Tungjitnob Suchat, Pasupa Kitsuchart, Suntisrivaraporn Boontawee
Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok 10520, Thailand.
Data Analytics, Chief Data Office, Siam Commercial Bank, Bangkok 10900, Thailand.
Heliyon. 2021 Aug 16;7(8):e07761. doi: 10.1016/j.heliyon.2021.e07761. eCollection 2021 Aug.
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, 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.
如今，银行业已从传统的分行服务转向移动银行应用程序或应用。通过客户细分，银行可以获得更多见解，更好地了解客户的生活方式和行为。在这项工作中，我们描述了一种将移动应用用户点击行为分为中小企业用户和非中小企业用户两组的方法。这项任务使银行能够识别匿名用户，并为他们提供合适的服务和产品。我们从点击日志数据中提取手工制作的特征，并用极端梯度提升算法（XGBoost）对其进行评估。我们还将这些日志转换为图像，这些图像捕获了时间信息。这些图像表示减少了对特征工程的需求，更易于可视化，并使用卷积神经网络（CNN）进行训练。我们将ResNet-18与图像数据集一起使用，平均准确率达到71.69%，优于仅达到61.70%准确率的XGBoost。我们还使用转换后的图像数据评估了一个半监督学习模型。我们的半监督方法仅使用一半的标记图像并结合未标记图像，就达到了73.12%的准确率。我们的方法表明，这些转换后的图像能够使用半监督算法进行训练，该算法在标记图像较少的情况下比CNN表现更好。我们的工作还使人们对移动银行用户行为有了更好的理解，并开创了一种开发客户细分分类器的新方法。
