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An Interpretable Deep Learning Model for General Insurance Pricing

arXiv 2509.08467 ↗First posted 10 Sep 2025cs.LG q-fin.GNcited by 0

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Abstract (arXiv)

This paper introduces the Actuarial Neural Additive Model, an inherently interpretable deep learning model for general insurance pricing that offers fully transparent and interpretable results while retaining the strong predictive power of neural networks. This model assigns a dedicated neural network (or subnetwork) to each individual covariate and pairwise interaction term to independently learn its impact on the modeled output while implementing various architectural constraints to allow for essential interpretability (e.g. sparsity) and practical requirements (e.g. smoothness, monotonicity) in insurance applications. The development of our model is grounded in a solid foundation, where we establish a concrete definition of interpretability within the insurance context, complemented by a rigorous mathematical framework. Comparisons in terms of prediction accuracy are made with traditional actuarial and state-of-the-art machine learning methods using both synthetic and real insurance datasets. The results show that the proposed model outperforms other methods in most cases while offering complete transparency in its internal logic, underscoring the strong interpretability and predictive capability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 10 Sep 2025.

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References · 21

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
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  3. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
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  10. 2020GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactionscited by 82
  11. 2020In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Predictioncited by 42
  12. 2015Monotonic Calibrated Interpolated Look-Up Tablescited by 35
9 more
  1. 2019Enhancing Explainability of Neural Networks through Architecture Constraintscited by 29
  2. 2022Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsitycited by 25
  3. 2021LocalGLMnet: interpretable deep learning for tabular datacited by 15
  4. 2022Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoringcited by 12
  5. 2021Stochastic loss reserving with mixture density neural networkscited by 5
  6. 2022Model Transparency and Interpretability : Survey and Application to the Insurance Industrycited by 5
  7. 2020Towards Explainability of Machine Learning Models in Insurance Pricingcited by 3
  8. 2023Smoothness and monotonicity constraints for neural networks using ICEnetcited by 2
  9. 2024Distributional Refinement Network: Distributional Forecasting via Deep Learningcited by 1

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