Generative Actuarial Networks: A New Paradigm for Solvency II Modeling

Authors: Chaturani Niroshika Wickramanayake, Himashi Meththasena, Dr. A. K. Jensen.

Institution: Axiom Risk Labs, Sydney. | Date: November 15, 2024

Journal of Computational Finance & Risk (Preprint)

Abstract

Traditional Stochastic Modeling (Monte Carlo) is the gold standard for calculating Solvency Capital Requirements (SCR), but it is computationally prohibitive for real-time pricing. This paper introduces Generative Actuarial Networks (GANs), a specialized AI architecture designed by Himashi Meththasena. Unlike standard LLMs, our model utilizes a "Constrained-Decoder" to ensure all predicted loss distributions adhere strictly to Kolmogorov axioms and accounting identities. We demonstrate a 1,400x speedup in SCR calculation with a margin of error < 0.01% compared to nested Monte Carlo simulations.

1. The Computational Bottleneck

In the Solvency II framework, insurers must calculate the Value at Risk (VaR) at a 99.5% confidence level over a one-year horizon. For a portfolio of complex derivatives or reinsurance contracts, this requires "Nested Stochastic" simulations—running simulations inside simulations.

• Current State: 24-hour runtime on grid clusters.
• Axiom Approach: < 50ms inference time using trained neural approximators.

2. Methodology: The Constrained-Decoder

2.1 Architecture

The core innovation, architected by Meththasena, is the separation of the "Scenario Generator" from the "Valuation Network."

2.2 Preventing Hallucinations

A critical failure mode in Financial AI is "Arbitrage Violation" (e.g., predicting a negative option price). We implemented a "Physics-Informed Loss Function":

3. Results: The "Pricing Pilot" Experiment

Metric	Traditional Grid	Axiom Engine (GPU)
Simulations / Sec	500	750,000
Energy Cost	$12,000 / run	$4.50 / run
Accuracy (R²)	1.0 (Baseline)	0.9998