User Profiling Based on Financial Transaction Patterns: A Clustering Approach for User Segmentation

Abstract

User profiling based on financial transaction patterns is crucial for improving customer segmentation and personalizing financial services. This study uses clustering techniques, specifically K-means, to analyze transaction data and segment users based on transaction amounts, times, and types. Three clusters were identified, each demonstrating distinct transaction behaviors: Cluster 0, primarily focused on purchases and occurring early in the week; Cluster 1, which emphasizes transfers and higher transaction amounts, typically occurring mid-week; and Cluster 2, similar to Cluster 0 but with a preference for later-week transactions. The analysis demonstrates that transaction patterns, including amount, time, and type, provide valuable insights for targeting specific user groups with personalized marketing strategies and financial products. The study also highlights the importance of improving clustering accuracy, as indicated by the moderate Silhouette Score of 0.33, suggesting that further refinement in the clustering methodology could lead to more distinct user segments. The findings of this study emphasize the potential for clustering techniques to enhance user profiling, ultimately improving business strategies, customer satisfaction, and loyalty. Limitations of the study, including the dataset’s single-month duration, suggest that further research incorporating larger and more diverse datasets, as well as alternative clustering techniques, could offer deeper insights into user behavior and refine segmentation strategies.