Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding

The Temporal Contrastive Transformer (TCT) is a new representation learning framework designed for financial transaction sequences to detect fraud. It uses self-supervised contrastive learning to generate embeddings that capture temporal behavioral patterns, showing meaningful predictive performance, especially when combined with domain-engineered features.