This paper presents an innovative approach to the early detection of expensive insurance claims by leveraging survival analysis concepts within a deep learning framework exploiting textual information from claims notes. Our proposed SMARTR model addresses limitations of state-of-the-art models, such as handling data-label mismatches and non-uniform data frequency, to enhance a posteriori classification and early detection. Our results suggest that incorporating temporal dynamics and empty period representation improves model performance, highlighting the importance of considering time in insurance claim analysis. The approach appears promising for application to other insurance datasets.