Abstract
Customer segmentation is an essential process that leads a bank to gain more insight and better understand their customers. In the past, this process requires analyses of data, both customer demographic and offline financial transactions. However, from the advancement of mobile technology, mobile banking has become more accessible than before. With over 10 million digital users, SCB easy app by Siam Commercial Bank receives an enormous volume of transactions each day. In this work, we propose a method to classify mobile user’s click behaviour into two groups, i.e. ‘SME-like’ and ‘Non-SME-like’ users. Thus, the bank can easily identify the customers and offer them the right products. We convert a user’s click log into an image that aims to capture temporal information. The image representation reduces the need for feature engineering. Employing ResNet-18 with our image data can achieve 71.69% average accuracy. Clearly, the proposed method outperforms the conventional machine learning technique with hand-crafted features that can achieve 61.70% average accuracy. Also, we discover a hidden insight behind ‘SME-like’ and ‘Non-SME-like’ user’s click behaviour from these images. Our proposed method can lead to a better understanding of mobile banking user behaviour and a novel way of developing a customer segmentation classifier.
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Tungjitnob, S., Pasupa, K., Thamwiwatthana, E., Suntisrivaraporn, B. (2020). SME User Classification from Click Feedback on a Mobile Banking Apps. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_29
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DOI: https://doi.org/10.1007/978-3-030-63820-7_29
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