NVIDIA has released an open-source framework for constructing transaction foundation models that convert raw financial data into high-value behavioral representations. As institutions adopt transformer architectures to decode sequential payment patterns, this pipeline accelerates model development using NVIDIA CUDA-X and NeMo AutoModel libraries. The initiative addresses longstanding limitations in tabular financial processing, where rule-based systems and manual feature engineering fail to capture complex customer histories. The workflow begins with GPU-accelerated data ingestion via cuDF, loading large transaction datasets directly into memory. Because general-purpose language models inefficiently encode numerical banking fields, the framework employs a custom domain tokenizer. This optimization reduces token output per transaction from thirty-nine to twelve, shrinking vocabulary size by over ninety percent and tripling context window capacity. The modular tokenizer processes amount binning, merchant hashing, temporal markers, and geographic identifiers entirely on the GPU. Pretraining leverages NeMo AutoModel to configure a decoder-only transformer trained via causal language modeling. By predicting subsequent financial events across packed sequences, the model absorbs dense behavioral gradients without labeled supervision. The training stack utilizes FSDP2 sharding, mixed precision, and automated checkpointing, enabling seamless scaling from single-GPU validation to multi-node clusters. Trained checkpoints export as standard safetensors, ensuring broad inference compatibility. During inference, the pretrained backbone operates as a feature extractor. By pooling the final hidden state at the sequence terminus, the pipeline generates compact embeddings that capture longitudinal customer behavior. Validation projections confirm these representations naturally cluster by merchant category and location despite zero exposure to target labels during training. Operational impact emerges in downstream applications. Integrated with an XGBoost fraud detector, the foundation embeddings yield a 41.76 percent increase in Average Precision and a 0.41 percent improvement in ROC-AUC compared to raw-feature baselines. Combining foundational embeddings with traditional tabular features outperforms either approach independently, proving that sequence-level context and event-level attributes deliver complementary predictive signals. Evaluated under strict temporal splits that prevent data leakage, the combined model maintains high precision across varying fraud thresholds. NVIDIA engineered the architecture for immediate industry adaptation. Financial organizations can modify the tokenizer to accommodate device fingerprints or alternative schema without rewriting core logic. The training configuration supports any HuggingFace-compatible decoder, while downstream pipelines can adapt the embedding pattern to churn prediction, credit scoring, or customer segmentation. Deployable through NVIDIA Launchable or GitHub environments, the framework provides a production-ready pathway for institutions operationalizing transaction foundation models. As payment networks continue launching proprietary variants, this open benchmark establishes a standardized route for next-generation financial intelligence systems.