Abdelhamid, N.; Ayesh, A.; Thabtah, F. (2014). Phishing detection based associative classification data mining. Expert Syst. Appl, 41, 5948–5959. [CrossRef]
Abellán, J.; Castellano, J.G. (2017). A comparative study on base classifiers in ensemble methods for credit scoring. Expert Syst. Appl, 73, 1–10.
Afolabi, I.T.; Ezenwoke, A.A.; Ayo, C.K. (2017). Competitive analysis of social media data in the banking industry. Int. J. Internet Market. Advert, 11, 183–201. [CrossRef]
Agrawal, R.; Imieli ´ nski, T.; Swami, A. (1993). Mining Association Rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data and ACM SIGMOD, Washington, DC, USA, 25–28 May 1; pp. 207–216.
Akhilomen, J. (2013). Data mining application for cyber credit-card fraud detection system. In Proceedings of the Industrial Conference on Data Mining, New York, NY, USA, 16–21 July 2013; Springer: Berlin/Heidelberg, Germany, 2013; pp. 218–228.
Alaraj, M.; Abbod, M.F. (2016). A new hybrid ensemble credit scoring model based on classifiers consensus system approach. Expert Syst. Appl, 64, 36–55. [CrossRef]
Alaraj, M.; Abbod, M.F. (20164). Classifiers consensus system approach for credit scoring. Knowl.-Based Syst. 2016, 104, 89–105. [CrossRef]
Ali, O.G.; Ariturk, U. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Syst. Appl, 41, 7889–7903.
Amani, F.A.; Fadlalla, A.M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. Int. J. Account. Inf. Syst, 24, 32–58. [CrossRef]
Amini, M.; Rezaeenour, J.; Hadavandi, E. (2015). A cluster-based data balancing ensemble classifier for response modeling in Bank Direct Marketing. Int. J. Comput. Intell. Appl, 14, 1550022. [CrossRef]
Azad, M.A.K. (2016). Predicting mobile banking adoption in Bangladesh: A neural network approach. Transnatl. Corp. Rev, 8, 207–214. [CrossRef]
Azimi, A.; Noor Hosseini, M. (2017). The hybrid approach based on genetic algorithm and neural network to predict financial fraud in banks. Int. J. Inf. Secur. Syst. Manag, 6, 657–667.
Bahari, T.F.; Elayidom, M.S. (2015). An efficient CRM-data mining framework for the prediction of customer behaviour. Procedia Comput. Sci, 46, 725–731. [CrossRef]
Barman, D.; Shaw, K.K.; Tudu, A.; Chowdhury, N. (2016). Classification of Bank Direct Marketing Data Using Subsets of Training Data. In Information Systems Design and Intelligent Applications; Springer: New Delhi, India; pp. 143–151.
Batmaz, I.; Danisoglu, S.; Yazici, C.; Kartal-Koc, E. (2017). A data mining application to deposit pricing: Main determinants and prediction models. Appl. Soft Comput, 60, 808–819. [CrossRef]
Behera, T.K.; Panigrahi, S. (2017). Credit Card Fraud Detection Using a Neuro-Fuzzy Expert System. In Computational Intelligence in Data Mining; Springer: Singapore, 2017; pp. 835–843.
Behera, T.K.; Panigrahi, S. (2015). Credit Card Fraud Detection: A Hybrid Approach Using Fuzzy Clustering & Neural Network. In Proceedings of the 2015 Second International Conference on Advances in Computing and Communication Engineering (ICACCE), Dehradun, India, 1–2; pp. 494–499.
Berry, M.; Linoff, G. (1999). Mastering Data Mining: The Art and Science of Customer Relationship Management; John Wiley & Sons: New York, NY, USA.
Bhasin, M.L. (2015). Menace of frauds in the Indian banking industry: An empirical study. Aust. J. Bus. Manag. Res, 4, 1–13. [CrossRef]
Bhattacharyya, S.; Jha, S.; Tharakunnel, K.; Westland, J.C. (2011). Data mining for credit card fraud: A comparative study. Decis. Support Syst, 50, 602–613. [CrossRef]
Bilal Zoric, A. (2016). Predicting customer churn in banking industry using neural networks. Interdiscip. Descr. Complex Syst, 14, 116–124. [CrossRef]
Breiman, L.; Friedman, J.; Olshen, R.; Stone, C. (1984). Classification and Regression Trees; Belmont: Wadsworth, OH, USA, 1984.
Carminati, M.; Caron, R.; Maggi, F.; Epifani, I.; Zanero, S. (2014). BankSealer: An online banking fraud analysis and decision support system. In Proceedings of the IFIP International Information Security Conference, Marrakech, Morocco, 2–4 June 2014; Springer: Berlin/Heidelberg, Germany, 2014; pp. 380–394.
Chen, Y.; Shi, Y.; Lee, C.F.; Li, M.; Liu, Y. (2014). Measuring and Predicting Systemic Risk in the Chinese Banking System. In Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, China, 14 December 2014; pp. 55–59.
Coumaros, J.; Buvat, J.; Auliard, O.; Roys, S.; Kvj, S.; Chretien, L.; Clerk, V. (2014). Big Data Alchemy: How can banks maximize the value of their customer data. In Banks Have Not Fully Exploited the Potential of Customer Data; Digital Transformation Research Institute and Capgemini Consulting: Paris, France, 2014. 107. Marous, J. Banking Industry Still Taking Small Steps with Big Data. The Financial Brand. 2017. Available online: https://thefinancialbrand.com/64166/banking-big-data-advanced-analytics-ai/ (accessed on 3 September 2017).
Danenas, P.; Garsva, G. (2015). Selection of support vector machines-based classifiers for credit risk domain. Expert Syst. Appl. 2015, 42, 3194–3204. [CrossRef]
Desai, D.B.; Kulkarni, R.V. (2013). A Review: Application of data mining tools in CRM for selected banks. Int. J. Comput. Sci. Inf. Technol, 4, 199–201.
Devadiga, N.; Kothari, H.; Jain, H.; Sankhe, S. (2017). E-Banking Security using Cryptography, Steganography and Data Mining. Int. J. Comput. Appl, 164, 26–30. [CrossRef]
Dreiseitl, S.; Ohno-Machado, L. (2002). Logistic regression and artificial neural network classification models: A methodology review. J. Biomed. Inform, 35, 352–359. [CrossRef]
Elsalamony, H.A. (2017). Bank direct marketing analysis of data mining techniques. Int. J. Comput. Appl. 2014, 85, 12–22. [CrossRef] fuzzy approach. Glob. Financ. J, 35, 58–71. [CrossRef]
Gautam, P.; Singh, Y.P.; Shaikh, P. (2017). Significance and Importance of Data Mining for Marketing Analysis in Finance, Banking Sectors. Int. J. Appl. Res. Sci. Eng, 26–29. Available online:
http://ijarse.org/images/ scripts/201706.pdf (accessed on 9 September 2017).
Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Syst. Appl., 42, 741–750. [CrossRef]
Hasheminejad, S.M.; Salimi, Z. FDiBC: (2018). A Novel Fraud Detection Method in Bank Club based on Sliding Time and Scores Window. J. AI Data Min. 2018, 6, 219–231
Hassani, H.; Huang, X.; Ghodsi, M. (2018). Big Data and Causality. Ann. Data Sci., 5, 133–156. [CrossRef]
Hassani, H.; Huang, X.; Silva, E.S.; Ghodsi, M. (2016). A review of data mining applications in crime. Stat. Anal. Data Min. ASA Data Sci. J., 9, 139–154. [CrossRef]
Hassani, H.; Saporta, G.; Silva, E.S. (2014). Data Mining and Official Statistics: The past, the present and the future. Big Data, 2, 34– [CrossRef] [PubMed]
Hassani, H.; Silva, E.S. Big Data: (2018). a big opportunity for the petroleum and petrochemical industry. OPEC Energy Rev., 42, 74–89. [CrossRef]
He, B.; Shi, Y.; Wan, Q.; Zhao, X. (2014). Prediction of customer attrition of commercial banks based on SVM model. Procedia Comput. Sci., 31, 423–430. [CrossRef]
He, W.; Tian, X.; Shen, J. (2015). Examining Security Risks of Mobile Banking Applications through Blog Mining. In Proceedings of the 26th Modern Artificial Intelligence and Cognitive Science Conference (MAICS), Greensboro, CA, USA, 25–26 April; pp. 103–108.
Hegazy, M.; Madian, A.; Ragaie, M. (2016). Enhanced Fraud Miner: Credit Card Fraud Detection using Clustering Data Mining Techniques. Egypt. Comput. Sci. J., 40, 72–81.
Herrera-Restrepo, O.; Triantis, K.; Seaver, W.L.; Paradi, J.C.; Zhu, H. (2016). Bank branch operational performance: A robust multivariate and clustering approach. Expert Syst. Appl., 50, 107–119. [CrossRef]
Jayasree, V.; Balan, R.S. (2017). Money laundering regulatory risk evaluation using Bitmap Index-based Decision Tree. J. Assoc. Arab Univ. Basic Appl. Sci., 23, 96–102. [CrossRef]
Jayasree, V.; Balan, R.V.S. (2013). A review on data mining in banking sector. Am. J. Appl. Sci., 10, 1160. [CrossRef]
John, S.N.; Anele, C.; Kennedy, O.O.; Olajide, F.; Kennedy, C.G. Realtime fraud detection in the banking sector using data mining techniques/algorithm. In Proceedings of the 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 15–17 December 2016; pp. 1186–1191.
Keramati, A.; Ghaneei, H.; Mirmohammadi, S.M. (2016). Developing a prediction model for customer churn from electronic banking services using data mining. Financ. Innov, 2, 10. [CrossRef]
Kharote, M.; Kshirsagar, V.P. (2014). Data mining model for money laundering detection in financial domain. Int. J. Comput. Appl., 85, 61–64. [CrossRef]
Kirkos, E.; Spathis, C.; Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl, 32, 995–1003. [CrossRef]
Koh, H.C.; Tan, W.C.; Goh, C.P. (2006). A two-step method to construct credit scoring models with data mining techniques. Int. J. Bus. Inf, 1, 96–118.
Koutanaei, F.N.; Sajedi, H.; Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. J. Retail. Consum. Serv., 27, 11–23. [CrossRef]
Lagazio, M.; Sherif, N.; Cushman, M. (2017). A multi-level approach to understanding the impact of cyber-crime on the financial sector. Comput. Secur, 45, 58–74. [CrossRef]
Lahmiri, S. (2017). A two-step system for direct bank telemarketing outcome classification. Intell. Syst. Account. Financ. Manag. 2017, 24, 49–55. [CrossRef] Big Data Cogn. Comput. 2018, 2, 18 12 of 13 65. Shih, J.Y.; Chen, W.H.; Chang, Y.J. Developing target marketing models for personal loans. In Proceedings of the 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bandar Sunway, Malaysia, 9–14 December 2014; pp. 1347–1351.
Langley, P.; Iba,W.; Thompson, K. (1992). An analysis of Bayesian classifiers. In Proceedings of the Tenth National Conference on Artificial Intelligence, San Jose, CA, USA, 12–16; Volume 90, pp. 223–228.
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. Eur. J. Oper. Res, 247, 124–136. [CrossRef]
Li, H.; Zhang, Y.; Zhang, N.; Jia, H. (2016). Detecting the Abnormal Lenders from P2P Lending Data. Procedia Comput. Sci, 91, 357–361. [CrossRef]
Liebana-Cabanillas, F.; Nogueras, R.; Herrera, L.J.; Guillén, A. (2013). Analysing user trust in electronic banking using data mining methods. Expert Syst. Appl, 40, 5439–5447. [CrossRef]
Louzada, F.; Ara, A.; Fernandes, G.B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Surv. Oper. Res. Manag. Sci, 21, 117–134. [CrossRef]
Madyatmadja, E.D.; Aryuni, M. (2014). Comparative study of data mining model for credit card application scoring in bank. J. Theor. Appl. Inf. Technol, 59, 269–274.
Malekpour, M.; Khademi, M.; Minae-Bidgoli, B. (2014). A Hybrid Data Mining Method for Intrusion and Fraud Detection in E-Banking Systems. J. Comput. Intell. Electron. Syst, 3, 1–6.
Mansingh, G.; Rao, L.; Osei-Bryson, K.M.; Mills, A. (2015). Profiling internet banking users: A knowledge discovery in data mining process model-based approach. Inf. Syst. Front, 17, 193–215. [CrossRef]
Marous, J. (2017). Improving the Customer Experience in Banking. Digital Banking Report. Available online: https://www.digitalbankingreport.com/dbr/dbr246/ (accessed on 8 September 2017).
Mayer-Schonberger, V.; Cukier, K. Big Data: (2013). A Revolution that Will Transform How We Live, Work, and Think; Houghton Mifflin Harcourt: New York, NY, USA.
Met, I.; Tunali, G.; Erkoc, A.; Tanrikulu, S.; Dolgun, M.O. (2017). Branch Efficiency and Location Forecasting: Application of Ziraat Bank. J. Appl. Financ. Bank., 7, 1–13.
Mitik, M.; Korkmaz, O.; Karagoz, P.; Toroslu, I.H.; Yucel, F. (2016). Data Mining Based Product Marketing Technique for Banking Products. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain, 12–15 December; pp. 552–559.
Mitik, M.; Korkmaz, O.; Karagoz, P.; Toroslu, I.H.; Yucel, F. (2017). Data Mining Approach for Direct Marketing of Banking Products with Profit/Cost Analysis. Rev. Soc. Strateg, 11, 17–31. [CrossRef]
Moro, S.; Cortez, P.; Rita, P. A data-driven approach to predict the success of bank telemarketing. Decis. Support Syst. 2014, 62, 22–31. [CrossRef]
Negnevitsky, M. (2017). Identification of failing banks using Clustering with self-organising neural networks. Procedia Comput. Sci, 108, 1327–1333. [CrossRef]
Ngai, E.W.; Xiu, L.; Chau, D.C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst. Appl, 36, 2592–2602. [CrossRef] Big Data Cogn. Comput. 2018, 2, 18 13 of 13
Ngai, E.W.T.; Hu, Y.;Wong, Y.H.; Chen, Y.; Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decis. Support Syst. 2011, 50, 559–569. [CrossRef]
Nishanth, K.J.; Ravi, V. (2013). A computational intelligence based online data imputation method: An application for banking. J. Inf. Process. Syst, 9, 633–650. [CrossRef]
Noori, B. (2015). An Analysis of Mobile Banking User Behavior Using Customer Segmentation. Int. J. Glob. Bus, 8, 55.
Ogwueleka, F.N.; Misra, S.; Colomo-Palacios, R.; Fernandez, L. (2015). Neural network and classification approach in identifying customer behavior in the banking sector: A case study of an international bank. Hum. Factors Ergonom. Manuf. Serv. Ind, 25, 28–42. [CrossRef]
Osinski, S.; Stefanowski, J.; Weiss, D. (2004). Lingo: Search results clustering algorithm based on singular value decomposition. In Intelligent Information Processing and Web Mining; Springer: Berlin/Heidelberg, Germany; pp. 359–368.
Oyeniyi, A.O.; Adeyemo, A.B.; Oyeniyi, A.O.; Adeyemo, A.B. (2015). Customer churn analysis in banking sector using data mining techniques. Afr. J. Comput. ICTs, 8, 165–174.
Pang-Ning, T.; Steinbach, M.; Kumar, V. (2017). Introduction to Data Mining (2ed edition); Pearson: Boston, MA, USA.
Panigrahi, S.; Kundu, A.; Sural, S.; Majumdar, A.K. (2009). Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning. Inf. Fusion, 10, 354–363. [CrossRef]
Parvatiyar, A.; Sheth, J.N. (2001). Customer relationship management: Emerging practice, process, and discipline. J. Econ. Soc. Res, 3, 1–34.
Patel, Y.S.; Agrawal, D.; Josyula, L.S. (2016). The RFM-based ubiquitous framework for secure and efficient banking. In Proceedings of the 2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), Noida, India, 3–5 February; pp. 283–288.
Pulakkazhy, S.; Balan, R.V.S. (2013). Data mining in banking and its applications—A review. J. Comput. Sci, 9, 1252–1259. [CrossRef]
Quinlan, J.R. (1992). C4.5: Program for Machine Learning; Morgan Kaufmann: Burlington, MA, USA.
Save, P.; Tiwarekar, P.; Jain, K.N.; Mahyavanshi, N. (2017). A Novel Idea for Credit Card Fraud Detection using Decision Tree. Int. J. Comput. Appl, 161, 6–9. [CrossRef]
Seeja, K.R.; Zareapoor, M. FraudMiner: (2018). A novel credit card fraud detection model based on frequent itemset mining. Sci. World J. 2014, 2014. [CrossRef] [PubMed] Big Data Cogn. Comput, 2, 18 10 of 13
Serrano-Cinca, C.; Gutiérrez-Nieto, B. (2016). The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending. Decis. Support Syst, 89, 113–122. [CrossRef]
Siami, M.; Hajimohammadi, Z. (2013). Credit scoring in banks and financial institutions via data mining techniques: A literature review. J. AI Data Min, 1, 119–129.
Soltani, Z.; Navimipour, N.J. (2016). Customer relationship management mechanisms: A systematic review of the state-of-the-art literature and recommendations for future research. Comput. Hum. Behav, 61, 667–688. [CrossRef]
Sousa, M.D.M.; Figueiredo, R.S. (2014). Credit analysis using data mining: Application in the case of a credit union. J. Inf. Syst. Technol. Manag, 11, 379–396. [CrossRef]
Sun, N.; Morris, J.G.; Xu, J.; Zhu, X.; Xie, M. (2014). iCARE: A framework for big data-based banking customer analytics. IBM J. Res. Dev, 58, 4:1–4:9. [CrossRef]
Sundarkumar, G.G.; Ravi, V. (2015). A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance. Eng. Appl. Artif. Intell, 37, 368–377. [CrossRef]
Suvarna, V.K.; Banerjee, B. (2014). Social Banking: Leveraging social media to Enhance Customer Engagement; Capgemini White Paper; Capgemini: Paris, France.
Suykens, J.A.; Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Process. Lett, 9, 293–300. [CrossRef]
Vajiramedhin, C.; Suebsing, A. (2014). Feature selection with data balancing for prediction of bank telemarketing. Appl. Math. Sci, 8, 5667–5672. [CrossRef]
Van Vlasselaer, V.; Bravo, C.; Caelen, O.; Eliassi-Rad, T.; Akoglu, L.; Snoeck, M.; Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst, 75, 38–48. [CrossRef]
Wang, S.; Petrounias, I. (2017). Big Data Analysis on Demographic Characteristics of Chinese Mobile Banking Users. In Proceedings of the 2017 IEEE 19th Conference on Business Informatics (CBI), Thessaloniki, Greece, 24–27 July; Volume 2, pp. 47–54.
Wanke, P.; Azad, A.K.; Emrouznejad, A. (2016). Efficiency in BRICS banking under data vagueness: A two-stage fuzzy approach. Glob. Financ. J, 35, 58–71. [CrossRef]
Wanke, P.; Azad, M.A.K.; Barros, C.P.; Hassan, M.K. (2016). Predicting efficiency in Islamic banks: An integrated multicriteria decision making (MCDM) approach. J. Int. Financ. Market. Inst. Money, 45, 126–141. [CrossRef]
Wanke, P.; Kalam Azad, M.; Barros, C.P.; Hadi-Vencheh, A. (2016). Predicting performance in ASEAN banks: An integrated fuzzy MCDM–neural network approach. Expert Syst, 33, 213–229. [CrossRef]
Wei, W.; Li, J.; Cao, L.; Ou, Y.; Chen, J. (2013). Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web, 16, 449–475. [CrossRef]
Widrow, B.; Rumelhart, D.E.; Lehr, M.A. (1994). Neural networks: Applications in industry, business and science. Commun. ACM, 37, 93–105. [CrossRef]
Wongchinsri, P.; Kuratach,W. (2016). A survey-data mining frameworks in credit card processing. In Proceedings of the 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), ChiangMai, Thailand, 28 June–1 July; pp. 1–6.
Wu, D.D.; Olson, D.L.; Luo, C. (2014). A decision support approach for accounts receivable risk management. IEEE Trans. Syst. Man Cybern. Syst, 44, 1624–1632. [CrossRef]
Xia, Y.; Liu, C.; Li, Y.; Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl, 78, 225–241. [CrossRef]
Xiong, T.; Wang, S.; Mayers, A.; Monga, E. (2013). Personal bankruptcy prediction by mining credit card data. Expert Syst. Appl, 40, 665–676. [CrossRef]
Xu, D.; Tian, Y. (2015). A comprehensive survey of clustering algorithms. Ann. Data Sci, 2, 165–193. [CrossRef]
Zakaryazad, A.; Duman, E. (2016). A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing. Neurocomputing, 175, 121–131. [CrossRef]
Zareapoor, M.; Shamsolmoali, P. (2015). Application of credit card fraud detection: Based on bagging ensemble classifier. Procedia Comput. Sci, 48, 679–685. [CrossRef]
Zhao, Z.; Xu, S.; Kang, B.H.; Kabir, M.M.J.; Liu, Y.; Wasinger, R. (2008). Investigation and improvement of multi-layer perceptron neural networks for credit scoring. Expert Syst. Appl, 42, 3508–3516. [CrossRef] Big
)