Neural network and classification approach in identifying customer behavior in the banking sector: A case study of an international bank

Abstract

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.

Digitalisation and Big Data Mining in Banking

“…Customer retention and acquisition [69][70][71][72][73][74][75] classification (DT, NN, LR, SVM), ARM, k-mean clustering EU [69], China [70], Nigeria [71], Croatia [73], Bangladesh [75] Customer churn prediction and prevention, attracting potential customers and strategic future service design.…”

“…Data relating to private banking customers in a European bank was analyzed in [69]; He et al [70] employed the SVM technique for customer churn and attrition prediction with a real life data set from a Chinese commercial bank; customer records of a major bank in Nigeria were investigated in [71]; later, in [72] a NN classification technique was applied on the customer database of an international bank for customer churn prediction; similar research was also conducted in a small Croatian bank in [73] and the electronic banking service data set in [74].…”

Capacity of Neural Networks and Discriminant Analysis in Classifying Potential Debtors

“…can be found in the works of e.g. Khemakhem and Boujelbènea (2015), Karaa and Krichene (2012), Huang et al (2004), Oreski et al (2012), Ogwueleka et al (2015), Linder et al (2004) …”

Neural Networks vs Discriminant Analysis in the Assessment of Default

“…), can be found in the works of e.g. Khemakhem and Boujelbènea [2015], Karaa and Krichene [2012], Huang, Chen and Hsu [2004], Oreski, Oreski and Oreski [2012], Ogwueleka et al [2015], Linder, Geier and Kölliker [2004] and many others.…”