Fairness by Design: Balancing Precision and Equity in Insurance Pricing Models

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The insurance industry is at a crossroads where innovation meets responsibility. As predictive models grow more granular, insurers and regulators must rethink how accuracy, equity, and mutuality can coexist.

The insurance industry faces a fundamental paradox in the age of artificial intelligence and big data. While sophisticated algorithms promise unprecedented pricing accuracy, they simultaneously raise urgent questions about fairness and discrimination. Insurers can now segment risk with remarkable precision, yet this same precision risks creating insurance deserts where coverage becomes unaffordable or unavailable for entire communities. The challenge lies in balancing between accuracy and equity, as well as in defining what responsible use of predictive power actually means.

Regulators worldwide are grappling with how to preserve insurance's foundational principle of risk pooling while permitting technological innovation. What was once a straightforward actuarial exercise has become a complex ethical and legal minefield where every data point carries potential for both insight and injustice.

Models can now incorporate thousands of variables to predict individual risk, but this granularity threatens to unravel the mutuality that makes insurance viable. The actuarial profession must navigate between statistical optimization and social responsibility, ensuring that technical sophistication does not inadvertently encode historical biases or create systemic inequities.

The Regulatory Grey Area

At the heart of the fairness challenge lies a regulatory blind spot created by advanced analytics. While direct discrimination based on protected characteristics like race, gender, or ethnicity is clearly prohibited, indirect discrimination presents a murkier problem. When insurers use seemingly neutral variables such as ZIP codes or credit scores, these can serve as proxies that correlate with protected characteristics, producing discriminatory outcomes without explicitly using prohibited factors.

This proxy discrimination has attracted intense regulatory scrutiny. The challenge is compounded by complex machine learning algorithms that can perpetuate historical biases in ways that are difficult to detect or assess.

Bridging Two Worlds

On one side, machine learning experts have developed various fairness criteria, but these have focused primarily on binary classification problems such as hiring decisions or loan approvals. On the other side, insurance pricing operates as a regression problem, predicting continuous outcomes like premium amounts rather than yes-or-no decisions. The disconnect meant that existing fairness frameworks couldn't be directly applied to insurance pricing.

In an award-winning paper on fairness in insurance pricing¹ , the researchers introduced fairness criteria applicable to insurance pricing as a regression model, aligned them with different levels of antidiscrimination regulations, and implemented them across various pricing models including both traditional generalized linear models and advanced approaches like Extreme Gradient Boosting.

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The research reveals that fairness in insurance pricing can be approached from several angles. Individual fairness focuses on treating similar individuals similarly, aligning with the actuarial principle that premiums should reflect individual risk profiles. Group fairness examines whether different demographic groups receive comparable treatment on average.

These concepts often conflict. Actuarial fairness emphasizes matching premiums precisely to individual risks, which may result in different average prices across demographic groups if those groups genuinely differ in risk profiles. Social fairness seeks to prevent disparate impacts on protected groups even when risk differences exist.

The authors demonstrate that insurers cannot simply remove protected characteristics and assume discrimination has been eliminated. However, they show that models can actively mitigate bias while maintaining predictive accuracy.

The Fairness-Accuracy Trade-Off

A central contribution of the research is its empirical comparison of different pricing models through the lens of the fairness-accuracy trade-off. The analysis demonstrates which approaches achieve the best balance between pricing fairness and predictive accuracy, providing insurers with evidence-based guidance for model selection.

Critically, the research shows that pursuing fairness need not come at excessive cost to predictive power. Through careful model design and the application of appropriate fairness constraints, insurers can develop pricing mechanisms that respect both actuarial principles and social equity considerations. The analysis also examines how different fairness approaches affect adverse selection and solidarity within insurance pools.

Their methodology involves treating fairness as an explicit objective alongside prediction accuracy rather than an afterthought. The authors show that by incorporating demographic parity or equalized odds constraints during model training, insurers can achieve what they term 'fairness-aware pricing' where models automatically balance risk prediction against equitable treatment. The key insight is that modest sacrifices in pure predictive power, typically negligible in practical terms, can yield substantial improvements in fairness metrics.

The researchers provide concrete algorithms that modify standard machine learning techniques like XGBoost to include fairness penalties. These penalties discourage the model from making predictions that correlate strongly with protected characteristics, even when those characteristics aren't directly included as inputs. Their empirical results show that fairness-constrained models achieve nearly identical loss ratios to unconstrained versions while dramatically reducing indirect discrimination, proving that the actuarial-fairness tradeoff is far less severe than previously assumed.

Through constrained optimization that penalizes demographic disparities during model training, they demonstrate that fairness-aware algorithms can reduce indirect discrimination by 40-60% while sacrificing less than 2% in predictive accuracy compared to unconstrained models

Practical Framework for Implementation

Perhaps the most valuable aspect of Huang and Xin's work is its practical applicability. By linking fairness criteria to specific antidiscrimination regulations and then embedding them into a range of pricing models, the study creates an actionable framework that insurers and regulators can actually use.

For actuaries, this means fairness considerations can be built directly into technical models rather than treated as an afterthought. The framework provides transparency when demonstrating compliance with regulations and alignment with company values. Instead of black-box algorithms that produce unexplainable results, actuaries can now point to specific fairness criteria embedded in their models.

For regulators, the research offers tools to translate abstract fairness principles into concrete, monitorable standards. Rather than simply prohibiting certain variables, regulators can specify which fairness criteria models must satisfy, creating clear expectations that can be audited. This approach balances fairness concerns with the need for predictive accuracy and market stability.

For insurers, the framework demonstrates that responsible pricing is both technically feasible and strategically wise. As regulatory scrutiny intensifies and public awareness of algorithmic bias grows, companies that proactively address fairness will be better positioned to maintain public trust and avoid regulatory sanction.

The Path Forward

Despite its significant contributions, Huang and Xin acknowledge that substantial questions remain. The appropriate fairness criteria may vary depending on the type of insurance product and the regulatory environment. Auto insurance, for example, may warrant different fairness considerations than life insurance or homeowners’ coverage.

Another open question involves where to apply fairness constraints in the pricing process. Should constraints be implemented during cost modeling, when translating costs into market prices, or both? Each approach affects insurers, regulators, and policyholders differently, with implications for market dynamics and consumer protection.

There is also a need for continued work on measuring fairness when insurers cannot collect data on protected characteristics, as is the case in many jurisdictions. Statistical techniques can provide partial solutions, but questions remain about accuracy, transparency, and potential for unintended bias.

A Model for Responsible Innovation

As AI and advanced analytics make risk segmentation even sharper, actuaries will increasingly sit at the fault line between technical accuracy and social acceptability. The future challenge will be finding better predictors that we can use publicly and fairly.

¹ Huang, F., & Xin, X. (2024). Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models. North American Actuarial Journal, 28(1). https://www.tandfonline.com/doi/full/10.1080/10920277.2023.2173020

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