Balancing Risk and Equity: The Future of Insurance Pricing

Author: Denis Avetisyan

A new optimization framework seeks to reconcile accurate risk assessment with critical fairness considerations in insurance coverage.

This review details a multi-objective approach using NSGA-II to navigate trade-offs between predictive power and diverse fairness metrics like demographic parity and counterfactual fairness.

While machine learning enhances predictive power in insurance pricing, it simultaneously intensifies the conflict between competing fairness criteria and regulatory demands. This challenge is addressed in ‘Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach’ through a novel framework employing multi-objective optimization with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to jointly balance accuracy with group, individual, and counterfactual fairness. Our results demonstrate that this approach consistently achieves improved trade-offs compared to single-model strategies, revealing nuanced performance differences between estimation methods like GLM and XGBoost. Can this methodology pave the way for more equitable and robust risk modeling in other sensitive domains?

The Illusion of Accuracy: Reconciling Prediction with Equity

Historically, insurance premiums have been calculated using actuarial science, striving for precise risk assessment and accurate pricing. However, these traditional models, while mathematically sound, frequently overlook the potential for unfair or discriminatory outcomes. Data used in these calculations can inadvertently reflect existing societal biases – for example, relying on zip codes that correlate with socioeconomic status – leading to systematically higher premiums for certain demographic groups, even when individual risk profiles are comparable. This isn’t necessarily intentional malice, but rather a consequence of algorithms reinforcing pre-existing inequalities present in the data itself. Consequently, a focus solely on predictive accuracy, without explicit consideration of fairness metrics and potential disparate impact, can result in pricing structures that are statistically robust, yet ethically problematic, fueling growing calls for greater transparency and accountability in algorithmic insurance.

The development of increasingly sophisticated insurance pricing models presents a critical challenge: reconciling the desire for accurate risk prediction with the imperative of equitable treatment. While algorithms can identify correlations between various factors and potential claims, these same algorithms risk perpetuating or even amplifying existing societal biases, leading to unfairly differentiated premiums. This is particularly sensitive as regulatory bodies worldwide are implementing stricter guidelines regarding algorithmic accountability and fairness, and public awareness of potential discriminatory practices embedded within automated systems continues to grow. Consequently, insurers must prioritize transparency and actively mitigate bias throughout the entire modeling process, not merely to avoid legal repercussions, but to foster trust and maintain ethical standards in a rapidly evolving landscape where data-driven decisions are under intense scrutiny.

Adverse selection presents a significant obstacle to accurate insurance pricing by fundamentally altering the risk pool. This phenomenon occurs when individuals with higher risk profiles are more likely to purchase insurance than those with lower risk, while those with lower risk may opt out, perceiving the premiums as too high for their personal risk level. Consequently, insurers find themselves insuring a disproportionately high-risk population, leading to an underestimation of overall claims costs and the potential for financial instability. Attempts to correct for this imbalance through increased premiums can then exacerbate the problem, driving away lower-risk individuals and further skewing the pool. The resulting cycle necessitates sophisticated modeling techniques, not only to predict individual risk but also to account for the evolving composition of the insured population and mitigate the effects of this inherent asymmetry.

Defining Fairness: Navigating Conflicting Principles

Multiple definitions of fairness are employed in assessing algorithmic outcomes, each with inherent trade-offs. Demographic Parity requires that the proportion of individuals receiving a positive outcome is equal across all groups, irrespective of qualifications. Equality of Opportunity focuses on ensuring equal true positive rates across groups – that qualified individuals have an equal chance of receiving a positive outcome, regardless of group membership. Finally, Individual Fairness posits that similar individuals should be treated similarly, though defining “similarity” itself presents a challenge. No single criterion is universally optimal; the appropriate choice depends on the specific application and the values prioritized, and often involves balancing competing notions of fairness.

The Disparity Impact Ratio (DIR) is a quantifiable metric used to assess fairness in algorithmic decision-making, specifically within insurance contexts. It is calculated as the ratio of the positive outcome rate for an unfavorable group to the positive outcome rate for a favored group. A DIR value of 0.8 or 0.80 is commonly used as a benchmark; values below this threshold indicate a potentially adverse impact, suggesting a disproportionately lower rate of positive outcomes for the unfavorable group. While the definition of ‘favorable’ and ‘unfavorable’ groups, and the definition of a ‘positive outcome’, are context-dependent, the DIR provides an objective, albeit simplified, assessment of potential bias by comparing outcome rates across defined groups. It’s important to note that the DIR doesn’t address the reason for disparity, only its existence.

Fairness Through Unawareness and Fairness Through Orthogonality represent distinct algorithmic approaches to bias mitigation in insurance and other applications. Fairness Through Unawareness involves removing explicitly sensitive attributes – such as race or gender – from the model’s input features during training and prediction. However, this approach may not fully eliminate bias if other features are correlated with the sensitive attribute. Fairness Through Orthogonality, conversely, aims to remove the correlation between the model’s predictions and the sensitive attribute, even if the attribute itself is not directly used as an input. This is achieved through regularization techniques that penalize correlations during model training, effectively decoupling predictions from protected characteristics without necessarily requiring the complete removal of potentially informative, but correlated, features.

Multi-Objective Optimization: Balancing Accuracy and Equity

Multi-objective optimization addresses the inherent conflict between maximizing predictive accuracy and ensuring fairness in pricing models. Traditional optimization often focuses solely on minimizing error, typically measured by Root Mean Squared Error (RMSE). However, this approach can exacerbate existing biases and lead to unfair outcomes. This framework explicitly defines both RMSE and fairness metrics as objectives to be simultaneously optimized. The resulting process does not aim for a single “best” solution, but rather generates a set of Pareto optimal solutions representing the trade-off surface between accuracy and fairness. The demonstrated framework allows for explicit control over this trade-off, enabling stakeholders to select a solution that best aligns with their specific priorities and risk tolerance.

The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is particularly effective in multi-objective optimization problems due to its capacity to generate a Pareto front. This front represents the set of non-dominated solutions, where no solution can improve one objective without worsening at least one other. NSGA-II employs a genetic algorithm with specific mechanisms – non-dominated sorting, crowding distance calculation – to maintain solution diversity and efficiently explore the trade-off space between competing objectives, such as prediction accuracy and fairness. The resulting Pareto front allows decision-makers to visualize the available options and select a solution that best aligns with their specific priorities and constraints, rather than being limited to a single, potentially suboptimal, optimized solution.

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a decision-making tool utilized to select a single optimal solution from the Pareto front generated by multi-objective optimization. TOPSIS functions by evaluating the distance of each solution to both the ideal solution (best values for all objectives) and the nadir solution (worst values for all objectives). Solutions are then ranked based on their proximity to the ideal solution and distance from the nadir, with the closest solution considered the most preferable. The weighting of individual objectives within the TOPSIS calculation allows decision-makers to incorporate specific organizational priorities or regulatory requirements into the solution selection process, ensuring the chosen model aligns with defined criteria beyond purely statistical performance.

The implementation of advanced machine learning models, specifically XGBoost, within a multi-objective optimization framework demonstrably improves predictive performance compared to Generalized Linear Models (GLM). Experimental results indicate that XGBoost consistently achieves lower Root Mean Squared Error (RMSE) values while simultaneously maintaining acceptable levels of fairness, as measured by relevant fairness metrics. This balance is achieved through the multi-objective approach, which allows for the simultaneous optimization of both prediction accuracy and fairness criteria, rather than prioritizing one over the other. The ensemble nature of XGBoost contributes to this improved performance, allowing it to capture complex relationships within the data and generalize effectively across different subgroups.

Validating Equitable Outcomes: The Power of Counterfactuals

Counterfactual fairness is a criterion used to evaluate machine learning models for discriminatory behavior. It posits that a model is fair if, for any individual, their predicted outcome remains consistent even if their sensitive attributes – such as race or gender – were hypothetically altered. This is determined by examining whether a change in these attributes results in a different prediction; if the prediction does change, the model fails the counterfactual fairness test for that individual. This approach provides a strong guarantee against discrimination as it assesses fairness at the individual level, ensuring that predictions are based on relevant factors and not on protected characteristics, thereby mitigating the risk of disparate treatment.

The Synthetic Control Method is a statistical technique used to estimate the counterfactual outcome for an individual or group by constructing a weighted combination of control units-those unaffected by a specific intervention or change in sensitive attributes. This method differs from simple causal inference by explicitly addressing the problem of selection bias. It identifies a “synthetic” control group that closely matches the treated unit on observed characteristics before the intervention, minimizing the discrepancy between the actual outcome and the estimated counterfactual. This allows for a more reliable assessment of the impact of sensitive attributes on model predictions, as the counterfactual outcome is derived from comparable data points, thereby providing a robust basis for evaluating fairness metrics like Individual Treatment Effect (ITE) and Disparity Ratio.

Insurers can leverage multi-objective optimization during model training to simultaneously maximize predictive accuracy and promote fairness, verified through counterfactual validation. This approach results in ensemble models exhibiting improved fairness metrics compared to models solely optimized for accuracy. Specifically, a lower Median Individual Treatment Effect (ITE), approaching 0, indicates reduced sensitivity to changes in sensitive attributes, demonstrating better counterfactual fairness. Concurrently, a Disparity Ratio closer to 1 signifies reduced disparity in outcomes between different groups, indicating improved group fairness. These results demonstrate that optimizing for both accuracy and fairness, with validation using counterfactuals, yields models that are not only predictive but also demonstrably equitable.

Beyond Prediction: Building a Future of Equitable Insurance

The successful implementation of sophisticated machine learning models in insurance pricing isn’t simply a matter of technical achievement; it demands continuous vigilance and proactive adjustment. Regulatory bodies worldwide are increasingly focused on algorithmic fairness and transparency, meaning insurers must establish robust monitoring systems to detect and mitigate potential biases embedded within these models. Beyond compliance, this ongoing adaptation is crucial because societal perceptions of risk and fairness evolve, and models trained on historical data may become outdated or perpetuate existing inequalities. Therefore, a dynamic approach-one that combines technical refinement with careful consideration of the legal and ethical landscape-is essential to ensure these advanced techniques deliver both accurate pricing and equitable outcomes, fostering long-term trust and sustainability within the insurance industry.

Recent advancements explore the potential of ‘Neural Network’ meta-learners as a sophisticated method for refining insurance models. These systems don’t simply select the best base model, but instead learn how to optimally combine the predictions of several diverse models, each potentially excelling in different areas or with different demographic groups. This approach allows for a nuanced evaluation of risk, moving beyond the limitations of any single predictive algorithm. The result is a system capable of achieving higher overall accuracy while simultaneously mitigating biases that might be present in individual models, thereby promoting fairer outcomes and more reliable risk assessment. By dynamically weighting the contributions of various base models, the meta-learner adapts to complex data patterns and ensures a more robust and equitable predictive process.

Insurance, at its core, functions as a critical component of societal risk management, and increasingly, its long-term viability depends on a demonstrated commitment to equitable practices. Prioritizing fairness alongside traditional profitability metrics isn’t merely an ethical consideration, but a strategic imperative; biased algorithms or discriminatory pricing models erode public trust and can lead to systemic disadvantages for vulnerable populations. Insurers who proactively integrate fairness-aware machine learning techniques and transparent underwriting processes cultivate stronger relationships with policyholders, attract responsible investment, and ultimately build a more sustainable business model. This shift towards equitable insurance strengthens the industry’s role in distributing risk effectively, fostering resilience against unforeseen events, and contributing to a more just and secure future for all.

The pursuit of fairness in algorithmic systems, as demonstrated by this work on multi-objective optimization for insurance pricing, often reveals a frustrating truth: perfect alignment with all desired criteria is an illusion. It echoes Georg Wilhelm Friedrich Hegel’s observation that “The truth is the whole.” This research doesn’t propose a solution, but rather a framework for navigating the inherent trade-offs between predictive power and various fairness metrics – group, individual, and counterfactual. The NSGA-II approach acknowledges that optimizing for one objective invariably impacts others, demanding a holistic understanding rather than a singular, idealized outcome. If the resulting models aren’t perfectly ‘fair’ by every definition, it simply suggests a deeper investigation of the conflicting objectives is needed, and that the process of refinement, of iterative failure, is where genuine progress lies.

What’s Next?

The pursuit of fairness in algorithmic pricing, as demonstrated by this work, reveals a familiar truth: optimization alone does not yield ethical outcomes. Balancing predictive power against multiple, often conflicting, definitions of fairness is not a solved problem, merely a refined one. The NSGA-II framework offers improved trade-offs, but the selection of appropriate fairness constraints – and the weighting given to each – remains a fundamentally subjective exercise. The model doesn’t decide what is fair; it merely quantifies the consequences of a pre-defined notion.

Future work must move beyond the assessment of statistical parity. While group fairness metrics provide a useful starting point, they offer limited protection against insidious forms of discrimination embedded within complex risk models. Investigating the robustness of counterfactual fairness definitions against data manipulation, and exploring methods for quantifying the cost of false positives and false negatives across demographic groups, are critical next steps. A model’s success shouldn’t be measured by its ability to appear unbiased, but by its demonstrable resistance to perpetuating existing inequalities.

Ultimately, an error in the pursuit of fairness isn’t a failure of the algorithm, but a message from the data. It signals a limitation in the chosen representation, a flaw in the underlying assumptions, or perhaps, a necessary confrontation with the inherent tension between prediction and equity. The field progresses not by eliminating errors, but by systematically diagnosing their causes, and acknowledging the impossibility of a truly ‘neutral’ price.

Original article: https://arxiv.org/pdf/2512.24747.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/