Credit Card Customer Profiling Using Self-Supervised Representation Learning on Multi-Source Financial Data

The recent improvement of machine learning and data integration has marked new heights in financial analytics. A major area here that has become very popular is credit card customer profiling, which aims to identify and classify the behavior, preferences and risks of the customer. Traditional methods depend largely on supervised learning, which has to be based on large labeled data points. Nonetheless, since the emergence of Self-Supervised Learning (SSL), it is possible to derive meaningful representations by selecting unlabeled and heterogeneous data sources. This paper presents an original scheme for credit card customer profiling based on self-supervised representation learning and financial data from multiple sources. The transaction records, customer demographics, online banking activity and credit scores are brought together through a single analytical model. The method builds into contrastive learning and transformer-based architectures to learn feature embeddings that are robust. We show high-quality profiling, clustering, and downstream tasks like creditworthiness assessment problems and churn prediction on a real-world financial dataset that was collected prior to February 2025 and made up a significant bulk of our experiments, stating that our framework essentially outperforms baselines in terms of profiling accuracy, clustering performance, and downstream tasks accurately. We elaborate on the comparisons of performance to standard models, the advantages of multiple-source merging, as well as what this could mean to the individual tailored financial services. We have also added thorough visualization, flowcharts and ablation studies to complement our findings