Idea
Interpretable deep learning model for insurance pricing delivering transparent risk assessment to insurers and actuaries.
Research Paper
Core Innovation
This paper presents the Actuarial Neural Additive Model which uses separate neural networks for each covariate and interaction term to ensure interpretability. It balances the predictive power of deep learning with the transparency required in insurance pricing. The model is supported by a rigorous mathematical framework defining interpretability specifically for insurance applications.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: General insurance pricing market with growing AI adoption and regulatory demand for transparency.
Potential Customers & Pain Points
- Insurance Companies Needing Transparent Pricing Models
- Actuaries Seeking Explainable Risk Predictions
- Regulatory Bodies Requiring Model Transparency
Business Model
SaaS platform offering API access to interpretable insurance pricing models with subscription and usage-based pricing.
Competitive Landscape
- Generalized Linear Models (GLM)
- Gradient Boosting Machines (GBM)
- Explainable Boosting Machines (EBM)
Implementation Challenges
- Regulatory Acceptance of New Models
- Integration with Legacy Insurance Systems
- Data Privacy and Security Concerns
Validation Strategy
- Benchmark model accuracy against traditional actuarial methods on real datasets
- Conduct pilot deployments with insurance partners
- Gather feedback on interpretability and usability from actuaries
Research Paper Overview
An Interpretable Deep Learning Model for General Insurance Pricing
Summary
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 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 and practical requirements in insurance applications. The model is grounded in a solid foundation with a concrete definition of interpretability within insurance, complemented by a rigorous mathematical framework. It outperforms traditional actuarial and state-of-the-art machine learning methods in prediction accuracy on synthetic and real insurance datasets while providing complete transparency in its internal logic.