp.2013.1252. 1259
[7] Ngai, E., Hu, Y., Wong, Y., Chen, Y., Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50 (3) 559- 569. doi:10.1016/j.dss.2010.08.006
[8] Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50 (3) 602-613. doi:10.1016/j.dss.2010.08.008
[9] Wei, W., Li, J., Cao, L., Ou, Y., Chen, J. (2012). Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web, 16 (4) 449-475. doi:10.1007/s11280-012-0178-0
[10] Yap, B. W., Ong, S. H., Husain, N. H. (2011). Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38 (10) 13274-13283. doi:10.1016/j.eswa.2011.04. 147
[11] Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., Wu, S. (2004). Credit rating analysis with support vector machines and neural networks: A market comparative study. Decision Support Systems, 37 (4) 543-558. https://doi.org/10.1016/S0167-9236(03)00086-1
[12] Gulati, R., Goswami, A., Kumar, S., (2018), What drives credit risk in the Indian banking industry? An empirical investigation, Economic Systems, https://doi.org/
10.1016/j.ecosys.2018.08.004.
[13] Ali, G., Arýtürk, U. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41 (17) 7889-7903. doi:10.1016/j.eswa.2014.06.018
[14] Curko, K., Bach, M. P., Radonic, G. (2007). Business Intelligence and Business Process Management in Banking Operations. 2007 29th International Conference on Information Technology Interfaces. doi:10.1109/iti.2007.4283744.
[15] Azadeh, A., Saberi, M., Jiryaei, Z. (2012). An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank. Expert Systems with Applications, 39 (16) 12358-12370. doi:10.1016/j.eswa.2012.04.056
[16] Hughes, A. M. (1996). The Complete Database Marketer: Secondgeneration Strategies and Techniques for Tapping the Power of Your Customer Database, McGraw Hill.
[17] Abdou, H. A., Tsafack, M., Ntim, C. G., Baker, R. (2016). Predicting Creditworthiness in Retail Banking with Limited Scoring Data. SSRN Electronic Journal.
doi:10.2139/ssrn.2756746.
[18] Srivastava, U., Gopalkrishnan, S. (2015). Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks, Procedia Computer Science, Vol. 50, Pages 643 – 652, https://doi.org/10.1016/j.procs.2015.04.098.
[19] Narindra Mandalaa, G. N., Badra Nawangpalupia, C., Rian Praktikto, F. (2012). Assessing Credit Risk: an Application of Data Mining in a Rural Bank, Procedia Economics and Finance, Vol. 4, Pages 406 – 412, https://doi.org/10.1016/S2212-5671(12)00355-3
[20] Pérez-Martín, A., Pérez-Torregrosa, A., Vaca, M., (2018). Big Data techniques to measure credit banking risk in home equity loans, Journal of Business Research, Vol. 89, Pages 448-454, https://doi.org/10.1016/j.jbusres.2018.02.008.
[21] Batmaz, I., Danisoglu, S., Yazici, C., Kartal-Koç, E., (2017). A data mining application to deposit pricing: Main determinants and prediction model, Applied Soft Computing, Vol. 60, Pages 808 819, https://doi.org/10.1016/j.asoc.2017.07.047.
[22] Tavana, M., Patnaik, S. (2018). Recent Developments in Data Science and Business Analytics: Proceedings of the International Conference on Data Science and Business
Analytics (ICDSBA- 2017). Cham: Springer International Publishing.
[23] Li, S., Yen, D. C., Lu, W., Wang, C. (2012). Identifying the signs of fraudulent accounts using data mining techniques. Computers in Human Behavior, 28 (3) 1002-1013. doi:10.1016/j.chb.2012.01.002.
[24] Cao, D. K., Do, P. (2012). Applying Data Mining in Money Laundering Detection for the Vietnamese Banking Industry. Intelligent Information and Database Systems Lecture Notes in Computer Science, 207-216. doi:10.1007/978-3-642-28490-8_22.
[25] Aburrous, M. R., Hossain, A., Dahal, K., Thabatah, F. (2009). Modelling Intelligent Phishing Detection System for E-banking Using Fuzzy Data Mining. 2009 International Conference on CyberWorlds. doi:10.1109/cw.2009.43
[26] Liébana-Cabanillas, F., Nogueras, R., Herrera, L., Guillén, A. (2013). Analysing user trust in electronic banking using data mining methods. Expert Systems with Applications, 40 (14) 5439-5447. doi:10.1016/j.eswa.2013. 03.010
[27] Miguéis, V. L., Camanho, A. S., Borges, J. (2017). Predicting direct marketing response in banking: comparison of class imbalance methods. Service Business. doi:10.1007/s11628-016-0332-3
[28] Ekinci, Y., Uray, N., Ülengin, F. (2014). A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing, 48 (3/4), 761-784. doi:10.1108/ejm-12-2011-0714
[29] Hsieh, N. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27 (4) 623-633. doi:10.1016/j.eswa.2004.06.007
[30] Au, W., Chan, K. (2003). Mining fuzzy association rules in a bank-account database. IEEE Transactions on Fuzzy Systems, 11 (2) 238-248. doi:10.1109/tfuzz.2003. 809901
[31] Lin, S., Shiue, Y., Chen, S., Cheng, H. (2009). Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks. Expert Systems with Applications, 36 (9) 11543- 11551. doi:10.1016/j.eswa.2009.03.029
[32] Carmona, P., Climent, F., Momparler, A. (2018). Predicting failure in the U.S. banking sector: An extreme gradient boosting approach, International Review of Economics & Finance, https://doi.org/10.1016/j.iref.2018.03.008.
[33] Climent, F., Momparler, A., Carmona, P. (2018). Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach, Journal of Business Research, https://doi.org/10.1016/j.jbusres.2018.11.015.
[34] Zhao, H., Sinha, A. P., Ge, W. (2009). Effects of feature construction on classification performance: An empirical study in bank failure prediction. Expert Systems with Applications, 36 (2) 2633-2644. doi:10.1016/j.eswa. 2008.01.053
[35] Costa, G., Folino, F., Locane, A., Manco, G., Ortale, R. (2007). Data Mining for Effective Risk Analysis in a Bank Intelligence Scenario. 2007 IEEE 23rd International Conference on Data Engineering Workshop. doi:10.1109/icdew.2007.4401083
[36] Baghbani, G., Eskandari, F. (2017). Calculating the required cash in bank branches: a Bayesian-data mining approach. Neural Computing and Applications.
doi:10.1007/s00521-017-2888-9
[37] Ponniah, P. (2007). Data modeling fundamentals: a practical guide for IT professionals. Hoboken, NJ: Wiley- Interscience.
[38] Ambler, S. W. (2003). Agile database techniques: effective strategies for the agile software developer. Hoboken, NJ: Wiley.
[39] Silberschatz, A. (2013). Database System Concepts. S.L.: Mcgraw-Hill Education.
[40] Bagui, S. S., Earp, R. (2011). Database design using entity-relationship diagrams. Boca Raton, FL: Auerbach.
[41] Sekaran, U., Bougie, J. R. (2016). Research methods for business: a skill-building approach. Chichester: Wiley.
[42] Ghosh, B. N. (2002). Designing social research. Leeds: Wisdom House.
[43] Weber, R. (1990). Basic Content Analysis. doi:10.4135/9781412983488.
[44] Eren, C. (2008). Nested bitemporal relational data model.