The proposed framework can be extended to handle more complex external contexts by incorporating additional layers of abstraction and feature engineering. For macroeconomic indicators, the model can be designed to extract relevant features from economic data sources and integrate them into the representation learning process. This can involve creating specialized modules within the model architecture to process and incorporate macroeconomic indicators into the client representations. Additionally, for social network data, the framework can be extended to include graph neural networks or attention mechanisms that can capture the relationships and interactions between clients in a social network. By incorporating these external contexts, the model can generate more comprehensive and informative representations that account for a wider range of factors influencing financial transactions.