Insurance pricing model with transparent policy document.

Smarter Insurance: How Predictive Modeling is Changing Premiums

Beau Callahan in Tech & Innovation September 2025 • 3 min read.

"Unlock fair and transparent insurance pricing with advanced Bayesian credibility models."

Imagine paying for insurance that truly reflects your risk profile, not just a broad category. That's the promise of experience rating, where insurers adjust premiums based on your individual history and characteristics. However, the most sophisticated models are often too complex, relying on intensive computation that makes them impractical for large groups of policyholders.

Traditional methods, like the Bühlmann credibility formula, offer simplicity but at the cost of accuracy and flexibility. These formulas often rely on rigid assumptions and can't adapt to the complex data available today. This creates a need for models that can balance accuracy, interpretability, and computational efficiency.

Now, a new approach is emerging, one that combines the power of Bayesian credibility models with the efficiency of surrogate modeling. This method promises to deliver fairer premiums, reduce computational burdens, and provide greater transparency in the insurance pricing process.

What is Experience Rating and Why Does It Matter?

Insurance pricing model with transparent policy document.

Experience rating allows insurance companies to tailor premiums to individual policyholders based on their past claims and specific attributes. It's a move away from one-size-fits-all pricing, aiming for a system where low-risk individuals pay less, and high-risk individuals pay more appropriately.

The challenge lies in creating models that are both accurate and practical. Data-driven models often require complex calculations that can be computationally expensive, especially when dealing with large insurance portfolios. This complexity can also lead to 'black-box' scenarios where it's difficult to understand how premiums are being calculated.

Fairness: Rewards safe behavior and penalizes risky behavior, leading to more equitable premiums.
Accuracy: Utilizes detailed data for precise risk assessment.
Transparency: Provides insights into how individual factors affect premiums.

To overcome these hurdles, researchers are exploring innovative methods like surrogate modeling to approximate complex Bayesian calculations. This can lead to more transparent and interpretable insurance pricing models.

The Future of Insurance Pricing

The integration of Bayesian credibility models with surrogate modeling represents a significant step forward in insurance pricing. This approach not only enhances accuracy and fairness but also ensures transparency and interpretability, which are crucial for both customers and regulators. As technology advances, we can expect even more sophisticated methods to refine insurance premiums, creating a system that is both data-driven and human-centered.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.insmatheco.2024.05.004,

Title: Effective Experience Rating For Large Insurance Portfolios Via Surrogate Modeling

Subject: stat.me q-fin.rm stat.ap stat.co

Authors: Sebastian Calcetero-Vanegas, Andrei L. Badescu, X. Sheldon Lin

Published: 11-11-2022

Everything You Need To Know

What is experience rating in the context of insurance, and why is it considered important?

Experience rating is a method used by insurance companies to customize premiums for individual policyholders, factoring in their past claims and specific attributes. It's important because it moves away from a one-size-fits-all approach, aiming for a fairer system where low-risk individuals pay less, and higher-risk individuals pay more appropriately. However, creating models that are both accurate and practical remains a challenge, which is being addressed through innovative methods like surrogate modeling.

What are Bayesian credibility models and how are they transforming insurance premiums?

Bayesian credibility models are advanced statistical tools used to unlock fairer and more transparent insurance pricing. By combining prior knowledge with observed data, they refine risk assessments and tailor premiums to individual risk profiles. This approach, especially when integrated with surrogate modeling, enhances accuracy, fairness, and transparency in the insurance pricing process, benefiting both customers and regulators.

What are the limitations of traditional methods, such as the Bühlmann credibility formula, in determining insurance premiums?

Traditional methods like the Bühlmann credibility formula offer simplicity but often at the cost of accuracy and flexibility. These formulas tend to rely on rigid assumptions and struggle to adapt to the complex data available today. This creates a need for more advanced models that can balance accuracy, interpretability, and computational efficiency, such as combining Bayesian credibility models with surrogate modeling.

How does surrogate modeling enhance the application of Bayesian credibility models in insurance pricing?

Surrogate modeling enhances the application of Bayesian credibility models by approximating complex Bayesian calculations, thereby reducing computational burdens. This leads to more transparent and interpretable insurance pricing models, making it easier to understand how premiums are calculated. By overcoming computational hurdles, surrogate modeling enables fairer premiums and broader application of Bayesian methods in insurance.

What are the key advantages of integrating Bayesian credibility models with surrogate modeling for insurance pricing, and what does it mean for the future of insurance?

The integration of Bayesian credibility models with surrogate modeling offers several key advantages: enhanced accuracy, fairness, transparency, and interpretability in insurance pricing. This approach ensures that premiums are tailored to individual risk profiles while providing insights into how factors affect premiums. The future of insurance involves even more sophisticated methods to refine premiums, creating a system that is data-driven and human-centered. This includes continuous refinement of these models and incorporation of new data sources to better assess and price risk, moving towards a more equitable and understandable insurance ecosystem.