Identifying SME customers from click feedback on mobile banking apps: Supervised and semi-supervised approaches

Abstract

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.