Research Article
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Enriching Customer Relationship Management with New Attributes and Machine Learning Methods in Banking Sector

Year 2019, Volume , Issue 16, 382 - 394, 31.08.2019
https://doi.org/10.31590/ejosat.520295

Abstract

A bank may have difficulty in acquiring detailed personal information from their clients. Over time, however, customers share a variety of information about their behavioral characteristics and life habits with the bank. This information for people can be hundreds or even thousands. It can reach billions of information in certain categories of all customers of a bank with millions of individual and corporate clients. Large organizations with millions of customers are generally referred to as “rich in data rich and lack in knowledge” in the field of Information Technology. This unprocessed data might contain many valuable information that is hidden from customers and the market. There might be a lot of valuable hidden information about both customers and market. Data Science essentially aims to extract this confidential information from raw data. In this study, suggestions have been made about the different vital and behavioral habits of institutional and individual customers which can be converted into numerical data. With this data, the structure of CRM (Customer Relationship Management), an artificial intelligence-based system which aims to better analyze the market, better customer segmentation, and offer services and products with less labor to the right customers, is recommended. In this project, it is aimed to propose an original artificial support model by using algorithms in Data Mining disciplines. With the identification of different data mining applications and processes, we have tried to give a new perspective to the field of banking and customer relationship management. 

References

  • Amani, F. A., & Fadlalla, A. M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32-58.
  • Bhat, S. A., & Darzi, M. A. (2016). Customer relationship management: An approach to competitive advantage in the banking sector by exploring the mediational role of loyalty. International Journal of Bank Marketing, 34(3), 388-410.
  • Bahari, T. F., & Elayidom, M. S. (2015). An efficient CRM-data mining framework for the prediction of customer behaviour. Procedia Computer Science, 46, 725-731.
  • Bhambri, V. (2011). Application of data mining in banking sector. IJCST, 2(2), 199-202.
  • Bulut, F., & Amasyali, M. F. (2017). Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster. Pattern Analysis and Applications, 20(2), 415-425.
  • Bulut, F. (2017). Örnek tabanlı sınıflandırıcı topluluklarıyla yeni bir klinik karar destek sistemi. Journal of the Faculty of Engineering and Architecture of Gazi University, 32(1), 65-76.
  • Chopra, B., Bhambri, V., & Krishan, B. (2011). Implementation of data mining techniques for strategic CRM issues. Int. J. Comput. Technol. Appli, 2, 879-883.
  • Ellaban, Mahmoud Ayesh Abu, (2013), The Role of Data Mining Technology in Building Marketing and Customer Relationship Management (CRM) for Telecommunication Industry, Islamic University, BusinessAdministration.
  • Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F. (2018). Imbalanced Classification for Big Data. In Learning from Imbalanced Data Sets (pp. 327-349). Springer, Cham.
  • Hasan, A. A. T. (2018). Customer Relationship Management (CRM) Practices of City Bank in Customer Retention Perspective in Bangladesh. Global Journal of Management And Business Research.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Ho, C. T. B., & Wu, D. D. (2009). Online banking performance evaluation using data envelopment analysis and principal component analysis. Computers & Operations Research, 36(6), 1835-1842.
  • Kasemsap, K. (2018). Facilitating customer relationship management in modern business. In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 1594-1604). IGI Global.
  • John, S. N., Anele, C., Kennedy, O. O., Olajide, F., & Kennedy, C. G. (2016, December). Realtime Fraud Detection in the Banking Sector Using Data Mining Techniques/Algorithm. In Computational Science and Computational Intelligence (CSCI), 2016 International Conference on (pp. 1186-1191). IEEE.
  • Jayasree, V., & Balan, R. V. S. (2013). A review on data mining in banking sector. American Journal of Applied Sciences, 10(10), 1160.
  • Mitik, M., Korkmaz, O., Karagoz, P., Toroslu, I. H., & Yucel, F. (2016, December). Data Mining Based Product Marketing Technique for Banking Products. In Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on (pp. 552-559). IEEE.
  • Moro, S., Laureano, R., & Cortez, P. (2011). Using data mining for bank direct marketing: An application of the crisp-dm methodology. In Proceedings of European Simulation and Modelling Conference-ESM'2011 (pp. 117-121). EUROSIS-ETI.
  • Narang, S. K., Kumar, S., & Verma, V. (2017). Knowledge discovery from massive data streams. In Web semantics for textual and visual information retrieval (pp. 109-143). IGI Global.
  • Nguyen, N. T., Hoang, D. H., Hong, T. P., Pham, H., & Trawiński, B. (Eds.). (2018). Intelligent Information and Database Systems: 10th Asian Conference, ACIIDS 2018, Dong Hoi City, Vietnam, March 19-21, 2018, Proceedings (Vol. 10751). Springer.
  • Oliveira, V. L. M. (2012). Analytical customer relationship management in retailing supported by data mining techniques.
  • Ren, S., Sun, Q., & Shi, Y. (2010, April). Customer segmentation of bank based on data warehouse and data mining. In Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on (pp. 349-353). IEEE.
  • Sharahi, M., & Aligholi, M. (2015). Classify the data of bank customers using data mining and clustering techniques (Case study: Sepah bank branches Tehran). Journal of Applied Environmental and Biological Sciences, 5(5), 458-464.
  • Tam, P. T., & Van Thuy, M. B. (2017). THE INDUSTRY 4.0 FACTOR AFFECTING THE SERVICE QUALITY OF COMMERCIAL BANKS IN DONG NAI PROVINCE. European Journal of Accounting Auditing and Finance Research, 5(9), 81-91.
  • STATISTICA Data Miner, (2019). A Trademark for industries, URL: www.statsoft.com, USA.
  • Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmentation. John Wiley & Sons.
  • Zerbino, P., Aloini, D., Dulmin, R., & Mininno, V. (2018). Big Data-enabled customer relationship management: A holistic approach. Information Processing & Management, 54(5), 818-846.

Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi

Year 2019, Volume , Issue 16, 382 - 394, 31.08.2019
https://doi.org/10.31590/ejosat.520295

Abstract

Bir banka müşterilerinden kendilerini tanımlayıcı detaylı kişisel verileri kolaylıkla alamayabilir. Fakat müşteriler zaman içerisinde, davranışsal özellikleri ve yaşamsal alışkanlıkları ile ilgili birçok bilgiyi farkında olmadan banka ile paylaşırlar. Bu bilgiler sadece bir birey için yüzlerce, hatta binlerce olabilir. Milyonlarca bireysel ve kurumsal müşterisi olan bir bankanın tüm müşterilerine ait belirli kategorilerdeki bilgileri milyarlara ulaşabilir. Milyonlarca müşterisi olan büyük kuruluşlar genel olarak Bilgi Teknolojileri alanında “veri zengini ve bilgi yoksunu” olarak nitelendirilirler. Bu işlenmemiş veriler içerisine müşteriler ve piyasa hakkında gizlenmiş birbirinden farklı oldukça fazla değerli bilgiler bulunabilmektedir. Veri Bilimi esasen bu gizli bilgileri ham veriler içerisinden çıkarmayı amaçlar. Bu çalışmada, kurumsal ve bireysel müşterilerin sayısal verilere dönüştürülebilen farklı yaşamsal ve davranışsal alışkanlıklarının neler olabileceği ile ilgili önerilerde bulunulmuştur. Bu veriler ile piyasanın daha iyi analiz edilmesi, müşteri segmentasyonunun daha iyi yapılması, hizmet ve ürünlerin doğru müşteri kitlelerine daha az emek ile satılmasını amaçlayan yapay zekâ tabanlı bir sunulan Müşteri İlişkileri Yönetimi yani CRM (Customer Relationship Management) uygulamasının yapısı önerilmektedir. Projede bilişim alanındaki Veri Madenciliği disiplinlerinde bulunan algoritmalar kullanılarak özgün bir yapay destek modelinin önerilmesi amaçlanmaktadır. Farklı veri madenciliği uygulamaları ve süreçlerin tanımlanmasıyla bankacılık ve müşteri ilişkileri yönetimi alanına yeni bir perspektif kazandırılmaya çalışılmıştır. 

References

  • Amani, F. A., & Fadlalla, A. M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32-58.
  • Bhat, S. A., & Darzi, M. A. (2016). Customer relationship management: An approach to competitive advantage in the banking sector by exploring the mediational role of loyalty. International Journal of Bank Marketing, 34(3), 388-410.
  • Bahari, T. F., & Elayidom, M. S. (2015). An efficient CRM-data mining framework for the prediction of customer behaviour. Procedia Computer Science, 46, 725-731.
  • Bhambri, V. (2011). Application of data mining in banking sector. IJCST, 2(2), 199-202.
  • Bulut, F., & Amasyali, M. F. (2017). Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster. Pattern Analysis and Applications, 20(2), 415-425.
  • Bulut, F. (2017). Örnek tabanlı sınıflandırıcı topluluklarıyla yeni bir klinik karar destek sistemi. Journal of the Faculty of Engineering and Architecture of Gazi University, 32(1), 65-76.
  • Chopra, B., Bhambri, V., & Krishan, B. (2011). Implementation of data mining techniques for strategic CRM issues. Int. J. Comput. Technol. Appli, 2, 879-883.
  • Ellaban, Mahmoud Ayesh Abu, (2013), The Role of Data Mining Technology in Building Marketing and Customer Relationship Management (CRM) for Telecommunication Industry, Islamic University, BusinessAdministration.
  • Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., & Herrera, F. (2018). Imbalanced Classification for Big Data. In Learning from Imbalanced Data Sets (pp. 327-349). Springer, Cham.
  • Hasan, A. A. T. (2018). Customer Relationship Management (CRM) Practices of City Bank in Customer Retention Perspective in Bangladesh. Global Journal of Management And Business Research.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Ho, C. T. B., & Wu, D. D. (2009). Online banking performance evaluation using data envelopment analysis and principal component analysis. Computers & Operations Research, 36(6), 1835-1842.
  • Kasemsap, K. (2018). Facilitating customer relationship management in modern business. In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 1594-1604). IGI Global.
  • John, S. N., Anele, C., Kennedy, O. O., Olajide, F., & Kennedy, C. G. (2016, December). Realtime Fraud Detection in the Banking Sector Using Data Mining Techniques/Algorithm. In Computational Science and Computational Intelligence (CSCI), 2016 International Conference on (pp. 1186-1191). IEEE.
  • Jayasree, V., & Balan, R. V. S. (2013). A review on data mining in banking sector. American Journal of Applied Sciences, 10(10), 1160.
  • Mitik, M., Korkmaz, O., Karagoz, P., Toroslu, I. H., & Yucel, F. (2016, December). Data Mining Based Product Marketing Technique for Banking Products. In Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on (pp. 552-559). IEEE.
  • Moro, S., Laureano, R., & Cortez, P. (2011). Using data mining for bank direct marketing: An application of the crisp-dm methodology. In Proceedings of European Simulation and Modelling Conference-ESM'2011 (pp. 117-121). EUROSIS-ETI.
  • Narang, S. K., Kumar, S., & Verma, V. (2017). Knowledge discovery from massive data streams. In Web semantics for textual and visual information retrieval (pp. 109-143). IGI Global.
  • Nguyen, N. T., Hoang, D. H., Hong, T. P., Pham, H., & Trawiński, B. (Eds.). (2018). Intelligent Information and Database Systems: 10th Asian Conference, ACIIDS 2018, Dong Hoi City, Vietnam, March 19-21, 2018, Proceedings (Vol. 10751). Springer.
  • Oliveira, V. L. M. (2012). Analytical customer relationship management in retailing supported by data mining techniques.
  • Ren, S., Sun, Q., & Shi, Y. (2010, April). Customer segmentation of bank based on data warehouse and data mining. In Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on (pp. 349-353). IEEE.
  • Sharahi, M., & Aligholi, M. (2015). Classify the data of bank customers using data mining and clustering techniques (Case study: Sepah bank branches Tehran). Journal of Applied Environmental and Biological Sciences, 5(5), 458-464.
  • Tam, P. T., & Van Thuy, M. B. (2017). THE INDUSTRY 4.0 FACTOR AFFECTING THE SERVICE QUALITY OF COMMERCIAL BANKS IN DONG NAI PROVINCE. European Journal of Accounting Auditing and Finance Research, 5(9), 81-91.
  • STATISTICA Data Miner, (2019). A Trademark for industries, URL: www.statsoft.com, USA.
  • Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmentation. John Wiley & Sons.
  • Zerbino, P., Aloini, D., Dulmin, R., & Mininno, V. (2018). Big Data-enabled customer relationship management: A holistic approach. Information Processing & Management, 54(5), 818-846.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Faruk BULUT>

0000-0003-2960-8725
Türkiye

Publication Date August 31, 2019
Published in Issue Year 2019, Volume , Issue 16

Cite

Bibtex @research article { ejosat520295, journal = {Avrupa Bilim ve Teknoloji Dergisi}, issn = {}, eissn = {2148-2683}, address = {}, publisher = {Osman SAĞDIÇ}, year = {2019}, pages = {382 - 394}, doi = {10.31590/ejosat.520295}, title = {Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi}, key = {cite}, author = {Bulut, Faruk} }
APA Bulut, F. (2019). Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi . Avrupa Bilim ve Teknoloji Dergisi , (16) , 382-394 . DOI: 10.31590/ejosat.520295
MLA Bulut, F. "Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi" . Avrupa Bilim ve Teknoloji Dergisi (2019 ): 382-394 <https://dergipark.org.tr/en/pub/ejosat/article/520295>
Chicago Bulut, F. "Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi". Avrupa Bilim ve Teknoloji Dergisi (2019 ): 382-394
RIS TY - JOUR T1 - Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi AU - Faruk Bulut Y1 - 2019 PY - 2019 N1 - doi: 10.31590/ejosat.520295 DO - 10.31590/ejosat.520295 T2 - Avrupa Bilim ve Teknoloji Dergisi JF - Journal JO - JOR SP - 382 EP - 394 VL - IS - 16 SN - -2148-2683 M3 - doi: 10.31590/ejosat.520295 UR - https://doi.org/10.31590/ejosat.520295 Y2 - 2019 ER -
EndNote %0 Avrupa Bilim ve Teknoloji Dergisi Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi %A Faruk Bulut %T Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi %D 2019 %J Avrupa Bilim ve Teknoloji Dergisi %P -2148-2683 %V %N 16 %R doi: 10.31590/ejosat.520295 %U 10.31590/ejosat.520295
ISNAD Bulut, Faruk . "Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi". Avrupa Bilim ve Teknoloji Dergisi / 16 (August 2019): 382-394 . https://doi.org/10.31590/ejosat.520295
AMA Bulut F. Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi. EJOSAT. 2019; (16): 382-394.
Vancouver Bulut F. Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi. Avrupa Bilim ve Teknoloji Dergisi. 2019; (16): 382-394.
IEEE F. Bulut , "Bankacılık Sektöründe Makine Öğrenmesi Yöntemleriyle Müşteri İlişkileri Yönetiminin Zenginleştirilmesi", Avrupa Bilim ve Teknoloji Dergisi, no. 16, pp. 382-394, Aug. 2019, doi:10.31590/ejosat.520295