Hyperparameters over Architecture: A Controlled Comparison of Neural Networks for Aggregate Loss Reserving

We compare neural network architectures for aggregate loss triangle reserving under matched data, training, and evaluation protocols, separating architectural choice from hyperparameter configuration across hundreds of training runs. We compare the GRU Baseline of the DeepTriangle framework against two attention-augmented variants on Workers’ Compensation and Private Passenger Auto data from NAIC Schedule P, and we report two findings. First, attention does not improve reserving accuracy on these short triangles. Both attention variants exhibit a bimodal training instability we call “attention collapse”, in which a sizable fraction of seeds degenerate to a naive mean predictor and the remaining seeds do not reliably outperform the Baseline once the full seed distribution and post hoc survivor filters are considered. Adding or removing the padding mask changes the failure rate only modestly, indicating that the issue is not a masking bug but the limited cross-position information available in 9-lag aggregate triangles. Second, hyperparameter configuration is the dominant driver of accuracy: learning rate receives the largest share of impurity-based importance (about 46%) in a Random Forest decomposition, and tuning the GRU Baseline yields a larger gain over Chain–Ladder than any architectural variant tested. The tuned GRU Baseline remains the recommended starting point for aggregate triangle reserving.