Pasupa, Kitsuchart

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Pasupa, Kitsuchart
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kitsuchart.pa@kmitl.ac.th

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    Item type:Publication,
    Identifying SME customers from click feedback on mobile banking apps: Supervised and semi-supervised approaches
    (2021-08-01)
    Tungjitnob, Suchat
    ;
    Pasupa, Kitsuchart
    ;
    Suntisrivaraporn, Boontawee
    Nowadays, the banking industry has moved from traditional branch services into mobile banking applications or apps. Using customer segmentation, banks can obtain more insights and better understand their customers' lifestyle and their behavior. In this work, we described a method to classify mobile app user click behavior into two groups, i.e. SME and Non-SME users. This task enabled the bank to identify anonymous users and offer them the right services and products. We extracted hand-crafted features from click log data and evaluated them with the Extreme Gradient Boosting algorithm (XGBoost). We also converted these logs into images, which captured temporal information. These image representations reduced the need for feature engineering, were easier to visualize and trained with a Convolutional Neural Network (CNN). We used ResNet-18 with the image dataset and achieved 71.69% accuracy on average, which outperformed XGBoost, which only achieved 61.70% accuracy. We also evaluated a semi-supervised learning model with our converted image data. Our semi-supervised method achieved 73.12% accuracy, using just half of the labeled images, combined with unlabeled images. Our method showed that these converted images were able to train with a semi-supervised algorithm that performed better than CNN with fewer labeled images. Our work also led to a better understanding of mobile banking user behavior and a novel way of developing a customer segmentation classifier.
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    Item type:Publication,
    SME User Classification from Click Feedback on a Mobile Banking Apps
    (2020-01-01)
    Tungjitnob, Suchat
    ;
    Pasupa, Kitsuchart
    ;
    Thamwiwatthana, Ek
    ;
    Suntisrivaraporn, Boontawee
    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.