A. Ignoring transactions with missing data to simplify the model's training process.

B. Imputing missing data in each modality independently using modality-specific imputation techniques (e.g., mean imputation for numerical data, most frequent category for categorical data).

C. Employing a joint imputation approach that leverages information from available modalities to predict and fill in missing values in other modalities.

D. Replacing missing data with a single, arbitrary placeholder value (e.g., -1 for numerical data, 'missing' for text) across all modalities.

E. Using a modality dropout technique during training, randomly masking modalities to force the model to learn robust representations from incomplete data.