The transformation of digital banking has fundamentally altered the way creditworthiness is assessed, making traditional static and statistical approaches increasingly insufficient to capture the complexity of customer financial behavior. This study proposes a novel framework called “Collective Financial Memory” in digital banking, which reconstructs credit decision-making based on the interaction history of customers rather than classical statistical data.In this framework, traditional credit indicators such as income level, loan history, and static credit scores are replaced by dynamic behavioral interaction data collected from digital banking environments. These include micro-transaction patterns, service usage behavior, responses to financial recommendations, and system interaction logs. These data are stored and processed within a collective memory structure, where each customer is represented not as a static entity but as a temporal sequence of financial interactions.The proposed model leverages sequential learning principles and memory-based architectures to dynamically reconstruct credit decisions. By capturing long-term behavioral evolution and interaction trajectories, the system enables more accurate prediction of future credit risk. The conceptual findings indicate that the collective financial memory approach significantly improves credit assessment accuracy compared to traditional models while reducing biases caused by incomplete or static datasets.The main innovation of this study lies in replacing the concept of “instantaneous credit scoring” with “accumulated behavioral memory,” thereby transforming credit evaluation into a continuously learning and adaptive system. This framework can serve as a foundational architecture for next-generation intelligent credit decision systems in digital banking environments.