ResPoNet: A Residual Neural Network for Efficient Valuation of Large Variable Annuity Portfolios
Bibliographic record
Abstract
Accurately valuing large portfolios of Variable Annuities (VAs) poses a significant challenge due to the high computational burden of Monte Carlo simulations and the limitations of spatial interpolation methods that rely on manually defined distance metrics. We introduce a residual portfolio valuation network (ResPoNet), a novel residual neural network architecture enhanced with weighted loss functions, designed to improve valuation accuracy and scalability. ResPoNet systematically accounts for mortality risk and path-dependent liabilities using residual layers, while the custom loss function ensures better convergence and interpretability. Numerical results on synthetic portfolios of 100,000 contracts show that ResPoNet achieves significantly lower valuation errors than baseline neural and spatial methods, with faster convergence and improved generalization. Sensitivity analysis reveals key drivers of performance, including guarantee complexity and contract maturity, demonstrating the robustness and practical applicability of ResPoNet in large-scale VA valuation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".